sentence1
stringlengths 40
15.9k
| sentence2
stringlengths 88
20k
| label
float64 0.5
0.99
|
|---|---|---|
1. A computer-implemented method, comprising: generating a set of seed rules comprising: one or more rules resulting from one or more previously performed machine learning operations that were previously performed in regard to a set of item description entries, and one or more randomly or pseudo-randomly generated rules; performing one or more machine learning operations on the set of seed rules to generate a set of duplicate detection rules for determining whether a given pair of item description entries is a duplicate pair, wherein a given duplicate pair represents different item description entries that describe a common item, wherein said performing the one or more machine learning operations on the set of seed rules is performed in regard to another set of item description entries, wherein the other set of item description entries includes one or more differences from the set of item description entries in regard to which the one or more previously performed machine learning operations were previously performed; and applying the set of duplicate detection rules to multiple item description entries to identify at least one duplicate pair of item description entries. | 1. A computer-implemented method, comprising: generating a set of seed rules comprising: one or more rules resulting from one or more previously performed machine learning operations that were previously performed in regard to a set of item description entries, and one or more randomly or pseudo-randomly generated rules; performing one or more machine learning operations on the set of seed rules to generate a set of duplicate detection rules for determining whether a given pair of item description entries is a duplicate pair, wherein a given duplicate pair represents different item description entries that describe a common item, wherein said performing the one or more machine learning operations on the set of seed rules is performed in regard to another set of item description entries, wherein the other set of item description entries includes one or more differences from the set of item description entries in regard to which the one or more previously performed machine learning operations were previously performed; and applying the set of duplicate detection rules to multiple item description entries to identify at least one duplicate pair of item description entries. 2. The computer-implemented method of claim 1 , further comprising merging the at least one duplicate pair of item description entries into a single combined item description entry for the common item. | 0.714988 |
17. At least one computer-readable medium storing a program comprising computer-executable instructions that, when executed, cause a computer to perform a method comprising: responsive to detecting, at the communication device, an indication from a user of the communication device to record, recording at least a portion of a communication between the communication device and a second remote communication device, prompting the user to speak a contact name, and recording at least a portion of speech spoken by the user in response to the prompting as a recording of the contact name; automatically converting the recording of the at least a portion of the communication and the recording of the contact name into text; automatically extracting the contact name and at least one other type of contact information from the text; and storing the extracted contact name and the at least one other type of contact information in an entry of the contact database without manual entry of the extracted contact name and the at least one other type of contact information by the user of the communication device. | 17. At least one computer-readable medium storing a program comprising computer-executable instructions that, when executed, cause a computer to perform a method comprising: responsive to detecting, at the communication device, an indication from a user of the communication device to record, recording at least a portion of a communication between the communication device and a second remote communication device, prompting the user to speak a contact name, and recording at least a portion of speech spoken by the user in response to the prompting as a recording of the contact name; automatically converting the recording of the at least a portion of the communication and the recording of the contact name into text; automatically extracting the contact name and at least one other type of contact information from the text; and storing the extracted contact name and the at least one other type of contact information in an entry of the contact database without manual entry of the extracted contact name and the at least one other type of contact information by the user of the communication device. 20. The at least one computer readable medium according to claim 17 , wherein the extracting the contact name and the at least one other type of contact information from the text comprises: detecting a lack of a tag within the text; inferring information for the tag from other information collectable at the communication device about a second user of the second remote communication device; and assigning the inferred information to a field of the entry of the contact database, wherein the field is associated with the tag. | 0.516129 |
12. A control method of a display apparatus comprising: recognizing a gaze of a user; determining whether the recognized gaze is within a predetermined recognition region; entering an interactive mode upon determining that the recognized gaze is within the predetermined recognition region; displaying a plurality of recognition modes for interaction with the user, the plurality of recognition modes including a gaze recognition mode, a motion recognition mode and a voice recognition mode; determining a recognition mode corresponding to a position of the recognized gaze from among the displayed recognition modes; if the gaze recognition mode is selected, controlling the motion recognition mode and the voice recognition mode to be turned off, and collecting an image of the user; if the motion recognition mode is selected, controlling the gaze recognition mode and the voice recognition mode to be turned off, and collecting an image of the user; if the voice recognition mode is selected, controlling the gaze recognition mode and the motion recognition mode to be turned off, and collecting an voice of the user; executing the determined recognition mode to recognize a command from the user in the determined recognition mode; and executing a function corresponding to the recognized command. | 12. A control method of a display apparatus comprising: recognizing a gaze of a user; determining whether the recognized gaze is within a predetermined recognition region; entering an interactive mode upon determining that the recognized gaze is within the predetermined recognition region; displaying a plurality of recognition modes for interaction with the user, the plurality of recognition modes including a gaze recognition mode, a motion recognition mode and a voice recognition mode; determining a recognition mode corresponding to a position of the recognized gaze from among the displayed recognition modes; if the gaze recognition mode is selected, controlling the motion recognition mode and the voice recognition mode to be turned off, and collecting an image of the user; if the motion recognition mode is selected, controlling the gaze recognition mode and the voice recognition mode to be turned off, and collecting an image of the user; if the voice recognition mode is selected, controlling the gaze recognition mode and the motion recognition mode to be turned off, and collecting an voice of the user; executing the determined recognition mode to recognize a command from the user in the determined recognition mode; and executing a function corresponding to the recognized command. 17. The control method according to claim 12 , further comprising completing the interactive mode when the gaze of the user is outside the region of the display unit for a predetermined period of time or more. | 0.622698 |
3. The method of claim 2 , wherein: determining an English language context value comprises: applying the composition inputs in an English context; comparing the composition inputs to prefixes of English words; and setting the English context value based on the comparing; and determining a Chinese language context value comprises: applying the composition inputs in a Chinese context; identifying Chinese characters that correspond to the composition inputs; evaluating the Chinese characters against a Chinese language model; and setting the Chinese context value based on the evaluation. | 3. The method of claim 2 , wherein: determining an English language context value comprises: applying the composition inputs in an English context; comparing the composition inputs to prefixes of English words; and setting the English context value based on the comparing; and determining a Chinese language context value comprises: applying the composition inputs in a Chinese context; identifying Chinese characters that correspond to the composition inputs; evaluating the Chinese characters against a Chinese language model; and setting the Chinese context value based on the evaluation. 4. The method of claim 3 , wherein the language models include a Chinese language model that includes a grammar rule set for the Chinese language. | 0.912555 |
11. A system to translate displayed user-interface text of a computer application, comprising: an interception module configured to intercept a command to display user-interface text in a first language, the command comprising the user-interface text to display in the first language; an extraction module configured to extract user-interface text to translate from the command; an interface to a translation mechanism, wherein the interface is configured to transmit extracted user-interface text to the translation mechanism, and to receive translated user-interface text from the translation mechanism; and an output module configured to display the translated user-interface text in the second language. | 11. A system to translate displayed user-interface text of a computer application, comprising: an interception module configured to intercept a command to display user-interface text in a first language, the command comprising the user-interface text to display in the first language; an extraction module configured to extract user-interface text to translate from the command; an interface to a translation mechanism, wherein the interface is configured to transmit extracted user-interface text to the translation mechanism, and to receive translated user-interface text from the translation mechanism; and an output module configured to display the translated user-interface text in the second language. 13. The system of claim 11 , wherein the translation mechanism is configured to translate a multi-word textual phrase. | 0.820175 |
1. A method of evaluating a sequence of characters received by one or more computing devices to determine the presence of a natural language word in the received sequence, the method comprising: finding a subsequence of alphabetical characters in the received sequence of characters, wherein the received sequence comprises both alphabetical and non-alphabetic characters, and wherein the subsequence corresponds to alphabetical characters occurring between the non-alphabetic characters in the received sequence; calculating a probability that the subsequence is a natural language word using a statistical model of a natural language; and determining if the subsequence is a natural language word based on the probability; wherein the finding, calculating, and determining steps are performed on the one or more computing devices. | 1. A method of evaluating a sequence of characters received by one or more computing devices to determine the presence of a natural language word in the received sequence, the method comprising: finding a subsequence of alphabetical characters in the received sequence of characters, wherein the received sequence comprises both alphabetical and non-alphabetic characters, and wherein the subsequence corresponds to alphabetical characters occurring between the non-alphabetic characters in the received sequence; calculating a probability that the subsequence is a natural language word using a statistical model of a natural language; and determining if the subsequence is a natural language word based on the probability; wherein the finding, calculating, and determining steps are performed on the one or more computing devices. 7. The method of claim 1 , wherein the sequence of characters is a password. | 0.569215 |
7. A method as in claim 1 , the step (c) of generating an ontology file comprising the steps of: i) parsing said content; ii) extracting names from parsed said content; and iii) converting said content into ontology files responsive to extracted said names and guided by a natural language processor filtering, cleaning and segmenting said names and identifying synonyms, acronyms and antonyms for said names. | 7. A method as in claim 1 , the step (c) of generating an ontology file comprising the steps of: i) parsing said content; ii) extracting names from parsed said content; and iii) converting said content into ontology files responsive to extracted said names and guided by a natural language processor filtering, cleaning and segmenting said names and identifying synonyms, acronyms and antonyms for said names. 8. A method as in claim 7 , wherein the step (ii) of extracting extracts names from parsed said content according to class, property and instance for text mining. | 0.892377 |
1. A method comprising: at a computing device having one or more processors and memory storing one or more programs for execution by the one or more processors: displaying a messaging application for a first user; responsive to a determination that a message body of a first electronic message satisfies a first set of content-based clustering rules associated with a first message cluster, assigning the first electronic message to the first message cluster, which has a first plurality of electronic messages, wherein each electronic message in the first message cluster is either addressed to the first user or originates from the first user; displaying a first view of a first cluster graphic for the first message cluster, wherein the first view collectively represents the electronic messages in the first message cluster; in response to a first predefined user action, displaying a second view of the first cluster graphic, wherein the second view replaces the first view and displays the first plurality of electronic messages individually within the first message cluster; and in response to a second predefined user action, expanding one or more electronic messages of the first plurality of electronic messages inline within the first message cluster. | 1. A method comprising: at a computing device having one or more processors and memory storing one or more programs for execution by the one or more processors: displaying a messaging application for a first user; responsive to a determination that a message body of a first electronic message satisfies a first set of content-based clustering rules associated with a first message cluster, assigning the first electronic message to the first message cluster, which has a first plurality of electronic messages, wherein each electronic message in the first message cluster is either addressed to the first user or originates from the first user; displaying a first view of a first cluster graphic for the first message cluster, wherein the first view collectively represents the electronic messages in the first message cluster; in response to a first predefined user action, displaying a second view of the first cluster graphic, wherein the second view replaces the first view and displays the first plurality of electronic messages individually within the first message cluster; and in response to a second predefined user action, expanding one or more electronic messages of the first plurality of electronic messages inline within the first message cluster. 12. The method of claim 1 , further comprising: responsive to a third predefined user action in connection with an electronic message included in the first message cluster: disassociating the electronic message from the first cluster graphic. | 0.641498 |
17. The method of claim 11 , wherein the method further comprises the step of: when the reputation score is better than a second predefined threshold, performing a second specified action associated with responding to messages that are not unsolicited, wherein the first predefined threshold is different from the second predefined threshold. | 17. The method of claim 11 , wherein the method further comprises the step of: when the reputation score is better than a second predefined threshold, performing a second specified action associated with responding to messages that are not unsolicited, wherein the first predefined threshold is different from the second predefined threshold. 19. The method of claim 17 , wherein the message is associated with a message recipient, and wherein the step of performing the second specified action comprises sending the message to the message recipient. | 0.891646 |
10. An apparatus for packet classification comprising: a memory for storing a hash table provided with rules for the packet classification; a tuple generator for determining a matching length of how long each field value of one or more fields in an input packet coincides with a field value of a corresponding field stored in a rule set by performing a field-by-field search on the fields in the input packet, generating a tuple list made up of a combination of one or more of the matching length for the respective fields, and selecting particular tuples existing in the rule set from the tuple list; and a packet classifying control for filtering each of the particular tuples selected by the tuple generator by using a Bloom filter to produce positive tuples, and searching for a best matching rule for the input packet by accessing the hash table based on a search pool including exclusively the positive tuples resulting from the filtering. | 10. An apparatus for packet classification comprising: a memory for storing a hash table provided with rules for the packet classification; a tuple generator for determining a matching length of how long each field value of one or more fields in an input packet coincides with a field value of a corresponding field stored in a rule set by performing a field-by-field search on the fields in the input packet, generating a tuple list made up of a combination of one or more of the matching length for the respective fields, and selecting particular tuples existing in the rule set from the tuple list; and a packet classifying control for filtering each of the particular tuples selected by the tuple generator by using a Bloom filter to produce positive tuples, and searching for a best matching rule for the input packet by accessing the hash table based on a search pool including exclusively the positive tuples resulting from the filtering. 17. The apparatus for packet classification in claim 10 , wherein the one or more fields are source prefix field and destination prefix field. | 0.712727 |
1. A computer method of enterprise risk management comprising: defining a hierarchical organizational model, with a server, to represent an enterprise, the model having a master level and one or more levels subordinate to the master level, the master level having plural materiality grades and plural probability grades, each subordinate level including one or more entities having a reporting relationship to one of the entities in the preceding level, each entity having plural materiality grades; collecting risk information comprising plural risk items via a browser interface, each risk item associated with a particular entity in the hierarchical organizational model and having a materiality measure and a probability measure; storing the collected risk information in a risk information database; upon input from a user indicating approval for release of collected risk information, releasing said risk information to a next higher level in the hierarchical organizational model for review and approval by an other user through the browser interface; collecting a risk synonym for at least one of the plural master level materiality grades, the risk synonym tying the materiality measure to a language of the particular entity in the hierarchical organizational model; displaying selected portions of the collected risk information, via the browser interface, in an output view that for each selected risk item relates the probability measure to the plural probability grades and the materiality measure to either the plural master level materiality grades or the plural entity level materiality grades depending on a selected level. | 1. A computer method of enterprise risk management comprising: defining a hierarchical organizational model, with a server, to represent an enterprise, the model having a master level and one or more levels subordinate to the master level, the master level having plural materiality grades and plural probability grades, each subordinate level including one or more entities having a reporting relationship to one of the entities in the preceding level, each entity having plural materiality grades; collecting risk information comprising plural risk items via a browser interface, each risk item associated with a particular entity in the hierarchical organizational model and having a materiality measure and a probability measure; storing the collected risk information in a risk information database; upon input from a user indicating approval for release of collected risk information, releasing said risk information to a next higher level in the hierarchical organizational model for review and approval by an other user through the browser interface; collecting a risk synonym for at least one of the plural master level materiality grades, the risk synonym tying the materiality measure to a language of the particular entity in the hierarchical organizational model; displaying selected portions of the collected risk information, via the browser interface, in an output view that for each selected risk item relates the probability measure to the plural probability grades and the materiality measure to either the plural master level materiality grades or the plural entity level materiality grades depending on a selected level. 19. The computer method of claim 1 wherein outputting selected portions of the collected risk information includes outputting the materiality measure in terms of the risk synonym. | 0.676158 |
1. A method of processing an input comprising: providing a graphical representation of a finite state machine including states and transitions between said states representing a syntax used for determining whether the input is syntactically valid, said graphical representation of said finite state machine including elements representing said states and said transitions between said states; generating, from said graphical representation, a second representation corresponding to said graphical representation; generating, using said second representation, parser source code, wherein said generating the parser source code further includes: generating code for an outer processing loop and a switch statement included within the outer processing loop whereby the switch statement switches between different cases based on a value of a parsing state variable, each of the different cases corresponding to a different parsing state; for each of the different cases corresponding to a different parsing state included in the second representation, performing first processing to generate code for the different parsing state as a case of the switch statement, said first processing including: determining a set of valid next tokens causing a state transition from the different parsing state; generating code to process each of the valid next tokens in the set; and determining a next parsing state included in the second representation for which said first processing is performed; and generating code for a default case of the switch statement corresponding to an error state; processing the parser source code to generate parser executable code for a parser used for parsing the input to determine its syntactic validity in accordance with said syntax, wherein processing the parser source code includes compiling the parser source code; parsing the input using the parser, wherein said parsing includes executing the parser executable code for the parser generated from the parser source code; responsive to said parsing and the input being syntactically correct, generating an intermediate representation of the input, and wherein said syntax is a first of a plurality of syntaxes, said parser is one of a plurality of parsers, a command is expressed in a plurality of different representations corresponding to said plurality of syntaxes, said input is one of the plurality of different representations of said command, and wherein each of said plurality of parsers parses one of the plurality of different representations of the command and generates a same intermediate representation that is said intermediate representation. | 1. A method of processing an input comprising: providing a graphical representation of a finite state machine including states and transitions between said states representing a syntax used for determining whether the input is syntactically valid, said graphical representation of said finite state machine including elements representing said states and said transitions between said states; generating, from said graphical representation, a second representation corresponding to said graphical representation; generating, using said second representation, parser source code, wherein said generating the parser source code further includes: generating code for an outer processing loop and a switch statement included within the outer processing loop whereby the switch statement switches between different cases based on a value of a parsing state variable, each of the different cases corresponding to a different parsing state; for each of the different cases corresponding to a different parsing state included in the second representation, performing first processing to generate code for the different parsing state as a case of the switch statement, said first processing including: determining a set of valid next tokens causing a state transition from the different parsing state; generating code to process each of the valid next tokens in the set; and determining a next parsing state included in the second representation for which said first processing is performed; and generating code for a default case of the switch statement corresponding to an error state; processing the parser source code to generate parser executable code for a parser used for parsing the input to determine its syntactic validity in accordance with said syntax, wherein processing the parser source code includes compiling the parser source code; parsing the input using the parser, wherein said parsing includes executing the parser executable code for the parser generated from the parser source code; responsive to said parsing and the input being syntactically correct, generating an intermediate representation of the input, and wherein said syntax is a first of a plurality of syntaxes, said parser is one of a plurality of parsers, a command is expressed in a plurality of different representations corresponding to said plurality of syntaxes, said input is one of the plurality of different representations of said command, and wherein each of said plurality of parsers parses one of the plurality of different representations of the command and generates a same intermediate representation that is said intermediate representation. 10. The method of claim 1 , wherein said finite state machine is a deterministic finite state machine representing a regular grammar for syntax of a command line, and said parser parses a command line obtained using a command line interface. | 0.552191 |
32. The computer program product of claim 31 , wherein embedding the character set information comprises embedding the character set information in a specified field of the service set identification information element. | 32. The computer program product of claim 31 , wherein embedding the character set information comprises embedding the character set information in a specified field of the service set identification information element. 33. The method of claim 32 , wherein embedding the character set information comprises embedding the character set information in a dedicated encoding field of the service set identification information element. | 0.863566 |
19. The apparatus of claim 16 , wherein when returning the subcache in response to the filtering query, wherein the execution mechanism is configured to indicate a maximum size allowed for the subcache and indicate a maximum age and a purgeable age for identifiers for images in the subcache. | 19. The apparatus of claim 16 , wherein when returning the subcache in response to the filtering query, wherein the execution mechanism is configured to indicate a maximum size allowed for the subcache and indicate a maximum age and a purgeable age for identifiers for images in the subcache. 20. The apparatus of claim 19 , wherein each subcache includes a maximum age and purgeable age limit, wherein the execution mechanism is configured to: remove an identifier for an image from the cache if the identifier for the image is older than the maximum age limit, wherein when removing the identifier for the image, the execution mechanism is configured to removing the underlying image from the location in memory or on disk; mark the identifier for the image as purgeable if the identifier for the image is older than the purgeable age; and wherein when an identifier for an image is marked as purgeable, the wherein the execution mechanism is configured to remove the associated image from the location in memory if wherein the execution mechanism requires the location in memory for another purpose. | 0.640744 |
10. Software stored in one or more computer-readable storage media for execution and when executed operable to: accept, as input at a transformation engine, a data file containing an implementation independent model written in a modeling language, wherein the implementation independent model includes one or more inheritable classes each of which includes zero or more attributes and zero or more relationships to another class; accept, as input at the transformation engine, a configuration file designating as a manageable resource one or more of the inheritable classes included in the implementation independent model, wherein the manageable resource represents a device having manageable capabilities, the manageable capabilities comprising connectivity and identity, and wherein the configuration file identifies one or more of the inheritable classes for exclusion; and output, at the transformation engine, each designated class as a manageable resource, wherein the manageable resource includes any subclasses by inheritance from the designated class unless excluded in the configuration file. | 10. Software stored in one or more computer-readable storage media for execution and when executed operable to: accept, as input at a transformation engine, a data file containing an implementation independent model written in a modeling language, wherein the implementation independent model includes one or more inheritable classes each of which includes zero or more attributes and zero or more relationships to another class; accept, as input at the transformation engine, a configuration file designating as a manageable resource one or more of the inheritable classes included in the implementation independent model, wherein the manageable resource represents a device having manageable capabilities, the manageable capabilities comprising connectivity and identity, and wherein the configuration file identifies one or more of the inheritable classes for exclusion; and output, at the transformation engine, each designated class as a manageable resource, wherein the manageable resource includes any subclasses by inheritance from the designated class unless excluded in the configuration file. 18. The software of claim 10 , wherein the transformation engine comprises the Muse software tool for creating WSDM-compliant interfaces. | 0.559231 |
1. A method for reconstructing character values of a message string given a plurality of strings comprising characters, the method comprising: providing a processing device for implementing the following steps: (A) identifying common strings of characters between pairs of strings selected from the plurality of strings, including, for a pair of strings, determining a longest common subsequence of characters between a first string and a second string; (B) identifying similarity measures between the pairs of strings, including, for a pair of strings: (i) calculating a first similarity value weight between the first string and the second string; (ii) identifying a first set of character positions in the first string that represent the characters in the longest common subsequence; and (iii) identifying a second set of character positions in the second string that represent the characters in the longest common subsequence; and (C) for a location of the message string, determining a reconstructed character value based on a set of candidate character values and a set of exclusion character values and their associated weights obtained using the similarity measures between the pairs of strings, including: updating a set of candidate character values by removing any character values that also are included in a set of exclusion character values; responsive to the updated set of candidate character values containing at least one character value, determining the reconstructed character value by selecting a candidate character value associated with a largest similarity value weight of the similarity value weights associated with the candidate character values in the updated set of candidate character values; responsive to the updated set of candidate character values being an empty set and the set of exclusion character values containing at least one character value, determining the reconstructed character value by selecting an exclusion character value associated with a largest similarity value weight of the similarity value weights associated with the exclusion character values in the set of exclusion character values; and responsive to the updated set of candidate character values and the set of exclusion character values both being empty sets, assigning a character value to the reconstructed character value. | 1. A method for reconstructing character values of a message string given a plurality of strings comprising characters, the method comprising: providing a processing device for implementing the following steps: (A) identifying common strings of characters between pairs of strings selected from the plurality of strings, including, for a pair of strings, determining a longest common subsequence of characters between a first string and a second string; (B) identifying similarity measures between the pairs of strings, including, for a pair of strings: (i) calculating a first similarity value weight between the first string and the second string; (ii) identifying a first set of character positions in the first string that represent the characters in the longest common subsequence; and (iii) identifying a second set of character positions in the second string that represent the characters in the longest common subsequence; and (C) for a location of the message string, determining a reconstructed character value based on a set of candidate character values and a set of exclusion character values and their associated weights obtained using the similarity measures between the pairs of strings, including: updating a set of candidate character values by removing any character values that also are included in a set of exclusion character values; responsive to the updated set of candidate character values containing at least one character value, determining the reconstructed character value by selecting a candidate character value associated with a largest similarity value weight of the similarity value weights associated with the candidate character values in the updated set of candidate character values; responsive to the updated set of candidate character values being an empty set and the set of exclusion character values containing at least one character value, determining the reconstructed character value by selecting an exclusion character value associated with a largest similarity value weight of the similarity value weights associated with the exclusion character values in the set of exclusion character values; and responsive to the updated set of candidate character values and the set of exclusion character values both being empty sets, assigning a character value to the reconstructed character value. 4. A non-transitory computer readable medium comprising one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method of claim 1 . | 0.637894 |
9. A system, comprising: a data processing apparatus; and a non-transitory computer readable storage medium in data communication with the data processing apparatus storing instructions executable by the data processing apparatus and that upon such execution causes the data processing apparatus to perform operations comprising: providing, by the data processing apparatus to a first plurality of user devices, a first user interface that includes: a first task definition describing a first user task to generate a command sentence for an action; a first set of non-terminal fields, each non-terminal field in the set listing a non-terminal type and a terminal that parses to the non-terminal type; and a command sentence input field in which a user-generated command sentence is input by the user; receiving, by the data processing apparatus and from the plurality of user devices, user-generated command sentences input into the command sentence input field; providing, by the data processing apparatus to a second plurality of user devices, a second user interface that includes: one of the user-generated command sentences selected from the received user-generated command sentences; the first set of non-terminal fields, each non-terminal field in the first set listing the non-terminal type and the terminal that parses to the non-terminal type; a second task definition describing a second user task to classify each of a plurality of n-grams of the command sentence as belonging to one of: the non-terminal types in the set of non-terminal types; or none of the non-terminal types in the set of non-terminal types; receiving, by the data processing apparatus and from the second plurality of user devices, second user task response data classifying the n-grams of the command sentence, wherein for each non-terminal type at least a respective first set of n-grams are classified as belonging to the non-terminal type and at least a second set of n-grams are classified as belonging to none of the non-terminal types; generating, by the data processing apparatus, command grammars for the action from the second user task response data, each of the command grammars defining non-terminals of each of the non-terminal types and at least one terminal defining at least one of the second set of n-grams; and persisting the command grammars to a command model. | 9. A system, comprising: a data processing apparatus; and a non-transitory computer readable storage medium in data communication with the data processing apparatus storing instructions executable by the data processing apparatus and that upon such execution causes the data processing apparatus to perform operations comprising: providing, by the data processing apparatus to a first plurality of user devices, a first user interface that includes: a first task definition describing a first user task to generate a command sentence for an action; a first set of non-terminal fields, each non-terminal field in the set listing a non-terminal type and a terminal that parses to the non-terminal type; and a command sentence input field in which a user-generated command sentence is input by the user; receiving, by the data processing apparatus and from the plurality of user devices, user-generated command sentences input into the command sentence input field; providing, by the data processing apparatus to a second plurality of user devices, a second user interface that includes: one of the user-generated command sentences selected from the received user-generated command sentences; the first set of non-terminal fields, each non-terminal field in the first set listing the non-terminal type and the terminal that parses to the non-terminal type; a second task definition describing a second user task to classify each of a plurality of n-grams of the command sentence as belonging to one of: the non-terminal types in the set of non-terminal types; or none of the non-terminal types in the set of non-terminal types; receiving, by the data processing apparatus and from the second plurality of user devices, second user task response data classifying the n-grams of the command sentence, wherein for each non-terminal type at least a respective first set of n-grams are classified as belonging to the non-terminal type and at least a second set of n-grams are classified as belonging to none of the non-terminal types; generating, by the data processing apparatus, command grammars for the action from the second user task response data, each of the command grammars defining non-terminals of each of the non-terminal types and at least one terminal defining at least one of the second set of n-grams; and persisting the command grammars to a command model. 11. The system of claim 9 , wherein: each non-terminal type corresponds to a variable for the action; and for each non-terminal type, a semantic yield of a non-terminal of the non-terminal type defines an argument of the variable for the action. | 0.5 |
1. A system comprising: one or more processors; and a computer-readable storage device storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving a query submitted by a user to a search engine, wherein the query includes a first compound term; and in response to receiving the query, performing the following operations: generating one or more splits of the first compound term, wherein each split divides the compound term into two or more subterms, wherein at least one subterm is a term in a dictionary that associates terms with scores derived from a respective frequency of use of the subterm; assigning a score to one or more subterms of each split that are in the dictionary, wherein the score for a subterm is the score stored in the dictionary for the subterm; determining an overall score for each split from the scores for the subterms of the split; selecting a first split from the one or more splits according to the overall score for each split; and augmenting the query with a first synonym phrase, wherein the first synonym phrase is a synonym of a first subterm of the first split. | 1. A system comprising: one or more processors; and a computer-readable storage device storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving a query submitted by a user to a search engine, wherein the query includes a first compound term; and in response to receiving the query, performing the following operations: generating one or more splits of the first compound term, wherein each split divides the compound term into two or more subterms, wherein at least one subterm is a term in a dictionary that associates terms with scores derived from a respective frequency of use of the subterm; assigning a score to one or more subterms of each split that are in the dictionary, wherein the score for a subterm is the score stored in the dictionary for the subterm; determining an overall score for each split from the scores for the subterms of the split; selecting a first split from the one or more splits according to the overall score for each split; and augmenting the query with a first synonym phrase, wherein the first synonym phrase is a synonym of a first subterm of the first split. 7. The system of claim 1 , wherein the score associated with each term in the dictionary is derived from a frequency with which the term is used in a particular corpus of documents. | 0.591629 |
1. A method comprising: at a server having one or more processors and memory storing programs executable by the processors, receiving from a first user a query for one or more items of interest, the query including at least one search term; obtaining and storing results responsive to the query; after obtaining and storing the results responsive to the query: receiving from the first user a separate navigational query for a first website, wherein the navigational query includes a first identifier of the first web site, and wherein the at least one search term is absent from the navigational query; and in response to receiving the navigational query, when the query is a recent query, obtaining the at least one search term from the recent query and executing an alternate query of the first web site to yield alternate search results responsive to the alternate query within a domain of the first website, the alternate query including the at least one search term and the navigational query; and formatting for display and transmitting to the first user the alternate search results, at least one top ranking navigational search result, and at least one remaining navigational search result, wherein the at least one top ranking navigational search result, the alternate search results, and the at least one remaining navigational search result are associated with a priority attribute that indicates a priority level for display, wherein a first priority attribute is associated with the at least one top ranking navigational search result and indicates that the at least one top ranking navigational search result is to be displayed above other search results displayed on a search result page, a second priority attribute is associated with the alternate query search result and indicates that the alternate query search result is to be displayed below the at least one top ranking navigational search result in the search result page, and a third priority attribute is associated with the at least one remaining navigational search result and indicates that the at least one remaining navigational search result is to be displayed below the alternate query search result in the search result page, the first priority attribute having a highest priority level and the third priority attribute having a lowest priority level. | 1. A method comprising: at a server having one or more processors and memory storing programs executable by the processors, receiving from a first user a query for one or more items of interest, the query including at least one search term; obtaining and storing results responsive to the query; after obtaining and storing the results responsive to the query: receiving from the first user a separate navigational query for a first website, wherein the navigational query includes a first identifier of the first web site, and wherein the at least one search term is absent from the navigational query; and in response to receiving the navigational query, when the query is a recent query, obtaining the at least one search term from the recent query and executing an alternate query of the first web site to yield alternate search results responsive to the alternate query within a domain of the first website, the alternate query including the at least one search term and the navigational query; and formatting for display and transmitting to the first user the alternate search results, at least one top ranking navigational search result, and at least one remaining navigational search result, wherein the at least one top ranking navigational search result, the alternate search results, and the at least one remaining navigational search result are associated with a priority attribute that indicates a priority level for display, wherein a first priority attribute is associated with the at least one top ranking navigational search result and indicates that the at least one top ranking navigational search result is to be displayed above other search results displayed on a search result page, a second priority attribute is associated with the alternate query search result and indicates that the alternate query search result is to be displayed below the at least one top ranking navigational search result in the search result page, and a third priority attribute is associated with the at least one remaining navigational search result and indicates that the at least one remaining navigational search result is to be displayed below the alternate query search result in the search result page, the first priority attribute having a highest priority level and the third priority attribute having a lowest priority level. 5. The method of claim 1 , further comprising, prior to performing the alternative query, confirming that a second user has previously selected results associated with the at least one search term from the recent query, wherein the previously selected results are associated with the first website. | 0.584369 |
5. The computer-implemented method of claim 4 wherein identifying the target community comprises determining the target community based on identification information for the single party. | 5. The computer-implemented method of claim 4 wherein identifying the target community comprises determining the target community based on identification information for the single party. 6. The computer-implemented method of claim 5 further comprising determining the identification information based on an Internet Protocol address, screen name, or a user profile of the single party. | 0.93839 |
9. The one or more computer readable storage media of claim 8 , wherein the first data set includes activity information associated with a behavioral change program and the one or more computer readable storage media further include instructions to cause the one or more server computers to: store the first data set in a data fusion module; and, update the first data set in the data fusion module as activities of the plurality of first users take place over time to generate an updated first data set for the plurality of first users. | 9. The one or more computer readable storage media of claim 8 , wherein the first data set includes activity information associated with a behavioral change program and the one or more computer readable storage media further include instructions to cause the one or more server computers to: store the first data set in a data fusion module; and, update the first data set in the data fusion module as activities of the plurality of first users take place over time to generate an updated first data set for the plurality of first users. 10. The one or more computer readable storage media of claim 9 , wherein the one or more computer readable storage media further include instructions to cause the one or more server computers to: receive the updated first data set for the plurality of first users and update the behavioral models for the selected user segment as the activities of the plurality of first users take place over time. | 0.900986 |
7. A speech synthesis device comprising: a central segment selection unit for selecting a plurality of central segments from among a plurality of speech segments; a prosody generation unit for generating prosody information for each central segment based on the central segments; a non-central segment selection unit for selecting a non-central segment, which is a segment outside of a central segment section, for each central segment based on the central segments and the prosody information; an optimum central segment selection unit for selecting an optimum central segment from among the plurality of central segments; and a waveform generation unit for generating a synthesized speech waveform based on the optimum central segment, prosody information generated based on an optimum central segment, and a non-central segment selected based on an optimum central segment. | 7. A speech synthesis device comprising: a central segment selection unit for selecting a plurality of central segments from among a plurality of speech segments; a prosody generation unit for generating prosody information for each central segment based on the central segments; a non-central segment selection unit for selecting a non-central segment, which is a segment outside of a central segment section, for each central segment based on the central segments and the prosody information; an optimum central segment selection unit for selecting an optimum central segment from among the plurality of central segments; and a waveform generation unit for generating a synthesized speech waveform based on the optimum central segment, prosody information generated based on an optimum central segment, and a non-central segment selected based on an optimum central segment. 8. The speech synthesis device according to claim 7 , wherein the central segment selection unit preferentially selects a speech segment having a long segment length as a central segment. | 0.541532 |
12. A method for managing form-generated data related to a patient encounter, the method comprising: receiving location information related to a form that has designated information fields in different locations on the form, wherein the location information is generated in response to a user writing on the form in one of the designated information fields; translating the location information to a contextualized data element, wherein the contextualized data element comprises contextual information that is associated with the user writing, wherein translating the location information to a contextualized data element comprises using the location information to identify a label by comparing the location information to a mapping data set that maps user areas on the form to labels that are associated with the designated information fields, and wherein the contextualized data element comprises the label; and wherein the label in the contextualized data element is utilized by an EMR/EHR application to perform a function related to the user writing on the form, wherein the contextualized data element is distributed to the EMR/EHR application via a publish/subscribe protocol in which the EMR/EHR application subscribes to a specific contextualized data element by identifying the label associated with the contextualized data element. | 12. A method for managing form-generated data related to a patient encounter, the method comprising: receiving location information related to a form that has designated information fields in different locations on the form, wherein the location information is generated in response to a user writing on the form in one of the designated information fields; translating the location information to a contextualized data element, wherein the contextualized data element comprises contextual information that is associated with the user writing, wherein translating the location information to a contextualized data element comprises using the location information to identify a label by comparing the location information to a mapping data set that maps user areas on the form to labels that are associated with the designated information fields, and wherein the contextualized data element comprises the label; and wherein the label in the contextualized data element is utilized by an EMR/EHR application to perform a function related to the user writing on the form, wherein the contextualized data element is distributed to the EMR/EHR application via a publish/subscribe protocol in which the EMR/EHR application subscribes to a specific contextualized data element by identifying the label associated with the contextualized data element. 16. The method of claim 12 further comprising generating an encounter data set comprising multiple contextualized data elements, which together characterize a patient encounter. | 0.583385 |
1. A computer-implemented method comprising: automatically generating, at a local rule engine, a rule engine vocabulary comprising a context description and a result description that respectively define an input to and an output of an external rule engine, the rule engine vocabulary defining a data type of a result; serializing the rule engine vocabulary in a schema document that, when received at the external rule engine, allows a rule to be defined by the external rule engine based on the context description and the result description; transmitting the schema document to the external rule engine; transmitting a context specified according to the context description as the input to the external rule engine, for evaluation by the rule to provide the result corresponding to the data type; receiving, at the local rule engine, the result specified according to the result description as the output of the external rule engine; and outputting the result. | 1. A computer-implemented method comprising: automatically generating, at a local rule engine, a rule engine vocabulary comprising a context description and a result description that respectively define an input to and an output of an external rule engine, the rule engine vocabulary defining a data type of a result; serializing the rule engine vocabulary in a schema document that, when received at the external rule engine, allows a rule to be defined by the external rule engine based on the context description and the result description; transmitting the schema document to the external rule engine; transmitting a context specified according to the context description as the input to the external rule engine, for evaluation by the rule to provide the result corresponding to the data type; receiving, at the local rule engine, the result specified according to the result description as the output of the external rule engine; and outputting the result. 2. The method of claim 1 , wherein the rule engine vocabulary is generated based on using generic data structures. | 0.581197 |
1. A computer-implemented method of identifying a legitimate search query spike using a computing system having memory, processor, and data storage subsystems, the computer-implemented method comprising: receiving a plurality of search query requests from one or more user input devices; identifying one or more spikes in the received search query requests; clustering the identified spikes together according to a temporal or textual correlation; determining a rate of acceleration in which each spike in the search query requests is received via the processor of the computing system; comparing the determined rate of acceleration for the clustered identified spikes with a similar temporal behavior of stored clusters; identifying a particular clustered spike of received search query requests as a malicious attack when the determined rate of acceleration exceeds a first threshold level and a comparison to temporal behavior is lower than a second threshold level; and storing non-malicious clustered spikes of received search query requests and results as one or more content groups in the data storage subsystem of the computing system for comparison and query suggestions to future related search query requests. | 1. A computer-implemented method of identifying a legitimate search query spike using a computing system having memory, processor, and data storage subsystems, the computer-implemented method comprising: receiving a plurality of search query requests from one or more user input devices; identifying one or more spikes in the received search query requests; clustering the identified spikes together according to a temporal or textual correlation; determining a rate of acceleration in which each spike in the search query requests is received via the processor of the computing system; comparing the determined rate of acceleration for the clustered identified spikes with a similar temporal behavior of stored clusters; identifying a particular clustered spike of received search query requests as a malicious attack when the determined rate of acceleration exceeds a first threshold level and a comparison to temporal behavior is lower than a second threshold level; and storing non-malicious clustered spikes of received search query requests and results as one or more content groups in the data storage subsystem of the computing system for comparison and query suggestions to future related search query requests. 7. The computer-implemented method of claim 1 , wherein the clustering produces a reduced number of false spikes, improves classification accuracy for detecting popular queries, and detects seasonal queries by comparing clustering across a number of time periods. | 0.683236 |
5. The method of claim 2 , wherein the at least one privilege supported by the document data object is a privilege supported by a node object corresponding to the document data object; the step of selecting a set of privilege from the at least one privilege supported by the document data object and granting the set of privilege selected to the role as the set of privilege of the role on the document data object comprises: selecting a set of privilege from the at least one privilege supported by the node object corresponding to the document data object, and granting the privilege selected to the role as the privilege of the role on the document data object. | 5. The method of claim 2 , wherein the at least one privilege supported by the document data object is a privilege supported by a node object corresponding to the document data object; the step of selecting a set of privilege from the at least one privilege supported by the document data object and granting the set of privilege selected to the role as the set of privilege of the role on the document data object comprises: selecting a set of privilege from the at least one privilege supported by the node object corresponding to the document data object, and granting the privilege selected to the role as the privilege of the role on the document data object. 15. The method of claim 5 , wherein controlling the operation of the role on the document data object according to the set of privilege granted to the role on the document data object comprises: determining whether the role has a privilege of performing the operation on the document data object according to the set of privilege of the role on the document data object, if the role does not have the privilege of performing the operation on the document data object, rejecting the operation of the role on the document data object. | 0.723178 |
11. The computer system of claim 10 , wherein the one or more hardware computer processors are configured to execute code in order to cause the one or more hardware computer processors to further: further in response to the second user input, perform the analysis of the second analysis type of the at least part of the data set. | 11. The computer system of claim 10 , wherein the one or more hardware computer processors are configured to execute code in order to cause the one or more hardware computer processors to further: further in response to the second user input, perform the analysis of the second analysis type of the at least part of the data set. 12. The computer system of claim 11 , wherein the one or more hardware computer processors are configured to execute code in order to cause the one or more hardware computer processors to further: spatially position the second data visualization below the first data visualization in the third panel. | 0.904762 |
1. An apparatus, comprising: a processor; and a memory, wherein the processor is configured to determine a category for a group of isolated noun phrases in a structured or semi-structured data source stored in the memory, wherein the group of isolated noun phrases comprises one or more isolated noun phrases; and translate the group of isolated noun phrases from a source language to a target language using a category-driven isolated noun phrase translation, wherein the determination of the category and the category-driven isolated noun phrase translation are performed based on context derived from the group of isolated noun phrases; wherein the processor is further configured to determine the category of the group of isolated noun phrases based on an automatic column categorization that identifies the most likely category of the group of isolated noun phrases that is obtained by combining results obtained for individual cells of the group of isolated noun phrases into a composite score for each category. | 1. An apparatus, comprising: a processor; and a memory, wherein the processor is configured to determine a category for a group of isolated noun phrases in a structured or semi-structured data source stored in the memory, wherein the group of isolated noun phrases comprises one or more isolated noun phrases; and translate the group of isolated noun phrases from a source language to a target language using a category-driven isolated noun phrase translation, wherein the determination of the category and the category-driven isolated noun phrase translation are performed based on context derived from the group of isolated noun phrases; wherein the processor is further configured to determine the category of the group of isolated noun phrases based on an automatic column categorization that identifies the most likely category of the group of isolated noun phrases that is obtained by combining results obtained for individual cells of the group of isolated noun phrases into a composite score for each category. 10. The apparatus of claim 1 , wherein the processor is further configured to divide cell data for the group of isolated noun phrases into tokens representing word-like units that are looked up in at least one of a dictionary and a translation memory and associated with a set of grammatical and semantic features. | 0.544314 |
1. A method of characterizing graphical representation of numerical simulation results comprising: initially creating, by a user, a training database in a computer system by including a plurality of graphical representations of respective simulation results obtained from a plurality of numerical simulations of a product under a design condition; associating, by said user or another user, a textual description with each graphical representation in the computer system, the textual description comprising a pertinent feature related to the numerical simulations; calculating a quality index for said each graphical representation with respect to the associated textual description by one of at least one application module installed in the computer system using an autocorrelation technique of correlating the graphical representations with one another; characterizing, by said one or another of the at least one application module, a new graphical representation obtained in each new numerical simulation with one of the textual descriptions and a corresponding confidence score by comparing the new graphical representation to all of the graphical representations in the training database, the confidence score indicating a level of confidence with regards to said one of the textual descriptions; and updating the training database as a result of the new graphical representation according to predefined training database update criteria. | 1. A method of characterizing graphical representation of numerical simulation results comprising: initially creating, by a user, a training database in a computer system by including a plurality of graphical representations of respective simulation results obtained from a plurality of numerical simulations of a product under a design condition; associating, by said user or another user, a textual description with each graphical representation in the computer system, the textual description comprising a pertinent feature related to the numerical simulations; calculating a quality index for said each graphical representation with respect to the associated textual description by one of at least one application module installed in the computer system using an autocorrelation technique of correlating the graphical representations with one another; characterizing, by said one or another of the at least one application module, a new graphical representation obtained in each new numerical simulation with one of the textual descriptions and a corresponding confidence score by comparing the new graphical representation to all of the graphical representations in the training database, the confidence score indicating a level of confidence with regards to said one of the textual descriptions; and updating the training database as a result of the new graphical representation according to predefined training database update criteria. 9. The method of claim 1 , wherein said comparing the new graphical representation to all of the graphical representations in the training database comprises converting the new graphical representation and said all of the graphical representations to fingerprints before conducting the comparison. | 0.5 |
2. The method of claim 1 wherein the information relating to the communication device comprises an identity of an owner of the communication device. | 2. The method of claim 1 wherein the information relating to the communication device comprises an identity of an owner of the communication device. 3. The method of claim 2 wherein the identity of the communication device's owner is determined based on one or more of (a) a unique identifier associated with the communication device and (b) a persistent login state of the communication device. | 0.936796 |
1. A method for estimating effort for implementing a system associated with a document, comprising: quantifying a structure of the document and evaluating a format of the document using a computing device; identifying a domain of an application associated with the document; defining a set of complexity variables associated with the document based on the structure of the document, a format of the document and a domain of the document; using a neural network to estimate an effort based on the set of complexity variables; and outputting the effort via a tangible medium; wherein quantifying the structure of the document comprises determining a volume of text in the document and determining a distribution of text in the document; wherein evaluating the format of the document comprises comparing the document to a predefined standard; and wherein identifying the domain of the document comprises identifying a subject of the document. | 1. A method for estimating effort for implementing a system associated with a document, comprising: quantifying a structure of the document and evaluating a format of the document using a computing device; identifying a domain of an application associated with the document; defining a set of complexity variables associated with the document based on the structure of the document, a format of the document and a domain of the document; using a neural network to estimate an effort based on the set of complexity variables; and outputting the effort via a tangible medium; wherein quantifying the structure of the document comprises determining a volume of text in the document and determining a distribution of text in the document; wherein evaluating the format of the document comprises comparing the document to a predefined standard; and wherein identifying the domain of the document comprises identifying a subject of the document. 3. The method of claim 1 , wherein quantifying the structure includes defining a set of structural volumetric variables that characterize terms within the document. | 0.508511 |
9. A non-transitory computer readable medium having software that improves a speech intelligibility and speech quality that models a speech segment based on a detected background comprising: a modeling logic that represents the background noise detected from an input signal comprising a desired signal and a plurality of undesired signals; a signal-to-noise logic that approximate a signal-to-noise ratio of at least one of the signals that comprise the input signal; an articulation logic that approximates an articulation index of the at least one of the signals that is processed by the signal-to-noise processor; and shaping logic that adjusts the spectrum of the speech segment to improve an intelligibility and quality of the speech segment, wherein the articulation index measures the intelligibility of the speech segment, and the shaping logic adjusts the spectrum of the speech segment based on a comparison of the articulation index to a plurality of predetermined thresholds. | 9. A non-transitory computer readable medium having software that improves a speech intelligibility and speech quality that models a speech segment based on a detected background comprising: a modeling logic that represents the background noise detected from an input signal comprising a desired signal and a plurality of undesired signals; a signal-to-noise logic that approximate a signal-to-noise ratio of at least one of the signals that comprise the input signal; an articulation logic that approximates an articulation index of the at least one of the signals that is processed by the signal-to-noise processor; and shaping logic that adjusts the spectrum of the speech segment to improve an intelligibility and quality of the speech segment, wherein the articulation index measures the intelligibility of the speech segment, and the shaping logic adjusts the spectrum of the speech segment based on a comparison of the articulation index to a plurality of predetermined thresholds. 15. The non-transitory computer readable medium of claim 9 , wherein the articulation index measures the intelligibility of the speech segment, and the shaping logic adjusts the spectrum of the speech signal based on a comparison of the articulation index to a plurality of predetermined thresholds. | 0.515879 |
10. A method of storing shared text data encoded in a structured document format in a server that is connected to a plurality of clients and observing the shared text data encoded in the structured document format stored in the server from a client, the method comprising: performing structure analysis of the shared text data; specifying a position and a length of a codestream of a mask object structuring each layout object of a structured text based on a result of the structure analysis; updating at least a length of the codestream of the mask object; outputting the updated shared text data; checking authority of a user; issuing the mask object constituting each layout object of a shared text according to the authority of the user; performing structure analysis of the shared text data from which the codestream of the mask object received from the server is deleted; adding the codestream of the mask object issued at the issuing to the shared text data from which the codestream of the mask object received from the server is deleted based on a result of the structure analysis; updating reference information of the shared text data to refer the added codestream of the mask object; and outputting updated shared text data. | 10. A method of storing shared text data encoded in a structured document format in a server that is connected to a plurality of clients and observing the shared text data encoded in the structured document format stored in the server from a client, the method comprising: performing structure analysis of the shared text data; specifying a position and a length of a codestream of a mask object structuring each layout object of a structured text based on a result of the structure analysis; updating at least a length of the codestream of the mask object; outputting the updated shared text data; checking authority of a user; issuing the mask object constituting each layout object of a shared text according to the authority of the user; performing structure analysis of the shared text data from which the codestream of the mask object received from the server is deleted; adding the codestream of the mask object issued at the issuing to the shared text data from which the codestream of the mask object received from the server is deleted based on a result of the structure analysis; updating reference information of the shared text data to refer the added codestream of the mask object; and outputting updated shared text data. 11. The method according to claim 10 , wherein the reference information is a position of the codestream of the mask object from the header of the file and a length of the codestream of the mask object. | 0.874691 |
14. The apparatus of claim 10 further comprises: the one or more managing circuits raise the set of unselected word lines that are adjacent to the selected word line to a fourth voltage during the second time period. | 14. The apparatus of claim 10 further comprises: the one or more managing circuits raise the set of unselected word lines that are adjacent to the selected word line to a fourth voltage during the second time period. 15. The apparatus of claim 14 further comprising: the one or more managing circuits raising the set of unselected word lines that are adjacent to the selected word line to the fourth voltage during the second time period before changing the set of unselected word lines that are adjacent to the selected word line to the third voltage during the second time period, and the fourth voltage is greater than the third voltage. | 0.873584 |
1. A method of utilizing a schema for defining a join relationship, the schema being utilized for merging a database part table with a database table by a database application executing on a computer system, comprising: receiving, in the database application, target table metadata associated with a database part in a plurality of relationship elements in the schema, the database part comprising a target table, wherein an element in the plurality of relationship elements functions as a container for a set of relationships, the set of relationships defining a plurality of merges for the database part table and the database table, wherein the database application provides a user option to at least one of accept and reject one or more of the plurality of merges defined by the set of relationships; receiving, in the database application, source table metadata associated with a database comprising a source table to be joined with the target table in the plurality of relationship elements in the schema, the source table metadata describing a relationship to a class of tables existing outside of the target table; receiving, in the database application, join type metadata in the plurality of relationship elements in the schema, the join type metadata defining how data in the target table is to be merged with data in the source table; and in response to receiving the target table metadata and the source table metadata, merging the target and source tables utilizing the join type metadata. | 1. A method of utilizing a schema for defining a join relationship, the schema being utilized for merging a database part table with a database table by a database application executing on a computer system, comprising: receiving, in the database application, target table metadata associated with a database part in a plurality of relationship elements in the schema, the database part comprising a target table, wherein an element in the plurality of relationship elements functions as a container for a set of relationships, the set of relationships defining a plurality of merges for the database part table and the database table, wherein the database application provides a user option to at least one of accept and reject one or more of the plurality of merges defined by the set of relationships; receiving, in the database application, source table metadata associated with a database comprising a source table to be joined with the target table in the plurality of relationship elements in the schema, the source table metadata describing a relationship to a class of tables existing outside of the target table; receiving, in the database application, join type metadata in the plurality of relationship elements in the schema, the join type metadata defining how data in the target table is to be merged with data in the source table; and in response to receiving the target table metadata and the source table metadata, merging the target and source tables utilizing the join type metadata. 7. The method of claim 1 , wherein receiving, in the database application, target table metadata associated with the database part in a plurality of relationship elements in the schema, comprises receiving target table metadata in a plurality of markup language elements. | 0.564162 |
17. A computer program product, comprising a non-transitory computer-usable medium having a computer-readable program code embodied therein, the computer-readable program code adapted to be executed on a processor to implement a method for delivering a message, the method comprising: receiving, by a computing device from a message server, an audio message from a prioritized list of audio messages, wherein the audio message in the prioritized list of audio messages is prioritized based on an aggregate message priority value for the audio message, the aggregate message priority value comprising a product of a first weight and a first sender designated priority of the audio message, and a product of a second weight and a first recipient prioritization attribute of the audio message; determining a delegate for responding to the audio message; determining a delegation action identifier identifying a delegation action of a plurality of delegation actions directing the delegate; and communicating a delegate identifier identifying the delegate and the delegation action identifier to the message server for delegation of the audio message to the delegate in accordance with the delegation action. | 17. A computer program product, comprising a non-transitory computer-usable medium having a computer-readable program code embodied therein, the computer-readable program code adapted to be executed on a processor to implement a method for delivering a message, the method comprising: receiving, by a computing device from a message server, an audio message from a prioritized list of audio messages, wherein the audio message in the prioritized list of audio messages is prioritized based on an aggregate message priority value for the audio message, the aggregate message priority value comprising a product of a first weight and a first sender designated priority of the audio message, and a product of a second weight and a first recipient prioritization attribute of the audio message; determining a delegate for responding to the audio message; determining a delegation action identifier identifying a delegation action of a plurality of delegation actions directing the delegate; and communicating a delegate identifier identifying the delegate and the delegation action identifier to the message server for delegation of the audio message to the delegate in accordance with the delegation action. 18. The computer program product of claim 17 , wherein the plurality of delegation actions comprises: 1) a first delegation action directing the delegate to generate a response and send the response to a sender of the audio message without notifying a delegator; 2) a second delegation action directing the delegate to generate the response and send the response to both the sender and the delegator; and 3) a third delegation action directing the delegate to generate the response and send the response to the delegator. | 0.5 |
15. A method of depicting a message in a a first language in a second language comprising: using a communication device having a screen on which said message in said first language is to be depicted in said second language, said screen displaying a programmable grid or list, wherein each element of the grid or list has at least one sentence, phrase, word, or character of the message displayed in the first language, each grid or list element has an associated indicator indicating to a user the order in which the at least one sentence, phrase, word, or character of the respective grid or list element should be selected relative to the other grid or list elements in order to depict the message in correct grammatical form in said second language; and depicting on the screen the at least one sentence, phrase, word, or character of the message in the second language and in the order in which the at least one sentence, phrase, word, or character of the message is selected in the first language by the user. | 15. A method of depicting a message in a a first language in a second language comprising: using a communication device having a screen on which said message in said first language is to be depicted in said second language, said screen displaying a programmable grid or list, wherein each element of the grid or list has at least one sentence, phrase, word, or character of the message displayed in the first language, each grid or list element has an associated indicator indicating to a user the order in which the at least one sentence, phrase, word, or character of the respective grid or list element should be selected relative to the other grid or list elements in order to depict the message in correct grammatical form in said second language; and depicting on the screen the at least one sentence, phrase, word, or character of the message in the second language and in the order in which the at least one sentence, phrase, word, or character of the message is selected in the first language by the user. 27. The method of claim 15 further comprising receiving inputs from a pointer device used by a user to follow the order to allow said at least one sentence, phrase, word, or character-to be depicted in the correct order, wherein said pointer device is selected from the group consisting of a mouse, stylus, and finger. | 0.5 |
9. A challenge search query game system, comprising: a search server computing system configured to execute an application programming interface (API), the API being configured to: receive a challenge query from a game program in response to a selection by a game player of a mini-game selector in a game published by a game publisher, the challenge query including game player profile information; retrieve a challenge in response to the challenge query, the challenge including one or more clues and a solution, the challenge based on at least one of game player profile information and search trend data stored on the search server computing system; send the challenge to the game program for display in the game, causing the game program to store the challenge and present a first clue from the one or more clues to the game player; receive a search query from the game program in response to providing the first clue to the game player, the search query based on one or more keywords input by the game player; cause the search server computing system to determine if the one or more keywords match the solution to the challenge; if the one or more keywords match the solution to the challenge: provide search results, sponsored links, and a solution notification embedded in the search results to the game program; and in response to the game player selecting the solution notification, send a message to the game program to return the game player to the game and provide an in-game reward in the game; if the one or more keywords do not match the solution to the challenge: provide search results and sponsored links; and in response to an action by the game player, present a second clue to the game program, where the second clue is different from the first clue; and in response to the game player selecting a sponsored link, generate and share revenue with the game publisher of the game. | 9. A challenge search query game system, comprising: a search server computing system configured to execute an application programming interface (API), the API being configured to: receive a challenge query from a game program in response to a selection by a game player of a mini-game selector in a game published by a game publisher, the challenge query including game player profile information; retrieve a challenge in response to the challenge query, the challenge including one or more clues and a solution, the challenge based on at least one of game player profile information and search trend data stored on the search server computing system; send the challenge to the game program for display in the game, causing the game program to store the challenge and present a first clue from the one or more clues to the game player; receive a search query from the game program in response to providing the first clue to the game player, the search query based on one or more keywords input by the game player; cause the search server computing system to determine if the one or more keywords match the solution to the challenge; if the one or more keywords match the solution to the challenge: provide search results, sponsored links, and a solution notification embedded in the search results to the game program; and in response to the game player selecting the solution notification, send a message to the game program to return the game player to the game and provide an in-game reward in the game; if the one or more keywords do not match the solution to the challenge: provide search results and sponsored links; and in response to an action by the game player, present a second clue to the game program, where the second clue is different from the first clue; and in response to the game player selecting a sponsored link, generate and share revenue with the game publisher of the game. 11. The system of claim 9 , wherein the game player profile information includes player search history and does not include personally identifiable information unless the player opts-in to share personally identifiable information with the search server computing system. | 0.504396 |
1. A social media content access system, comprising: a memory; a processor disposed in communication with said memory, and configured to issue a plurality of processing instructions stored in the memory, wherein the processor issues instructions for: identifying a request from a server, to access user social media content; obtaining, at the server, from a user, user authorization credentials to access user social media content; sending an access request with the obtained user authorization credentials to a social media platform; receiving an indication of a user experienced media asset indicating the user has been exposed to the media asset; receiving social media content data from the social media platform; determining a data format type of the received social media content data; tagging the received social media content data based on the data format type according to a progressive taxonomy mechanism of hierarchical category tags; receiving a social media analytics request related to consumer impression for the user experienced media asset; selecting a set of key terms descriptive of characteristics of the user experienced media asset; querying the tagged social media content data based on the selected key terms related to the user experienced media asset; determining the queried tagged social media content data is related to the user experienced media asset; and determining a consumer impression level for the user experienced media asset based on the queried tagged social media content data from query results. | 1. A social media content access system, comprising: a memory; a processor disposed in communication with said memory, and configured to issue a plurality of processing instructions stored in the memory, wherein the processor issues instructions for: identifying a request from a server, to access user social media content; obtaining, at the server, from a user, user authorization credentials to access user social media content; sending an access request with the obtained user authorization credentials to a social media platform; receiving an indication of a user experienced media asset indicating the user has been exposed to the media asset; receiving social media content data from the social media platform; determining a data format type of the received social media content data; tagging the received social media content data based on the data format type according to a progressive taxonomy mechanism of hierarchical category tags; receiving a social media analytics request related to consumer impression for the user experienced media asset; selecting a set of key terms descriptive of characteristics of the user experienced media asset; querying the tagged social media content data based on the selected key terms related to the user experienced media asset; determining the queried tagged social media content data is related to the user experienced media asset; and determining a consumer impression level for the user experienced media asset based on the queried tagged social media content data from query results. 11. The system of claim 1 , wherein the type of the received media content data comprises any of structured data and unstructured data. | 0.528097 |
1. A computer-implemented method of recognizing a first language used in information content, comprising: at a computer having one or more processors and memory for storing programs to be executed by the one or more processors: integrating a first vocabulary list and a second vocabulary list that are built based on a first language and a second language, respectively, into a comprehensive vocabulary list, wherein the integrating includes analyzing the first vocabulary list in view of the second vocabulary list to at least identify a first vocabulary sub-list, in the comprehensive vocabulary list, that is used in the first language, but not in the second language; identifying, within the information content, a plurality of expressions that are included in the comprehensive vocabulary list; identifying, within the plurality of expressions, a subset of expressions that are included in the first vocabulary sub-list; determining that a total frequency of occurrence of the subset of expressions within the information content meets predetermined occurrence criteria; and in accordance with the determination, determining that the information content is composed in the first language. | 1. A computer-implemented method of recognizing a first language used in information content, comprising: at a computer having one or more processors and memory for storing programs to be executed by the one or more processors: integrating a first vocabulary list and a second vocabulary list that are built based on a first language and a second language, respectively, into a comprehensive vocabulary list, wherein the integrating includes analyzing the first vocabulary list in view of the second vocabulary list to at least identify a first vocabulary sub-list, in the comprehensive vocabulary list, that is used in the first language, but not in the second language; identifying, within the information content, a plurality of expressions that are included in the comprehensive vocabulary list; identifying, within the plurality of expressions, a subset of expressions that are included in the first vocabulary sub-list; determining that a total frequency of occurrence of the subset of expressions within the information content meets predetermined occurrence criteria; and in accordance with the determination, determining that the information content is composed in the first language. 13. The method of claim 1 , wherein the first vocabulary list and the second vocabulary list are analyzed to identify a mixed vocabulary sub-list and a second vocabulary sub-list in the comprehensive vocabulary list, the mixed vocabulary sub-list being used in both the first and second languages, the second vocabulary sub-list being used in the second language, but not in the first language, the total frequency of occurrence comprising a first frequency of occurrence, the method further comprising: determining, in the information content, a second frequency of occurrence for expressions included in the second vocabulary sub-list; determining, in the information content, a mixed frequency of occurrence for expressions included in the mixed vocabulary sub-list; and determining a primary language used by the information content based on the first frequency of occurrence, the second frequency of occurrence and the mixed frequency of occurrence. | 0.502708 |
1. A computer-implemented method comprising: identifying one or more sessions for a query, each session comprising a chain of respective resources linked to each other and watched by a respective user, each session beginning with a first resource that was identified by a first search result responsive to the query and linked to one or more second resources, wherein each second resource was associated with a different resource in the session by a respective link, and wherein the user visited each second resource by following the links; associating a total of watch times of the respective resources watched in the sessions with the query; calculating one or more watch time signals for the first resource and the query based on the total of watch times associated with the query; after the one or more sessions have ended: receiving the query from a user; obtaining a search result responsive to the query, wherein the search result identifies the first resource and has an associated score S; calculating an updated score S′ based on at least S and a watch time function, the watch time function being a function of the one or more watch time signals; and providing the updated score S′ to a process for ranking search results including the search result. | 1. A computer-implemented method comprising: identifying one or more sessions for a query, each session comprising a chain of respective resources linked to each other and watched by a respective user, each session beginning with a first resource that was identified by a first search result responsive to the query and linked to one or more second resources, wherein each second resource was associated with a different resource in the session by a respective link, and wherein the user visited each second resource by following the links; associating a total of watch times of the respective resources watched in the sessions with the query; calculating one or more watch time signals for the first resource and the query based on the total of watch times associated with the query; after the one or more sessions have ended: receiving the query from a user; obtaining a search result responsive to the query, wherein the search result identifies the first resource and has an associated score S; calculating an updated score S′ based on at least S and a watch time function, the watch time function being a function of the one or more watch time signals; and providing the updated score S′ to a process for ranking search results including the search result. 5. The method of claim 1 , wherein the watch time function is given by:
S′=S×M Q,D i , wherein M Q,D i is a watch time multiplier computed from the one or more watch time signals for the query and the first resource. | 0.608827 |
11. A computing device for detecting ruby text in a fixed format document, comprising: a processing unit; and a memory including computer-readable instructions which when executed by the processor are operable to: detect, at a parser, a fixed format document; detect, at a line detection engine, one or more lines in the fixed format document containing one or more attributes of a ruby line; retain the one or more lines in the fixed format document containing one or more attributes of a ruby line as ruby line candidates and a line successive to the one or more lines as ruby base line candidates; analyze, by a document processor, the ruby line candidate for finding one or more ruby texts contained in the ruby line candidate; match the one or more ruby texts with a corresponding ruby base text in a successive ruby base line candidate for reconstruction in a flow format document; and reconstruct, by a serializer, the fixed format document as the flow format document containing the matched one or more ruby texts and the corresponding ruby base text. | 11. A computing device for detecting ruby text in a fixed format document, comprising: a processing unit; and a memory including computer-readable instructions which when executed by the processor are operable to: detect, at a parser, a fixed format document; detect, at a line detection engine, one or more lines in the fixed format document containing one or more attributes of a ruby line; retain the one or more lines in the fixed format document containing one or more attributes of a ruby line as ruby line candidates and a line successive to the one or more lines as ruby base line candidates; analyze, by a document processor, the ruby line candidate for finding one or more ruby texts contained in the ruby line candidate; match the one or more ruby texts with a corresponding ruby base text in a successive ruby base line candidate for reconstruction in a flow format document; and reconstruct, by a serializer, the fixed format document as the flow format document containing the matched one or more ruby texts and the corresponding ruby base text. 15. The computing device of claim 11 , wherein detecting one or more lines in the fixed format document containing one or more attributes of a ruby line comprises: analyzing the one or more lines of text for determining if a font size of characters in a line of text is smaller than a font size of characters in a successive line of text; if the font size of the characters in the line of text is smaller than the font size of the characters in the successive line of text, retaining the line of text as a ruby line candidate and the successive line of text as a ruby base line candidate. | 0.5 |
2. A knowledge base system for retrieving information by using a concept relation model which expresses knowledge y a plurality of interconnected nodes, each node represents a concept corresponding to information included in said node, said node being connected to other nodes by links, each of said links represents a relation between concepts represented by nodes connected by said link, comprising: a first memory for storing concept names corresponding to said nodes, each concept name corresponds to a concept; a second memory for storing information representing subsumption relations between different concepts corresponding to links connected between said concepts; a third memory for storing information representing definitions of different kinds of relations; a fourth memory for storing information representing relations between different concepts corresponding to links connected between said concepts; and relation deducting means for inputting concept names from a user of said system and deducting relations between concepts indicated by said inputted concept names by using knowledge stored in said first to fourth memory; retrieval means for retrieving information from at least one of said nodes based on said inputted concept names and said relations deducted by said relation deducting means; a fifth memory for storing syntax rules for understanding at least one natural language; a sixth memory for storing a lexicon, which can be edited based on the content of said first and third memories; lexical analysis means for effecting lexical analysis by inputting a sentence written in the natural language and referring to information coming from said sixth memory; syntactic analysis means for forming possible sentence structures based on results of the lexical analysis and information stored in said fifth memory; and means for extracting and selecting only sentence structures appropriate in meaning relative to the inputted sentence formed based on a result of syntactic analysis by said relation deducting means. | 2. A knowledge base system for retrieving information by using a concept relation model which expresses knowledge y a plurality of interconnected nodes, each node represents a concept corresponding to information included in said node, said node being connected to other nodes by links, each of said links represents a relation between concepts represented by nodes connected by said link, comprising: a first memory for storing concept names corresponding to said nodes, each concept name corresponds to a concept; a second memory for storing information representing subsumption relations between different concepts corresponding to links connected between said concepts; a third memory for storing information representing definitions of different kinds of relations; a fourth memory for storing information representing relations between different concepts corresponding to links connected between said concepts; and relation deducting means for inputting concept names from a user of said system and deducting relations between concepts indicated by said inputted concept names by using knowledge stored in said first to fourth memory; retrieval means for retrieving information from at least one of said nodes based on said inputted concept names and said relations deducted by said relation deducting means; a fifth memory for storing syntax rules for understanding at least one natural language; a sixth memory for storing a lexicon, which can be edited based on the content of said first and third memories; lexical analysis means for effecting lexical analysis by inputting a sentence written in the natural language and referring to information coming from said sixth memory; syntactic analysis means for forming possible sentence structures based on results of the lexical analysis and information stored in said fifth memory; and means for extracting and selecting only sentence structures appropriate in meaning relative to the inputted sentence formed based on a result of syntactic analysis by said relation deducting means. 3. A knowledge base system according to claim 2, wherein the inputted sentence written in the natural language consists of a series of nouns and said relation deducting means deducts the relation between different nouns constituting said series of nouns on the basis of an assembly of facts stored in a knowledge base described by the concepts and the relations. | 0.522227 |
6. A system comprising: accessing search queries that were previously submitted by users and a corresponding set of search results that were previously presented in search results pages for each of the search queries, each search result in the set of the search results including a link to a corresponding web page; identifying, for a given web page that was previously accessed through user interaction with links included in search results from the set of search results, a set of the search queries that were previously used to provide search results pages that included the links with which the user interactions occurred to access the given web page; determining, for each given search query in the set of search queries, a feature score based on a user dwell time at the given web page following user interaction with the links to the given web page that were provided in the search results pages for the given search query, including assigning a higher scores for higher dwell times at the given web page; storing, based on the determined features scores, one or more of the search queries from the identified set of search queries as keywords for the given web page, including storing the given query having a best feature score as a given keyword for the given web page; receiving a request for content to be displayed with the given web page; selecting content for display with the given web page based on the given keyword for the given web page matching terms used to distribute the content; and transmitting, via a network, the selected content for display with the given web page responsive to the request for content. | 6. A system comprising: accessing search queries that were previously submitted by users and a corresponding set of search results that were previously presented in search results pages for each of the search queries, each search result in the set of the search results including a link to a corresponding web page; identifying, for a given web page that was previously accessed through user interaction with links included in search results from the set of search results, a set of the search queries that were previously used to provide search results pages that included the links with which the user interactions occurred to access the given web page; determining, for each given search query in the set of search queries, a feature score based on a user dwell time at the given web page following user interaction with the links to the given web page that were provided in the search results pages for the given search query, including assigning a higher scores for higher dwell times at the given web page; storing, based on the determined features scores, one or more of the search queries from the identified set of search queries as keywords for the given web page, including storing the given query having a best feature score as a given keyword for the given web page; receiving a request for content to be displayed with the given web page; selecting content for display with the given web page based on the given keyword for the given web page matching terms used to distribute the content; and transmitting, via a network, the selected content for display with the given web page responsive to the request for content. 7. The system of claim 6 , wherein at least one search query in the set of search queries comprises one or more n-grams. | 0.55162 |
16. A computer-readable storage medium having instructions stored thereon that, when executed, cause a processor to perform a method comprising: in response to receiving, from a personal information management application, a request message comprising a find places request and content from a location field within a meeting item of the personal information management application, parsing the request message for a place name, street address, or source-related identifier, wherein the meeting item includes: a meeting request form, appointment, email, calendar entry, or a contact entry; querying a web service, a mailbox associated with a user of the personal information management application, and/or a managed database using the place name, the street address, or the source-related identifier; receiving results of the query; and filtering and formatting the results to generate a response message, the response message comprising location information associated with the place name or the source-related identifier indicated by the request message. | 16. A computer-readable storage medium having instructions stored thereon that, when executed, cause a processor to perform a method comprising: in response to receiving, from a personal information management application, a request message comprising a find places request and content from a location field within a meeting item of the personal information management application, parsing the request message for a place name, street address, or source-related identifier, wherein the meeting item includes: a meeting request form, appointment, email, calendar entry, or a contact entry; querying a web service, a mailbox associated with a user of the personal information management application, and/or a managed database using the place name, the street address, or the source-related identifier; receiving results of the query; and filtering and formatting the results to generate a response message, the response message comprising location information associated with the place name or the source-related identifier indicated by the request message. 17. The medium of claim 16 , wherein the request message comprises at least one parameter selected from the group consisting of a query string, a source-related location identifier, a culture parameter, a maximum number of results, a source of location information, and geo-coordinates of a location or user. | 0.578065 |
1. A computer-implemented method for speech recognition, the computer-implemented method comprising: accessing acoustic data of a first speaker, the acoustic data of the first speaker being a collection of recorded utterances spoken by the first speaker; accessing a baseline acoustic speech model of an automated speech recognition system, the baseline acoustic speech model having a plurality of acoustic parameters used in converting spoken words to text; estimating, using a maximum a posteriori probability process, statistical changes to acoustic parameters of the baseline acoustic speech model that improve speech recognition accuracy of the acoustic model when executing speech recognition on utterances of the first speaker, wherein using the maximum a posteriori probability process includes comparing an analysis of the acoustic data of the first speaker to the plurality of acoustic parameters of the baseline acoustic speech model, wherein using the maximum a posteriori probability process includes restricting estimation of statistical changes such that an amount of acoustic parameters from the baseline acoustic speech model that have an estimated statistical change is less than a total number of acoustic parameters included in the baseline acoustic speech model; and storing changes to a set of acoustic parameters corresponding to acoustic parameters from the baseline acoustic speech model that have an estimated statistical change, the changes being stored as acoustic parameter adaptation data linked to the first speaker. | 1. A computer-implemented method for speech recognition, the computer-implemented method comprising: accessing acoustic data of a first speaker, the acoustic data of the first speaker being a collection of recorded utterances spoken by the first speaker; accessing a baseline acoustic speech model of an automated speech recognition system, the baseline acoustic speech model having a plurality of acoustic parameters used in converting spoken words to text; estimating, using a maximum a posteriori probability process, statistical changes to acoustic parameters of the baseline acoustic speech model that improve speech recognition accuracy of the acoustic model when executing speech recognition on utterances of the first speaker, wherein using the maximum a posteriori probability process includes comparing an analysis of the acoustic data of the first speaker to the plurality of acoustic parameters of the baseline acoustic speech model, wherein using the maximum a posteriori probability process includes restricting estimation of statistical changes such that an amount of acoustic parameters from the baseline acoustic speech model that have an estimated statistical change is less than a total number of acoustic parameters included in the baseline acoustic speech model; and storing changes to a set of acoustic parameters corresponding to acoustic parameters from the baseline acoustic speech model that have an estimated statistical change, the changes being stored as acoustic parameter adaptation data linked to the first speaker. 5. The computer-implemented method of claim 1 , wherein restricting estimation of statistical changes includes restricting estimation of statistical changes such that an amount of acoustic parameters from the baseline acoustic speech model that have an estimated statistical change is based on a predetermined amount of acoustic parameters indicated to have a statistical change. | 0.646552 |
10. The computer-implemented method of claim 8 , and comprising: accessing speech data and forming the speech lattice by generating the plurality of speech recognition hypotheses from the speech data, wherein generating the plurality of speech recognition hypotheses comprises generating a first text transcript for the portion of speech data and at least one alternative speech recognition hypothesis for the portion of speech data. | 10. The computer-implemented method of claim 8 , and comprising: accessing speech data and forming the speech lattice by generating the plurality of speech recognition hypotheses from the speech data, wherein generating the plurality of speech recognition hypotheses comprises generating a first text transcript for the portion of speech data and at least one alternative speech recognition hypothesis for the portion of speech data. 11. The computer-implemented method of claim 10 , wherein the plurality of speech recognition hypotheses comprise hypotheses for a phrase in the speech data. | 0.922195 |
1. An apparatus for processing a data stream, the apparatus comprising: a cluster generator configured to generate at least one cluster of queries based on a commonality of words included in the queries, and to extract a representative query for each generated cluster; a query classification processor configured to, in response to receiving a query, determine a cluster to which the received query belongs, and map the received query to a representative query of the determined cluster; and a query execution unit configured to execute the representative query from the determined cluster to determine an answer of the representative query based on the data stream, and to provide the answer of the representative query as an approximate answer of the received query instead of processing the received query, wherein the queries comprise a feature vector, and the cluster generator is further configured to generate the cluster based on the feature vector of the queries. | 1. An apparatus for processing a data stream, the apparatus comprising: a cluster generator configured to generate at least one cluster of queries based on a commonality of words included in the queries, and to extract a representative query for each generated cluster; a query classification processor configured to, in response to receiving a query, determine a cluster to which the received query belongs, and map the received query to a representative query of the determined cluster; and a query execution unit configured to execute the representative query from the determined cluster to determine an answer of the representative query based on the data stream, and to provide the answer of the representative query as an approximate answer of the received query instead of processing the received query, wherein the queries comprise a feature vector, and the cluster generator is further configured to generate the cluster based on the feature vector of the queries. 8. The apparatus of claim 1 , further comprising: a feature vector extraction unit configured to extract at least one feature vector from each of the queries, a dimension graph generation unit configured to define a vector space in a predetermined number of dimensions based on a number of the at least one feature vector, wherein the cluster generator is configured to map the queries in the predetermined number of dimensions to generate the at least one cluster. | 0.5 |
1. A method for automatic construction of a planogram, comprising: receiving one or more shelving images, assembling said images, in the case where there are several, automatic construction of a structure constituting the planogram, automatic recognition of products contained in the images, positioning the products in the planogram according to the results of the previous automatic recognition of the products and the results of the previous detection of the structure, wherein the automatic recognition step comprises at a minimum: a) an initial recognition step, resulting in a set of possibilities, each possibility being in a form of an ordered pair consisting of a hypothesis and a a probability, called a detection probability, that said hypothesis is true, in which the hypothesis is an ordered pair consisting of a position in the image and an identified product, wherein this initial recognition step consists of a first classification step for classifying products according to several categories, wherein the number of categories is less than the number of products, and a first filtering step of global filtering of these results by re-evaluating the detection probability of each hypothesis in each possibility using probabilistic methods for integrating information specific to the products, and b) a second recognition step, using the resulting set of possibilities from the original recognition step to make a new detection, wherein this second recognition step consists of a second classification step based on matchings of points of interest, and a second filtering step at the planogram level that consists of reevaluating the detection probability of each hypothesis in each possibility using probabilistic methods for integrating global information on a context previously estimated on a base of planograms, each of the first classification step, first filtering step, second classification step and second filtering step being followed immediately by a selection step of selecting the best candidates, each selection step respectively corresponding to a thresholding of the detection probabilities. | 1. A method for automatic construction of a planogram, comprising: receiving one or more shelving images, assembling said images, in the case where there are several, automatic construction of a structure constituting the planogram, automatic recognition of products contained in the images, positioning the products in the planogram according to the results of the previous automatic recognition of the products and the results of the previous detection of the structure, wherein the automatic recognition step comprises at a minimum: a) an initial recognition step, resulting in a set of possibilities, each possibility being in a form of an ordered pair consisting of a hypothesis and a a probability, called a detection probability, that said hypothesis is true, in which the hypothesis is an ordered pair consisting of a position in the image and an identified product, wherein this initial recognition step consists of a first classification step for classifying products according to several categories, wherein the number of categories is less than the number of products, and a first filtering step of global filtering of these results by re-evaluating the detection probability of each hypothesis in each possibility using probabilistic methods for integrating information specific to the products, and b) a second recognition step, using the resulting set of possibilities from the original recognition step to make a new detection, wherein this second recognition step consists of a second classification step based on matchings of points of interest, and a second filtering step at the planogram level that consists of reevaluating the detection probability of each hypothesis in each possibility using probabilistic methods for integrating global information on a context previously estimated on a base of planograms, each of the first classification step, first filtering step, second classification step and second filtering step being followed immediately by a selection step of selecting the best candidates, each selection step respectively corresponding to a thresholding of the detection probabilities. 3. The method according to claim 1 , to which is added, following the second recognition step, a specialised recognition step comprising a step of classification and then selection of the best candidates and a step of global filtering and then selection of the best candidates, wherein the specialised recognition step distinguishes between products that are identical to within a few details, using visual identification algorithms. | 0.5 |
2. The method of claim 1 , further comprising highlighting the linguistic element of a current key selection with a third highlighting to distinguish the current key selection from the highlighted keys corresponding to the default language object. | 2. The method of claim 1 , further comprising highlighting the linguistic element of a current key selection with a third highlighting to distinguish the current key selection from the highlighted keys corresponding to the default language object. 4. The method of claim 2 , further comprising: identifying, for each successive key selection of the input as the current key selection, a default language object having at least an initial portion that corresponds with the linguistic elements of the input; and when the default language object is a different language object than that which was identified as a preceding default language object, highlighting keys on the keyboard corresponding to the different language object with the first highlighting. | 0.718646 |
13. A non-transitory computer-readable storage medium having stored thereon computer executable program code, which, when executed by a computer, causes the computer to perform a method of: receiving a Simple Protocol And Resource Description Framework Query Language (SPARQL) query; generating a native query from the SPARQL query using a mapping file comprising metadata that describes a multidimensional database; mapping a first Resource Description Framework (RDF) class specified in the SPARQL query with a first column in the multidimensional database; mapping a first property corresponding to the first RDF class specified in the SPARQL query with first values corresponding to the first column in the multidimensional database; executing the native query on the multidimensional database; receiving a native result comprising data stored in the multidimensional database resulting from execution of the native query against the multidimensional database; and generating a SPARQL result from the native result using the mapping file, the SPARQL result representing a response to the SPARQL query. | 13. A non-transitory computer-readable storage medium having stored thereon computer executable program code, which, when executed by a computer, causes the computer to perform a method of: receiving a Simple Protocol And Resource Description Framework Query Language (SPARQL) query; generating a native query from the SPARQL query using a mapping file comprising metadata that describes a multidimensional database; mapping a first Resource Description Framework (RDF) class specified in the SPARQL query with a first column in the multidimensional database; mapping a first property corresponding to the first RDF class specified in the SPARQL query with first values corresponding to the first column in the multidimensional database; executing the native query on the multidimensional database; receiving a native result comprising data stored in the multidimensional database resulting from execution of the native query against the multidimensional database; and generating a SPARQL result from the native result using the mapping file, the SPARQL result representing a response to the SPARQL query. 18. The non-transitory computer-readable storage medium of claim 13 , wherein the native query is expressed as an Multidimensional Expressions (MDX) query. | 0.726316 |
1. A computer-implemented method for displaying bi-directional text on a computer display comprising: detecting by a computer that a string of characters for display to a human interface device contains one or more Arabic Letters followed by one or more European Numbers; responsive to the detecting, treating by the computer the one or more European Numbers as one or more Arabic Numbers by: assigning bidirectional attributes to a logical character stream; assigning initial level numbers while honoring any directional overrides by explicit processing, wherein the directional overrides include Left-to-right display order and Right-to-left display order; changing attribute types based upon surrounding attribute types through weak processing and neutral processing, wherein, during the weak processing, a directional override is changed to Right-to-left display order for a last Arabic Letter of the one or more Arabic Letters which immediate precedes a first character of the one or more European Numbers while retaining an attribute type of Arabic Letter for the last Arabic Letter, thereby causing the first European Number to change to an attribute type of Arabic Number; associating final level numbers to the logical character stream through implicit processing; and reordering the string of characters within the logical character stream into display order according to the final level numbers by separately handling facets of layout relating to character reordering and facets related to character stream rendering; and displaying by a computer the reordered string of characters to a human interface device. | 1. A computer-implemented method for displaying bi-directional text on a computer display comprising: detecting by a computer that a string of characters for display to a human interface device contains one or more Arabic Letters followed by one or more European Numbers; responsive to the detecting, treating by the computer the one or more European Numbers as one or more Arabic Numbers by: assigning bidirectional attributes to a logical character stream; assigning initial level numbers while honoring any directional overrides by explicit processing, wherein the directional overrides include Left-to-right display order and Right-to-left display order; changing attribute types based upon surrounding attribute types through weak processing and neutral processing, wherein, during the weak processing, a directional override is changed to Right-to-left display order for a last Arabic Letter of the one or more Arabic Letters which immediate precedes a first character of the one or more European Numbers while retaining an attribute type of Arabic Letter for the last Arabic Letter, thereby causing the first European Number to change to an attribute type of Arabic Number; associating final level numbers to the logical character stream through implicit processing; and reordering the string of characters within the logical character stream into display order according to the final level numbers by separately handling facets of layout relating to character reordering and facets related to character stream rendering; and displaying by a computer the reordered string of characters to a human interface device. 2. The computer-implemented method as set forth in claim 1 wherein the reordering is performed in at least in part in a functional programming language. | 0.825342 |
3. A method of detecting errors in the bits of a data word containing up to N-1 bits in error where N is the number of bits in said data word comprising the steps of generating a unique syndrome word for each one of a plurality of possible error patterns in said data word, wherein said generating step comprises the steps of: generating a first syndrome word corresponding to the location of the bits in error in said data word; changing the relative significance of the bits in said data word; generating a succeeding syndrome word corresponding to the bits in error in said data word after each such change in significance; and combining said first and second succeeding syndrome words in a predetermined manner. | 3. A method of detecting errors in the bits of a data word containing up to N-1 bits in error where N is the number of bits in said data word comprising the steps of generating a unique syndrome word for each one of a plurality of possible error patterns in said data word, wherein said generating step comprises the steps of: generating a first syndrome word corresponding to the location of the bits in error in said data word; changing the relative significance of the bits in said data word; generating a succeeding syndrome word corresponding to the bits in error in said data word after each such change in significance; and combining said first and second succeeding syndrome words in a predetermined manner. 5. A method according to claim 3 wherein said significance changing step comprises the step of rotating said data word. | 0.803922 |
7. An object identification system, comprising: an object detection module configured to detect target-class object occurrences and related-class object occurrences in each video shot of an input video; a hint information generation module configured to generate hint information including a small subset of frames representing the input video; an object tracking and recognition module configured to perform object tracking and recognition based on the hint information and to combine tracking and recognition results; and an output module configured to output labeled objects based on the combined tracking and recognition results; wherein the hint information generation module is further configured to: apply a video summarization approach; apply k-means clustering algorithms using identify-sensitive features on all detected objects; and perform initial object identification using a knowledge database wherein the hint information possesses a desired identity coverage, a desired local representation, a desired pose variation coverage and a desired illumination variation coverage. | 7. An object identification system, comprising: an object detection module configured to detect target-class object occurrences and related-class object occurrences in each video shot of an input video; a hint information generation module configured to generate hint information including a small subset of frames representing the input video; an object tracking and recognition module configured to perform object tracking and recognition based on the hint information and to combine tracking and recognition results; and an output module configured to output labeled objects based on the combined tracking and recognition results; wherein the hint information generation module is further configured to: apply a video summarization approach; apply k-means clustering algorithms using identify-sensitive features on all detected objects; and perform initial object identification using a knowledge database wherein the hint information possesses a desired identity coverage, a desired local representation, a desired pose variation coverage and a desired illumination variation coverage. 9. The system according to claim 7 , wherein: the k-means clustering algorithms are applied using identify-sensitive features on all detected objects to select the frames which contain the k centroid objects, where k equals to a total number of objects need to be identified in the video. | 0.851633 |
11. The method of claim 5 , in which the plurality of image characteristics comprise a fifth image characteristic representing a classification of similarly shaped letters in the image. | 11. The method of claim 5 , in which the plurality of image characteristics comprise a fifth image characteristic representing a classification of similarly shaped letters in the image. 12. The method of claim 11 , in which the classification of similarly shaped letters in the image is in a group consisting of ascending characters, descending characters, mean height characters, dotted characters, majuscule letters, miniscule letters, upper case letters and lower case letters. | 0.893966 |
1. A method for presenting a search result in response to a current search term, comprising: determining a pre-established first model corresponding to preselected user information and including historical user search data; identifying a selected historical search term in the historical user search data that corresponds with the current search term; identifying a selected historical selection result in the historical user search data that corresponds with the identified historical search term; determining the search result based upon the identified historical selection result, said determining the search result comprising determining an online recommendation result based upon the identified historical selection result; processing the online recommendation result to generate a generated recommendation result; and presenting the generated recommendation result, wherein said processing comprises: calculating a literal association degree between the online recommendation result and the current search term according to a logistic regression process such that the online recommendation result has the literal association degree that is greater than a preselected third threshold value; calculating a semantic association degree between the online recommendation result and the current search term according to a gradient boost decision tree such that the online recommendation result has the semantic association degree that is greater than a preselected fourth threshold value; or a combination thereof. | 1. A method for presenting a search result in response to a current search term, comprising: determining a pre-established first model corresponding to preselected user information and including historical user search data; identifying a selected historical search term in the historical user search data that corresponds with the current search term; identifying a selected historical selection result in the historical user search data that corresponds with the identified historical search term; determining the search result based upon the identified historical selection result, said determining the search result comprising determining an online recommendation result based upon the identified historical selection result; processing the online recommendation result to generate a generated recommendation result; and presenting the generated recommendation result, wherein said processing comprises: calculating a literal association degree between the online recommendation result and the current search term according to a logistic regression process such that the online recommendation result has the literal association degree that is greater than a preselected third threshold value; calculating a semantic association degree between the online recommendation result and the current search term according to a gradient boost decision tree such that the online recommendation result has the semantic association degree that is greater than a preselected fourth threshold value; or a combination thereof. 6. The method of claim 1 , wherein said determining the online recommendation result comprises: searching in a pre-established online recommendation result repository according to the historical selection result; and acquiring the online recommendation result based upon said searching. | 0.62358 |
2. A system comprising: one or more sensors configured to sense one or more physical characteristics of an authoring user, wherein said one or more sensors configured to sense one or more physical characteristics of an authoring user, comprises: a functional near infrared (fNIR) device; an acquisition module configured to acquire data indicative of an inferred mental state of the authoring user based, at least in part, on the one or more physical characteristics sensed by the one or more sensors; and an association module configured to associate the data indicative of the inferred mental state of the authoring user with an electronic message. | 2. A system comprising: one or more sensors configured to sense one or more physical characteristics of an authoring user, wherein said one or more sensors configured to sense one or more physical characteristics of an authoring user, comprises: a functional near infrared (fNIR) device; an acquisition module configured to acquire data indicative of an inferred mental state of the authoring user based, at least in part, on the one or more physical characteristics sensed by the one or more sensors; and an association module configured to associate the data indicative of the inferred mental state of the authoring user with an electronic message. 28. The system of claim 2 , further comprising: a user interface. | 0.665123 |
1. A method for replacing an at least one symbol in a first image, the method comprising: obtaining the first image comprising a plurality of pixels representing the at least one symbol and a plurality of pixels representing a background area; defining a first and a second boundary in the first image, wherein the first and the second boundaries are each a path comprising a plurality of pixels that minimizes a cost associated with change in color for the path and each of the paths are positioned on opposite sides of the at least one symbol; generating a plurality of pixels representing an at least one translated symbol of the at least one symbol; generating a plurality of pixels representing an augmented version of the background area, by interpolating a plurality of background pixel values between the first and the second boundaries; and constructing a second image comprising the plurality of pixels representing the at least one translated symbol and the plurality of pixels representing the augmented version of the background area. | 1. A method for replacing an at least one symbol in a first image, the method comprising: obtaining the first image comprising a plurality of pixels representing the at least one symbol and a plurality of pixels representing a background area; defining a first and a second boundary in the first image, wherein the first and the second boundaries are each a path comprising a plurality of pixels that minimizes a cost associated with change in color for the path and each of the paths are positioned on opposite sides of the at least one symbol; generating a plurality of pixels representing an at least one translated symbol of the at least one symbol; generating a plurality of pixels representing an augmented version of the background area, by interpolating a plurality of background pixel values between the first and the second boundaries; and constructing a second image comprising the plurality of pixels representing the at least one translated symbol and the plurality of pixels representing the augmented version of the background area. 2. The method of claim 1 , wherein each of the first and second boundaries is defined as a string of pixels along one side of the at least one symbol. | 0.915737 |
10. The method of operating a common point authoring system of claim 7 wherein said step of converting comprises: maintaining in a read-only mode, a set of data that defines an informational object, said set of data comprising a plurality of said unique identifiers that correspond to a selected set of said plurality of data elements. | 10. The method of operating a common point authoring system of claim 7 wherein said step of converting comprises: maintaining in a read-only mode, a set of data that defines an informational object, said set of data comprising a plurality of said unique identifiers that correspond to a selected set of said plurality of data elements. 11. The method of operating a common point authoring system of claim 10 wherein said authorized authoring member creates an informational object, said step of converting further comprises: associating said unique identifier assigned to said created informational object with said unique identifiers that correspond to a selected set of said plurality of data elements. | 0.871024 |
10. The method of claim 9 wherein naïve assumption of conditional independence of attributes given the value of attribute i is: P ′ ( x 1 , … , x n ❘ X x i ( i ) ) = ∏ j = 1 n P ′ ( x j ❘ x i ) , where conditional higher-order probability mass function P′(x j |x i ) is estimated by P ′ ( x j = 1 ❘ x i ) = φ ( j , X x i ( i ) ) Φ ( X x i ( i ) ) since P′(x j =0|x i )=1−P′(x j =1|x i ), and the proposed transform is a non-linear mapping Z=(z 1 (x), . . . , z n (x)): {0,1} n → n , from a n-dimensional boolean space X to a n-dimensional real space Z, where function Z maps each boolean attribute i to the real domain by a non-linear function z i (x 1 , . . . , x n ):{0,1}→ n , and mapping functions z i are defined over space of higher-order paths as z i ( x 1 , … , x n ) = P ′ ( x i = 1 ❘ x 1 , … , x n ) P ′ ( x i = 0 ❘ x 1 , … , x n ) = ∏ j = 1 n P ′ ( x j ❘ x i = 1 ) P ′ ( x j ❘ x i = 0 ) P ′ ( X 1 ( i ) ) P ′ ( X 0 ( i ) ) . | 10. The method of claim 9 wherein naïve assumption of conditional independence of attributes given the value of attribute i is: P ′ ( x 1 , … , x n ❘ X x i ( i ) ) = ∏ j = 1 n P ′ ( x j ❘ x i ) , where conditional higher-order probability mass function P′(x j |x i ) is estimated by P ′ ( x j = 1 ❘ x i ) = φ ( j , X x i ( i ) ) Φ ( X x i ( i ) ) since P′(x j =0|x i )=1−P′(x j =1|x i ), and the proposed transform is a non-linear mapping Z=(z 1 (x), . . . , z n (x)): {0,1} n → n , from a n-dimensional boolean space X to a n-dimensional real space Z, where function Z maps each boolean attribute i to the real domain by a non-linear function z i (x 1 , . . . , x n ):{0,1}→ n , and mapping functions z i are defined over space of higher-order paths as z i ( x 1 , … , x n ) = P ′ ( x i = 1 ❘ x 1 , … , x n ) P ′ ( x i = 0 ❘ x 1 , … , x n ) = ∏ j = 1 n P ′ ( x j ❘ x i = 1 ) P ′ ( x j ❘ x i = 0 ) P ′ ( X 1 ( i ) ) P ′ ( X 0 ( i ) ) . 11. The method of claim 10 wherein a log transformation of mapping functions is defined as: log z i ( x 1 , … , x n ) = ∑ j = 1 n P ′ ( x j ❘ x i = 1 ) P ′ ( x j ❘ x i = 0 ) + log P ′ ( X 1 ( i ) ) P ′ ( X 0 ( i ) ) . | 0.772727 |
5. The method of claim 1 , wherein the outputting includes displaying the accessed data in a first view as documents with widgets presenting related data in views having alternative presentation formats based on the respective classifications and relationships of different data elements of the accessed data. | 5. The method of claim 1 , wherein the outputting includes displaying the accessed data in a first view as documents with widgets presenting related data in views having alternative presentation formats based on the respective classifications and relationships of different data elements of the accessed data. 7. The method of claim 5 , wherein the displaying includes manipulating a graphical visualization scheme comprising nodes. | 0.951426 |
1. A method of tokenization, comprising: accessing a string of characters; accessing a first token table and a second token table, each of the first token table and the second token table mapping each of a set of input values to a different token value, the first token table different than the second token table; replacing, by a processor, a first substring of the string of characters with a first token value mapped to a value of the first sub string of characters by the first token table to form a first intermediate string of characters; replacing a second substring of the intermediate string of characters with a second token value mapped to a value of the second sub string of characters by the second token table to form a second intermediate string of characters; and combining the second intermediate string of characters with metadata describing the tokenization to form a tokenized string of characters. | 1. A method of tokenization, comprising: accessing a string of characters; accessing a first token table and a second token table, each of the first token table and the second token table mapping each of a set of input values to a different token value, the first token table different than the second token table; replacing, by a processor, a first substring of the string of characters with a first token value mapped to a value of the first sub string of characters by the first token table to form a first intermediate string of characters; replacing a second substring of the intermediate string of characters with a second token value mapped to a value of the second sub string of characters by the second token table to form a second intermediate string of characters; and combining the second intermediate string of characters with metadata describing the tokenization to form a tokenized string of characters. 5. The method of claim 1 , wherein the second substring comprises at least one character not replaced by the first token. | 0.769368 |
6. The method of claim 1, wherein evaluating the recognition results comprises evaluating accuracy of the recognition results. | 6. The method of claim 1, wherein evaluating the recognition results comprises evaluating accuracy of the recognition results. 7. The method of claim 6, wherein the recognition results comprise an ordered list of hypotheses about contents of the speech sample, and wherein evaluating the recognition results comprises reordering the list as a result of the evaluated accuracy of each hypothesis. | 0.888693 |
38. A computer implemented method, the method comprising: repetitively conducting probabilistic percolation crawling at desired time intervals to generate informational network neighborhoods at the desired time intervals, the informational network neighborhood including a community of network nodes linked by referenced links, wherein conducting probabilistic percolation crawling comprises following the one or more reference links in and out of the one or more web pages to one or more neighboring nodes probablistically, wherein performing percolation crawling further comprises randomly selecting reference links in and out of the web page and in and out of the one or more neighboring nodes, wherein selected reference out-links are added to the network neighborhood when the link satisfies a first probability and selected reference in-links are added to the network neighborhood when the link satisfies a second probability, and wherein the communities of network nodes are determined by iteratively partitioning the network neighborhood, wherein each network node includes at least one concept associated with the community, the community comprising a set of network nodes that are more linked amongst themselves than to network nodes that are not included in the community based on the probabilistic percolation crawling; maintaining a representation of the informational network neighborhoods at the desired time intervals in a memory; and identifying changes in the informational network neighborhood by comparing at least two of the informational network neighborhoods maintained in the memory. | 38. A computer implemented method, the method comprising: repetitively conducting probabilistic percolation crawling at desired time intervals to generate informational network neighborhoods at the desired time intervals, the informational network neighborhood including a community of network nodes linked by referenced links, wherein conducting probabilistic percolation crawling comprises following the one or more reference links in and out of the one or more web pages to one or more neighboring nodes probablistically, wherein performing percolation crawling further comprises randomly selecting reference links in and out of the web page and in and out of the one or more neighboring nodes, wherein selected reference out-links are added to the network neighborhood when the link satisfies a first probability and selected reference in-links are added to the network neighborhood when the link satisfies a second probability, and wherein the communities of network nodes are determined by iteratively partitioning the network neighborhood, wherein each network node includes at least one concept associated with the community, the community comprising a set of network nodes that are more linked amongst themselves than to network nodes that are not included in the community based on the probabilistic percolation crawling; maintaining a representation of the informational network neighborhoods at the desired time intervals in a memory; and identifying changes in the informational network neighborhood by comparing at least two of the informational network neighborhoods maintained in the memory. 40. The method of claim 38 wherein the changes represent network traffic directed to the community. | 0.590631 |
40. An imaging system, comprise: an imaging device having an optical mask and being configured for capturing an image of a scene, wherein said optical mask is selected to optically decompose said image into a plurality of channels, each being characterized by a different depth-dependence of a spatial frequency response of said imaging device; a non-transitory computer readable medium storing an in-focus dictionary defined over a plurality of dictionary atoms, and an out-of-focus dictionary defined over a plurality of sets of dictionary atoms, each set corresponding to a different out-of-focus condition, wherein said out-of-focus dictionary comprises a plurality of sub-dictionaries, each being characterized by a defocus parameter, and wherein different sub-dictionaries correspond to different values of said defocus parameter; and a digital image processor configured for accessing said computer readable medium, for computing and storing in a memory at least one sparse representation of said decomposed image over said dictionaries, for generating a processed image using said sparse representation, and for displaying said processed image on a display device. | 40. An imaging system, comprise: an imaging device having an optical mask and being configured for capturing an image of a scene, wherein said optical mask is selected to optically decompose said image into a plurality of channels, each being characterized by a different depth-dependence of a spatial frequency response of said imaging device; a non-transitory computer readable medium storing an in-focus dictionary defined over a plurality of dictionary atoms, and an out-of-focus dictionary defined over a plurality of sets of dictionary atoms, each set corresponding to a different out-of-focus condition, wherein said out-of-focus dictionary comprises a plurality of sub-dictionaries, each being characterized by a defocus parameter, and wherein different sub-dictionaries correspond to different values of said defocus parameter; and a digital image processor configured for accessing said computer readable medium, for computing and storing in a memory at least one sparse representation of said decomposed image over said dictionaries, for generating a processed image using said sparse representation, and for displaying said processed image on a display device. 48. The imaging system according to claim 40 , wherein a number of sub-dictionaries in said out-of-focus dictionary is selected from the group consisting of at least three sub-dictionaries, at least four sub-dictionaries, at least five sub-dictionaries, at least six sub-dictionaries and at least seven sub-dictionaries. | 0.5 |
15. The computer-readable medium of claim 14, wherein the corresponding comparator program module outputs a replacement indicator to indicate that the replacement candidate string was identified. | 15. The computer-readable medium of claim 14, wherein the corresponding comparator program module outputs a replacement indicator to indicate that the replacement candidate string was identified. 16. The computer-readable medium of claim 15, wherein the arbitrator program module has further computer-executable instructions comprising: receiving a replacement indicator from each of the comparator program modules; if the replacement indicators satisfy a selection criterion then selecting one of the replacement candidate strings as the replacement string. | 0.893673 |
12. A system, comprising a server computer communicatively coupled to a network and comprising at least one processor executing computer-executable instructions within a memory that, when executed, cause the system to: receive, via a first graphical user interface (GUI) for a multi-lingual domain name search engine displayed on a client computer, a domain name search string; tokenize the domain name search string; identify a search string token within the domain name search string as a concept seed; execute a first database command to create a data record storing the search string token in association with a concept id; execute a crawl of: a domain name search log, or at least one domain name system (DNS) zone file; tokenize at least one domain name text string within the domain name search log or the at least one DNS zone file; identify, within the at least one domain name text string, at least one synonym or translation of the search string token; execute a second database command to create at least one data record storing: the at least one synonym or translation of the search string token; the concept id; and at least one language associated with the at least one synonym or translation; identify, based on the search string token in the domain name search string, at least one concept comprising a collection of the at least one data record sharing the concept id; generate a second GUI including a displayed list recommending at least one available domain name comprising the at least one concept in the at least one language, the displayed list being ordered according to a frequency of use of the at least one concept; and transmit the second GUI to the client computer for display. | 12. A system, comprising a server computer communicatively coupled to a network and comprising at least one processor executing computer-executable instructions within a memory that, when executed, cause the system to: receive, via a first graphical user interface (GUI) for a multi-lingual domain name search engine displayed on a client computer, a domain name search string; tokenize the domain name search string; identify a search string token within the domain name search string as a concept seed; execute a first database command to create a data record storing the search string token in association with a concept id; execute a crawl of: a domain name search log, or at least one domain name system (DNS) zone file; tokenize at least one domain name text string within the domain name search log or the at least one DNS zone file; identify, within the at least one domain name text string, at least one synonym or translation of the search string token; execute a second database command to create at least one data record storing: the at least one synonym or translation of the search string token; the concept id; and at least one language associated with the at least one synonym or translation; identify, based on the search string token in the domain name search string, at least one concept comprising a collection of the at least one data record sharing the concept id; generate a second GUI including a displayed list recommending at least one available domain name comprising the at least one concept in the at least one language, the displayed list being ordered according to a frequency of use of the at least one concept; and transmit the second GUI to the client computer for display. 15. The system of claim 12 , wherein the server computer is further configured to: determine a co-occurrence frequency of the at least one concept from: at least one user session; or at least one domain name zone file; and identify the at least one available domain name according to a concept dictionary using the co-occurrence frequency of the at least one concept. | 0.575252 |
7. The method of claim 1 , wherein the first affinity coefficient is a positive or negative numerical value. | 7. The method of claim 1 , wherein the first affinity coefficient is a positive or negative numerical value. 8. The method of claim 7 , wherein the natural-language processing utilizes a dictionary comprising crowd-sourced adjectives or objects. | 0.96138 |
9. In a speech recognition system, a computerized method used in determining Markov model sequences for words in a vocabulary based on multiple utterances of each word, the method comprising the steps of: (a) generating, from an acoustic processor which assigns one of an alphabet of speech-type labels to each successive interval of speech, a respective string of labels for each utterance of a subject word; (b) storing the respective strings in computer memory; and (c) partitioning the generated strings for each utterance of the subject word into successive word segments; wherein step (c) includes the steps of: (d) computing and storing arc probabilities and label output probabilities for each of a set of Markov models, wherein each Markov model in the set corresponds to a respective label; (e) retrieving from storage the generated string corresponding to a prototype utterance for a subject word; (f) selecting the one Markov model after another in sequence which corresponds to the respective one label after another generated by the acoustic processor for the prototype utterance; (g) aligning each Markov model for the prototype utterance against labels generated for another utterance of the subject word, wherein the successive Markov models for the prototype utterance are aligned against successive substrings for said other utterance based on the stored probabilities; and (h) repeating step (g) for each utterance other than the prototype utterance; the ith label of the prototype string and the ith substring of each other string representing the ith segment of each respective utterance. | 9. In a speech recognition system, a computerized method used in determining Markov model sequences for words in a vocabulary based on multiple utterances of each word, the method comprising the steps of: (a) generating, from an acoustic processor which assigns one of an alphabet of speech-type labels to each successive interval of speech, a respective string of labels for each utterance of a subject word; (b) storing the respective strings in computer memory; and (c) partitioning the generated strings for each utterance of the subject word into successive word segments; wherein step (c) includes the steps of: (d) computing and storing arc probabilities and label output probabilities for each of a set of Markov models, wherein each Markov model in the set corresponds to a respective label; (e) retrieving from storage the generated string corresponding to a prototype utterance for a subject word; (f) selecting the one Markov model after another in sequence which corresponds to the respective one label after another generated by the acoustic processor for the prototype utterance; (g) aligning each Markov model for the prototype utterance against labels generated for another utterance of the subject word, wherein the successive Markov models for the prototype utterance are aligned against successive substrings for said other utterance based on the stored probabilities; and (h) repeating step (g) for each utterance other than the prototype utterance; the ith label of the prototype string and the ith substring of each other string representing the ith segment of each respective utterance. 14. The method of claim 9 wherein step (d) includes the steps of: (u) selecting one of the strings for a given word and constructing a preliminary baseform of the given word formed of the sequence of fenemic Markov models corresponding to the labels in the selected string; (v) computing arc probabilities and label output probabilities for fenemic Markov models based on the labels generated for all strings other than the selected one string of step (u). | 0.753689 |
64. The method of claim 46 , wherein the claim chart information contains at least one of preamble text information and text information for at least one claim limitation, and further wherein the claim chart information contains information regarding whether an identified product or service contains features of at least one claim limitation, literally or under the Doctrine of Equivalents. | 64. The method of claim 46 , wherein the claim chart information contains at least one of preamble text information and text information for at least one claim limitation, and further wherein the claim chart information contains information regarding whether an identified product or service contains features of at least one claim limitation, literally or under the Doctrine of Equivalents. 65. The method of claim 64 , further comprising: causing the at least one term in the claim chart information to be at least one of highlighted, underlined, and emboldened. | 0.831049 |
1. An information retrieval system, comprising: a plurality of files stored in memory, each defining a different hierarchical relationships of terms describing an organizational framework for information; a user interface which permits a user to select a level within each of at least two of the different hierarchical relationships; a search query generator responsive to the selection of the level within each of the at least two different hierarchical relationships to construct individual search queries of terms that are based upon the selected levels, and combine the individual search queries of terms to form a search query that is provided to a search engine, wherein the search engine searches a source of information to locate documents which correspond to the search query; and a display which displays information about the located documents to the user. | 1. An information retrieval system, comprising: a plurality of files stored in memory, each defining a different hierarchical relationships of terms describing an organizational framework for information; a user interface which permits a user to select a level within each of at least two of the different hierarchical relationships; a search query generator responsive to the selection of the level within each of the at least two different hierarchical relationships to construct individual search queries of terms that are based upon the selected levels, and combine the individual search queries of terms to form a search query that is provided to a search engine, wherein the search engine searches a source of information to locate documents which correspond to the search query; and a display which displays information about the located documents to the user. 4. The information retrieval system of claim 1 , wherein the user interface permits the user to select one or more sources of information to be searched. | 0.606814 |
1. A method comprising: iterating the steps of: receiving at a first system via a network interface from a remote system at least one term for use in searching of information, the at least one term for use in determining documents relating to the at least one term; providing search results within a search result set from the first system to the remote system, the search results including an indication of at least some of the documents relating to the at least one term, the search result set defining a resulting search space; providing at least one further term, the at least one further term provided for reducing the search results in the search result set by approximately 40%-60%, the at least one further term provided in dependence upon the at least one term and terms relating to documents within the search result set, the at least one further term relating to the resulting search space; wherein the resulting search space is reduced over an earlier search space by approximately two to the power of a number of terms selected since the earlier search space. | 1. A method comprising: iterating the steps of: receiving at a first system via a network interface from a remote system at least one term for use in searching of information, the at least one term for use in determining documents relating to the at least one term; providing search results within a search result set from the first system to the remote system, the search results including an indication of at least some of the documents relating to the at least one term, the search result set defining a resulting search space; providing at least one further term, the at least one further term provided for reducing the search results in the search result set by approximately 40%-60%, the at least one further term provided in dependence upon the at least one term and terms relating to documents within the search result set, the at least one further term relating to the resulting search space; wherein the resulting search space is reduced over an earlier search space by approximately two to the power of a number of terms selected since the earlier search space. 2. A method according to claim 1 wherein the at least one further term is for reducing the search result set to a proportion thereof, the proportion within predetermined limits. | 0.713381 |
13. A computer-implemented simulation tool system for simulating a run-time user interaction with a voice application running on an application server, said tool system comprising a processor configured to: load a user simulation script; and processing the user simulation script to generate both a simulated input to the voice application and a simulated output from the voice application; wherein the simulation tool and user simulation script replace actual inputs provided by a live user, actual outputs provided by the voice application and all speech technologies needed when the voice application is not in the simulation environment, and wherein all actual interactions between the user and the voice application are represented by scripted text-equivalents that simulate both the content and execution time of such interactions. | 13. A computer-implemented simulation tool system for simulating a run-time user interaction with a voice application running on an application server, said tool system comprising a processor configured to: load a user simulation script; and processing the user simulation script to generate both a simulated input to the voice application and a simulated output from the voice application; wherein the simulation tool and user simulation script replace actual inputs provided by a live user, actual outputs provided by the voice application and all speech technologies needed when the voice application is not in the simulation environment, and wherein all actual interactions between the user and the voice application are represented by scripted text-equivalents that simulate both the content and execution time of such interactions. 16. The computer-implemented simulation tool system of claim 13 , wherein the simulated output simulates an output from a text to speech engine in response to the simulated input. | 0.782609 |
8. The apparatus of claim 7 , wherein the range-dependent match function comprises a procedure which is configured to use a counter to count a number of keyword instances identified in order and an array of offsets to keep track of position offsets for the identified keyword instances. | 8. The apparatus of claim 7 , wherein the range-dependent match function comprises a procedure which is configured to use a counter to count a number of keyword instances identified in order and an array of offsets to keep track of position offsets for the identified keyword instances. 9. The apparatus of claim 8 , wherein the procedure is further configured to increment the counter if a keyword identifier for a current keyword instance is equal to the counter's value plus one. | 0.783482 |
1. A method of processing results of a recognition by an automatic speech recognition (ASR) system on a speech input, the results comprising a first recognition result identified by the ASR system as most likely to be a correct recognition result for the speech input, the results further comprising at least one alternative recognition result identified by the ASR system, the method comprising: determining whether the first recognition result includes a member of a set of words or phrases, each member of the set comprising a word or phrase and being associated with at least one other member of the set, and whether the at least one alternative recognition result includes any of the at least one other member associated with the member in the set, wherein the first recognition result includes at least one first word or phrase other than the member of the set of words or phrases and each of the at least one alternative recognition result includes at least one second word or phrase other than the at least one other member, the at least one first word or phrase and the at least one second word or phrase being the same or different; and in response to determining that the first recognition result includes the member of the set of words or phrases and that the at least one alternative recognition result includes any of the at least one other member associated with the member in the set, triggering an alert. | 1. A method of processing results of a recognition by an automatic speech recognition (ASR) system on a speech input, the results comprising a first recognition result identified by the ASR system as most likely to be a correct recognition result for the speech input, the results further comprising at least one alternative recognition result identified by the ASR system, the method comprising: determining whether the first recognition result includes a member of a set of words or phrases, each member of the set comprising a word or phrase and being associated with at least one other member of the set, and whether the at least one alternative recognition result includes any of the at least one other member associated with the member in the set, wherein the first recognition result includes at least one first word or phrase other than the member of the set of words or phrases and each of the at least one alternative recognition result includes at least one second word or phrase other than the at least one other member, the at least one first word or phrase and the at least one second word or phrase being the same or different; and in response to determining that the first recognition result includes the member of the set of words or phrases and that the at least one alternative recognition result includes any of the at least one other member associated with the member in the set, triggering an alert. 5. The method of claim 1 , wherein: a first member of the set is associated with a second member of the set with which the first member is acoustically-confusable and that, when substituted for the first member in a recognition result, changes a meaning of the recognition result; and the determining whether the first recognition result includes a member of the set and whether the at least one alternative recognition result includes any of the at least one other member comprises determining whether the first recognition result includes the first member and whether the at least one alternative recognition result includes the second member. | 0.571317 |
1. A device comprising: at least one processor; a display accessible to the at least one processor; and storage accessible to the at least one processor and bearing instructions executable by the at least one processor to: store a first phrase from a sent message for presentation of the first phrase again during a subsequent composition of a second message, wherein the first phrase from the sent message that is stored comprises a variable when stored that will be replaced in the second message with at least one character for a particular recipient during composition of the second message; and identify the first phrase for presentation during composition of the second message; and present, on the display and during composition of the second message, the first phrase. | 1. A device comprising: at least one processor; a display accessible to the at least one processor; and storage accessible to the at least one processor and bearing instructions executable by the at least one processor to: store a first phrase from a sent message for presentation of the first phrase again during a subsequent composition of a second message, wherein the first phrase from the sent message that is stored comprises a variable when stored that will be replaced in the second message with at least one character for a particular recipient during composition of the second message; and identify the first phrase for presentation during composition of the second message; and present, on the display and during composition of the second message, the first phrase. 16. The device of claim 1 , wherein the instructions are executable by the at least one processor to: present, during composition of the second message, the variable in a text input field that is presented on the display, wherein the variable is replaceable by a user during composition of the second message. | 0.511411 |
8. One or more computer-readable non-transitory storage media in one or more computing systems, the media embodying logic that is operable when executed to: provide social content for display in a mobile application running on a mobile device of a user of a social-networking system, the mobile application configured to display an interface for selecting media content to view on a display device of the user that is separate from the mobile device, wherein: the social content is determined from a social graph of the social-networking system, the social graph comprising a plurality of nodes and edges connecting the nodes, the nodes and edges comprising: device nodes that each correspond to a respective mobile device; user nodes that are each associated with a particular user of the social-networking system; concept nodes that are each associated with particular media content; and a plurality of ownership edges connecting the user nodes and the device nodes, each particular ownership edge indicating that a particular user corresponding to a particular user node owns a particular device corresponding to a particular device node; the mobile device of the user is determined by analyzing the device nodes and the ownership edges of the social graph; and the interface of the mobile application is configured to display, proximate to each particular media content that is displayed for selection, particular social content that is connected to both the user and the particular media content in the social graph, the particular social content comprising: an identification of at least one friend of the user who has previously liked the particular media content; and an identification of at least one friend of the user who is currently watching the particular media content; receive an indication from the mobile application that an option to view particular media content has been selected by the user using the interface of the mobile application running on the mobile device; and in response to the selection by the user on the mobile device, provide one or more instructions to display the selected particular media content on the display device of the user that is separate from the mobile device. | 8. One or more computer-readable non-transitory storage media in one or more computing systems, the media embodying logic that is operable when executed to: provide social content for display in a mobile application running on a mobile device of a user of a social-networking system, the mobile application configured to display an interface for selecting media content to view on a display device of the user that is separate from the mobile device, wherein: the social content is determined from a social graph of the social-networking system, the social graph comprising a plurality of nodes and edges connecting the nodes, the nodes and edges comprising: device nodes that each correspond to a respective mobile device; user nodes that are each associated with a particular user of the social-networking system; concept nodes that are each associated with particular media content; and a plurality of ownership edges connecting the user nodes and the device nodes, each particular ownership edge indicating that a particular user corresponding to a particular user node owns a particular device corresponding to a particular device node; the mobile device of the user is determined by analyzing the device nodes and the ownership edges of the social graph; and the interface of the mobile application is configured to display, proximate to each particular media content that is displayed for selection, particular social content that is connected to both the user and the particular media content in the social graph, the particular social content comprising: an identification of at least one friend of the user who has previously liked the particular media content; and an identification of at least one friend of the user who is currently watching the particular media content; receive an indication from the mobile application that an option to view particular media content has been selected by the user using the interface of the mobile application running on the mobile device; and in response to the selection by the user on the mobile device, provide one or more instructions to display the selected particular media content on the display device of the user that is separate from the mobile device. 13. The media of claim 8 , wherein providing the one or more instructions to display the selected particular media content on the display device comprises providing, by the one or more computer systems of the social-networking system, one or more infrared (IR) instructions to a content source, the content source comprising one of: a set-top box (STB); a digital video recorder (DVR); a gaming console; and a device configured to provide access to media content from an over-the-top (OTT) content provider. | 0.5 |
1. A computer-implemented method for inserting content into a model using a design tool, the method comprising: displaying, by a computer, a list of user-selectable construct icons on a user interface screen of the design tool, the construct icons associated with constructs; determining, by the computer, a construct icon selected by the user; displaying, by the computer, a plurality of computing environment interface screens able to receive the selected construct icon, the plurality of computing environment interface screens being simultaneously displayed on the user interface screen of the design tool and associated with a plurality of computing environments including at least one textual computing environment and at least one graphical computing environment; receiving, by the computer, a selection of a selected computing environment interface screen from the plurality of computing environment interface screens into which the construct associated with the user selected construct icon is placed; identifying, by the computer, a selected computing environment from the plurality of computing environments, wherein the selected computing environment is associated with the selected computing environment interface screen; determining, by the computer, a position of the placed construct in the selected computing environment; selecting, by the computer, a template based on the selected computing environment and the placed construct; and inserting, by the computer, the selected template into the selected computing environment at the determined position in the selected computing environment. | 1. A computer-implemented method for inserting content into a model using a design tool, the method comprising: displaying, by a computer, a list of user-selectable construct icons on a user interface screen of the design tool, the construct icons associated with constructs; determining, by the computer, a construct icon selected by the user; displaying, by the computer, a plurality of computing environment interface screens able to receive the selected construct icon, the plurality of computing environment interface screens being simultaneously displayed on the user interface screen of the design tool and associated with a plurality of computing environments including at least one textual computing environment and at least one graphical computing environment; receiving, by the computer, a selection of a selected computing environment interface screen from the plurality of computing environment interface screens into which the construct associated with the user selected construct icon is placed; identifying, by the computer, a selected computing environment from the plurality of computing environments, wherein the selected computing environment is associated with the selected computing environment interface screen; determining, by the computer, a position of the placed construct in the selected computing environment; selecting, by the computer, a template based on the selected computing environment and the placed construct; and inserting, by the computer, the selected template into the selected computing environment at the determined position in the selected computing environment. 4. The method according to claim 1 , further comprising: creating a construct based on a user input; storing the construct in the design tool; creating a second construct icon that corresponds to the created construct; and displaying the second construct icon corresponding to the created construct in the list of constructs on the user interface screen. | 0.648601 |
11. A communication device programmed to render a graphical context menu on a display screen of the communication device in response to receiving a menu request, said communication device comprising: the display screen configured to render the graphical context menu over an application currently being displayed on the display screen; and a microprocessor in signal communication with the display screen, the microprocessor having a menu program associated therewith for controlling operation of said communication device, said menu program configured to: receive a menu request; select, based on a context of the menu request, a subset of menu items from a complete set of menu items available for the context; determine a number of slots for the graphical context menu based on a number of menu items selected in the subset of menu items; compare the number of slots for the graphical context menu to the number of menu items; obtain at least one filler item from the complete set of menu items, the at least one filler item not being included in the subset of menu items; insert the subset of menu items and the at least one filler menu item into selected slots in the graphical context menu; and render the graphical context menu on the display screen with the menu items and the at least one filler menu item. | 11. A communication device programmed to render a graphical context menu on a display screen of the communication device in response to receiving a menu request, said communication device comprising: the display screen configured to render the graphical context menu over an application currently being displayed on the display screen; and a microprocessor in signal communication with the display screen, the microprocessor having a menu program associated therewith for controlling operation of said communication device, said menu program configured to: receive a menu request; select, based on a context of the menu request, a subset of menu items from a complete set of menu items available for the context; determine a number of slots for the graphical context menu based on a number of menu items selected in the subset of menu items; compare the number of slots for the graphical context menu to the number of menu items; obtain at least one filler item from the complete set of menu items, the at least one filler item not being included in the subset of menu items; insert the subset of menu items and the at least one filler menu item into selected slots in the graphical context menu; and render the graphical context menu on the display screen with the menu items and the at least one filler menu item. 18. The communication device of claim 11 wherein selecting the subset of menu items further comprises determining the context of the menu request and identifying menu items associated with the context of the menu request. | 0.527926 |
15. The method of claim 3 wherein the angle set data structure further comprises data for a plurality of story angles, the story angle data comprising, for each story angle (1) an identifier for that story angle, and (2) data representative of at least one applicability condition for that story angle. | 15. The method of claim 3 wherein the angle set data structure further comprises data for a plurality of story angles, the story angle data comprising, for each story angle (1) an identifier for that story angle, and (2) data representative of at least one applicability condition for that story angle. 19. The method of claim 15 wherein the story angle data of the angle set data structure further comprises, for each story angle, data representative of an importance value for that story angle. | 0.864748 |
3. The method of claim 1 wherein the converting the source code written in the pseudo assembly language further comprises: applying contextual recognition and reconstruction to the source code written in the pseudo assembly language to generate the source code written in the second assembly language, wherein the contextual recognition and reconstruction further comprises determining an objective of a segment of the source code written in the pseudo language; generating a segment of source code written in the second assembly language that achieves the objective of the segment of the source code written in the pseudo assembly language. | 3. The method of claim 1 wherein the converting the source code written in the pseudo assembly language further comprises: applying contextual recognition and reconstruction to the source code written in the pseudo assembly language to generate the source code written in the second assembly language, wherein the contextual recognition and reconstruction further comprises determining an objective of a segment of the source code written in the pseudo language; generating a segment of source code written in the second assembly language that achieves the objective of the segment of the source code written in the pseudo assembly language. 5. The method of claim 3 , wherein the contextual recognition and reconstruction further comprises: identifying a macro label in the source code written in the pseudo assembly language; the method further comprising generating source code where the macro label is replaced with expanded source code written in the second assembly language. | 0.787559 |
1. A computer-implemented method for providing secure credentials for accessing a target resource, which when executed on one or more processors, causes the one or more processors to perform steps of: receiving a connection request to the target resource from an unattended requestor application, the connection request including target resource information identifying the target resource and configuration information necessary to authenticate the requestor application, wherein the configuration information of the requestor application is fingerprint information, which uniquely identifies a node of the requestor application; decoding the request to extract the target resource information and the configuration information required by a credential manager to authenticate the requestor application and to retrieve the secure credentials for accessing the target resource, the credential manager managing and storing credentials for the target resource; securely communicating the extracted information to the credential manager to retrieve credentials; generating a native target resource connection request to the target resource, including the retrieved credentials; and passing the native target resource connection request to a native target resource connectivity component to establish a connection between the requestor application and the target resource. | 1. A computer-implemented method for providing secure credentials for accessing a target resource, which when executed on one or more processors, causes the one or more processors to perform steps of: receiving a connection request to the target resource from an unattended requestor application, the connection request including target resource information identifying the target resource and configuration information necessary to authenticate the requestor application, wherein the configuration information of the requestor application is fingerprint information, which uniquely identifies a node of the requestor application; decoding the request to extract the target resource information and the configuration information required by a credential manager to authenticate the requestor application and to retrieve the secure credentials for accessing the target resource, the credential manager managing and storing credentials for the target resource; securely communicating the extracted information to the credential manager to retrieve credentials; generating a native target resource connection request to the target resource, including the retrieved credentials; and passing the native target resource connection request to a native target resource connectivity component to establish a connection between the requestor application and the target resource. 8. The method of claim 1 , wherein the method implements Open Database Connectivity (ODBC) APIs. | 0.602728 |
1. A method of identifying a name and boundary of a neighborhood based on web documents, the method comprising: extracting, via one or more processors, n-grams appearing in a plurality of web documents and being of less than a threshold word count: obtaining a plurality of web documents, each web document being associated with a respective geographic location, the web documents including user reviews of local businesses associated with the respective geographic locations in the geographic information system, extracting n-grams from each of the web documents; associating, via the one or more processors, the n-grams with geographic locations associated with the web documents from which the n-grams were extracted, including associating each of the n-grams with a respective latitude and longitude coordinate of the web document from which the n-grams were extracted; identifying, via the one or more processors, a neighborhood by identifying a cluster of geographic locations associated with the n-grams, including: filtering from the n-grams stop-words, filtering from the n-grams phrases occurring in the web documents less than a threshold amount, filtering from the n-grams at least some n-grams that do not correspond with a cluster, filtering from the n-grams at least some n-grams that correspond to more than a threshold amount of clusters, filtering from the n-grams at least some n-grams that correspond to more than a threshold amount of geographic locations outside of a cluster; determining, via the one or more processors, a boundary for the neighborhood from the distribution of geographical locations in the cluster, including determining a convex hull of the cluster by identifying geographic locations of vertices of a polygon that contains at least a substantial portion of the cluster; determining, via the one or more processors, a name for the neighborhood from the n-gram, including: designating the n-gram as a candidate name for the geographic area defined by the polygon, identifying one or more candidate names for geographic areas at least partially overlapping the polygon, ranking the candidate names based on an amount of times the name appears in the web documents and the size of the geographic areas at least partially overlapping the polygon, and selecting the highest ranking candidate name as the name; and adding, via the one or more processors, the name and boundary of the neighborhood to a geographic information system, including storing the name in memory in a record that associates the name with the geographic area defined by the boundary. | 1. A method of identifying a name and boundary of a neighborhood based on web documents, the method comprising: extracting, via one or more processors, n-grams appearing in a plurality of web documents and being of less than a threshold word count: obtaining a plurality of web documents, each web document being associated with a respective geographic location, the web documents including user reviews of local businesses associated with the respective geographic locations in the geographic information system, extracting n-grams from each of the web documents; associating, via the one or more processors, the n-grams with geographic locations associated with the web documents from which the n-grams were extracted, including associating each of the n-grams with a respective latitude and longitude coordinate of the web document from which the n-grams were extracted; identifying, via the one or more processors, a neighborhood by identifying a cluster of geographic locations associated with the n-grams, including: filtering from the n-grams stop-words, filtering from the n-grams phrases occurring in the web documents less than a threshold amount, filtering from the n-grams at least some n-grams that do not correspond with a cluster, filtering from the n-grams at least some n-grams that correspond to more than a threshold amount of clusters, filtering from the n-grams at least some n-grams that correspond to more than a threshold amount of geographic locations outside of a cluster; determining, via the one or more processors, a boundary for the neighborhood from the distribution of geographical locations in the cluster, including determining a convex hull of the cluster by identifying geographic locations of vertices of a polygon that contains at least a substantial portion of the cluster; determining, via the one or more processors, a name for the neighborhood from the n-gram, including: designating the n-gram as a candidate name for the geographic area defined by the polygon, identifying one or more candidate names for geographic areas at least partially overlapping the polygon, ranking the candidate names based on an amount of times the name appears in the web documents and the size of the geographic areas at least partially overlapping the polygon, and selecting the highest ranking candidate name as the name; and adding, via the one or more processors, the name and boundary of the neighborhood to a geographic information system, including storing the name in memory in a record that associates the name with the geographic area defined by the boundary. 11. The method of claim 1 , wherein identifying the neighborhood by identifying the cluster of geographic locations associated with the n-gram comprises: determining that the n-gram includes or is modified by a term associated with geographic locations in a language in which the n-gram is expressed. | 0.515831 |
1. A method comprising: accepting a request to a wild card auto completion service including an input term at least partly in a target language and at least partly in an initial language, wherein the initial language and the target language are two different languages, the service employing a syntax to accept the input term including zero to multiple wild card characters in the target language or in the initial language in a first part and a delimiter indicating a second part, the second part comprising the target language or the initial language such that: if the language of the first part includes the initial language, the second part includes the target language and if the language of the first part includes the target language, the second part includes the initial language, and the second part identifying at least one of a context or a domain for the wild card auto completion service; identifying an initial-target language pair for the request; aggregating two or more consecutive homogenous wild card characters, wherein the aggregating comprises replacing the two or more consecutive homogenous wild card characters with a single wild card character of a same type as the homogenous wild card character; parsing the input term to identify a pattern of the input term; selecting a matcher corresponding to the pattern of the input term; matching the input term to an entry using the matcher selected; and returning the entry. | 1. A method comprising: accepting a request to a wild card auto completion service including an input term at least partly in a target language and at least partly in an initial language, wherein the initial language and the target language are two different languages, the service employing a syntax to accept the input term including zero to multiple wild card characters in the target language or in the initial language in a first part and a delimiter indicating a second part, the second part comprising the target language or the initial language such that: if the language of the first part includes the initial language, the second part includes the target language and if the language of the first part includes the target language, the second part includes the initial language, and the second part identifying at least one of a context or a domain for the wild card auto completion service; identifying an initial-target language pair for the request; aggregating two or more consecutive homogenous wild card characters, wherein the aggregating comprises replacing the two or more consecutive homogenous wild card characters with a single wild card character of a same type as the homogenous wild card character; parsing the input term to identify a pattern of the input term; selecting a matcher corresponding to the pattern of the input term; matching the input term to an entry using the matcher selected; and returning the entry. 5. A method as recited in claim 1 , wherein matching the input term to the dictionary entry includes: performing a prefix match to identify a sorted list of dictionary entries matching a prefix made up of characters in the input term that precede a first wild card character in the input term; and performing a binary search on the dictionary entries matching the prefix, the dictionary entries corresponding to a number of characters that the at least one wild card character represents. | 0.603728 |
6. The computing apparatus of claim 1 , wherein the VANC engine is further configured to: send the network policy macro to a server via the network interface; receive a response from the server via the network interface; and generate a natural language or haptic feedback for a user. | 6. The computing apparatus of claim 1 , wherein the VANC engine is further configured to: send the network policy macro to a server via the network interface; receive a response from the server via the network interface; and generate a natural language or haptic feedback for a user. 8. The computing apparatus of claim 6 , wherein the response comprises user-requested information encoded within the input text string. | 0.878216 |
2. The method of claim 1 , further comprising: for candidate subsegments having a similarity that does not exceed the threshold similarity, using a model-based statistical machine translation to identify target subsegments corresponding to the input subsegments. | 2. The method of claim 1 , further comprising: for candidate subsegments having a similarity that does not exceed the threshold similarity, using a model-based statistical machine translation to identify target subsegments corresponding to the input subsegments. 3. The method of claim 2 , further comprising: receiving an input adjusting the similarity threshold, where adjusting the similarity threshold results in different sized candidate subsegments. | 0.944821 |
18. The media of claim 17 , the content item browsing history of the user comprising a short term browsing history identifying a content item most recently accessed by the user and a long term browsing history identifying one or more content items accessed by the user within a predefined time window, the system further comprising instructions that when executed by the at least one processor cause the at least one processor to: determine each topic's relevance by: adding, for each of the one or more content items identified in the long term browsing history, a weighted relevance of the topic to the content item to an aggregate relevance of the topic, the weighted relevance being determined using the topic's relevance to the content item and a weighting that is based on a time associated with the content item; combining the topic's aggregate relevance with a relevance of the topic to the content item identified in the short term browsing history to form a combined relevance for the topic. | 18. The media of claim 17 , the content item browsing history of the user comprising a short term browsing history identifying a content item most recently accessed by the user and a long term browsing history identifying one or more content items accessed by the user within a predefined time window, the system further comprising instructions that when executed by the at least one processor cause the at least one processor to: determine each topic's relevance by: adding, for each of the one or more content items identified in the long term browsing history, a weighted relevance of the topic to the content item to an aggregate relevance of the topic, the weighted relevance being determined using the topic's relevance to the content item and a weighting that is based on a time associated with the content item; combining the topic's aggregate relevance with a relevance of the topic to the content item identified in the short term browsing history to form a combined relevance for the topic. 19. The media of claim 18 , further comprising instructions that when executed by the at least one processor cause the at least one processor to: determine, for each topic having a combined relevance, whether the user's interest in the topic is significant by comparing the topic's combined relevance with the user-interest threshold; and for each topic determined to be of significant interest to the user, use the topic to perform a per interest retrieval, the per interest retrieval retrieving content items in the plurality of content items that have a relationship with the topic; group any remaining topics, and perform a group interest retrieval to retrieve content items in the plurality of content items, each content item retrieved via the group interest retrieval having a relationship with all of the grouped topics. | 0.500646 |
21. The method of claim 19 , further comprising overlaying statistical data on the natural interaction forum to measure a participation of the learner in the natural interaction forum to obtain participation numerical data. | 21. The method of claim 19 , further comprising overlaying statistical data on the natural interaction forum to measure a participation of the learner in the natural interaction forum to obtain participation numerical data. 22. The method of claim 21 , further comprising: creating a communication network visualization that graphically visualizes a communication network of the natural interaction forum; and displaying the communication network visualization. | 0.918426 |
1. A system for guiding the progressive development and documentation of user thinking and knowledge about an inquiry based project according to exemplary approaches used by experts, comprising: An interface; A process manager system executable on at least one processor and operable to execute activities comprising: receiving user specification of an initial stage of understanding regarding an arbitrary problem or inquiry based project according to at least one of a plurality of entry or starting points; Providing at least one of an interactive workspace or a suggestion to a user, or both, to facilitate the further development of the understanding regarding an arbitrary problem or inquiry project towards a completion stage; Providing an integrated archetype based model of user understanding regarding the arbitrary problem or inquiry based project, in display or output or both. | 1. A system for guiding the progressive development and documentation of user thinking and knowledge about an inquiry based project according to exemplary approaches used by experts, comprising: An interface; A process manager system executable on at least one processor and operable to execute activities comprising: receiving user specification of an initial stage of understanding regarding an arbitrary problem or inquiry based project according to at least one of a plurality of entry or starting points; Providing at least one of an interactive workspace or a suggestion to a user, or both, to facilitate the further development of the understanding regarding an arbitrary problem or inquiry project towards a completion stage; Providing an integrated archetype based model of user understanding regarding the arbitrary problem or inquiry based project, in display or output or both. 4. The system of claim 1 further comprising that the initial stage of understanding further comprises at least a subset of at least one analysis, knowledge component, information component, data component, model, model project, or project template, or that the entry or starting point further comprises the defining at least a subset of at least one analysis, knowledge component, information component, data component, model, model project or project template, or a combination thereof. | 0.759843 |
15. A system comprising a computer having a processor and a data storage, the processor being programmed to perform the following acts: identifying appearances of a plurality of geographical names in a document; determining one or more frequencies of each geographical name's appearances in the document, the plurality of geographical names including a first geographical name and a second geographical name; assigning one or more positional weights to the first geographical name according to positions of the first geographical name's appearances in the document, the assigning including: identifying one or more position types where the first geographical name appears in the document, assigning a same position type to a first instance of the first geographical name and a first instance of the second geographical name in response to determining that the first instance of the first geographical name and the first instance of the second geographical name are within a same paragraph of the document; and assigning a respective positional weight to each position type where the first geographical name appears in the document; computing a score of the first geographical name based on the one or more frequencies and the one or more positional weights of the first geographical name. | 15. A system comprising a computer having a processor and a data storage, the processor being programmed to perform the following acts: identifying appearances of a plurality of geographical names in a document; determining one or more frequencies of each geographical name's appearances in the document, the plurality of geographical names including a first geographical name and a second geographical name; assigning one or more positional weights to the first geographical name according to positions of the first geographical name's appearances in the document, the assigning including: identifying one or more position types where the first geographical name appears in the document, assigning a same position type to a first instance of the first geographical name and a first instance of the second geographical name in response to determining that the first instance of the first geographical name and the first instance of the second geographical name are within a same paragraph of the document; and assigning a respective positional weight to each position type where the first geographical name appears in the document; computing a score of the first geographical name based on the one or more frequencies and the one or more positional weights of the first geographical name. 25. The system as recited in claim 15 , wherein determining one or more frequencies of the first geographical name's appearances in the document comprises: ignoring an instance of the first geographical name if a news agency name appears in immediate proximity to the first geographical name in the document. | 0.61547 |
15. A computer-implemented data processing method for electronically linking one or more activities to a respective segment of a plurality of segments that make up a piece of multimedia content so that at least two users can create an asynchronous conversation that is linked to the respective segment of the multimedia content, comprising: providing, by an activity management system, a first graphical user interface comprising a first segment display, wherein each segment on the first segment display is associated with a respective segment of the plurality of segments that make up the piece of multimedia content being viewed by a first viewer; receiving at a first time, via the first graphical user interface, a first activity from the first viewer of the multimedia content; electronically linking, by the activity management system, the received first activity to a first segment of the multimedia content; creating a first electronic record that comprises one or more of the first activity, a multimedia identifier for the multimedia content, a first viewer identifier, and a first segment identifier for the first segment, and digitally storing the first electronic record; and presenting, on the first graphical user interface: a first representation of the first activity on the first segment display in association with a point on the first segment display corresponding to the first segment of the multimedia; and a word cloud that indicates a visual display of a graphic density of one or more particular words that appear in the first activity and the one or more activities. | 15. A computer-implemented data processing method for electronically linking one or more activities to a respective segment of a plurality of segments that make up a piece of multimedia content so that at least two users can create an asynchronous conversation that is linked to the respective segment of the multimedia content, comprising: providing, by an activity management system, a first graphical user interface comprising a first segment display, wherein each segment on the first segment display is associated with a respective segment of the plurality of segments that make up the piece of multimedia content being viewed by a first viewer; receiving at a first time, via the first graphical user interface, a first activity from the first viewer of the multimedia content; electronically linking, by the activity management system, the received first activity to a first segment of the multimedia content; creating a first electronic record that comprises one or more of the first activity, a multimedia identifier for the multimedia content, a first viewer identifier, and a first segment identifier for the first segment, and digitally storing the first electronic record; and presenting, on the first graphical user interface: a first representation of the first activity on the first segment display in association with a point on the first segment display corresponding to the first segment of the multimedia; and a word cloud that indicates a visual display of a graphic density of one or more particular words that appear in the first activity and the one or more activities. 16. The computer-implemented data processing method of claim 15 , the method further comprising: receiving a request to filter the one or more activities based at least in part on an activity term; in response to the request, identifying one or more of the one or more activities that comprise the activity term, and displaying, adjacent the word cloud, the one or more of the one or more activities that comprise the activity term in association with an associated particular segment. | 0.70655 |
1. A method for font recommendation, comprising: obtaining a product category; determining, using one or more processors, whether a font recommendation should be made with respect to the product category, comprising: obtaining font information of a frequently used font within a webpage that corresponds to a product within the product category; and determining whether a predetermined correspondence exists between the product category and the frequently used font, including by looking up the product category and the frequently used font in the plurality of predetermined correspondences, wherein the font recommendation is to be made in the event that no predetermined correspondence is determined; in the event that the font recommendation should be made: determining a recommended font that corresponds to the product category, the determination being based at least in part on a plurality of predetermined correspondences, and the plurality of predetermined correspondences indicating associations between a plurality of product categories and a respective plurality of fonts; and outputting information pertaining to the recommended font. | 1. A method for font recommendation, comprising: obtaining a product category; determining, using one or more processors, whether a font recommendation should be made with respect to the product category, comprising: obtaining font information of a frequently used font within a webpage that corresponds to a product within the product category; and determining whether a predetermined correspondence exists between the product category and the frequently used font, including by looking up the product category and the frequently used font in the plurality of predetermined correspondences, wherein the font recommendation is to be made in the event that no predetermined correspondence is determined; in the event that the font recommendation should be made: determining a recommended font that corresponds to the product category, the determination being based at least in part on a plurality of predetermined correspondences, and the plurality of predetermined correspondences indicating associations between a plurality of product categories and a respective plurality of fonts; and outputting information pertaining to the recommended font. 4. The method of claim 1 , wherein determining whether the font recommendation should be made includes: obtaining font information of a frequently used font within a webpage that corresponds to a product in the product category; determining whether a predetermined correspondence exists between the product category and the frequently used font, including by looking up the product category and the frequently used font in the plurality of predetermined correspondences; and determining whether a number of webpages, for which no predetermined correspondence exists between their respective product categories and frequently used fonts, exceeds a threshold, wherein the font recommendation is to be made in the event that the threshold is exceeded. | 0.554675 |
31. The system of claim 29 , wherein the predefined types of entities include physical entities and logical entities. | 31. The system of claim 29 , wherein the predefined types of entities include physical entities and logical entities. 32. The system of claim 31 , wherein the logical entities include logical groups. | 0.971963 |
53. A computer system for resolving ambiguities in date values associated with an attribute of an entity, the computer system comprising: one or more processors; memory; and one or more programs stored in the memory, the one or more programs comprising instructions to: identify a plurality of web documents associated with an attribute of an entity; for each web document in the plurality of web documents, obtain, from the web document, at least two text strings associated with the attribute of the entity; identify one or more date formats for at least two text strings; and assign confidence values to each of the one or more date formats for the at least two text strings based on a number of unknown variables that remain when interpreting the at least one of the one or more at least two text strings using each of the one or more date formats; determine date strings expressed in date formats with highest confidence values for the at least two text strings; and merge subsets of the date strings to obtain a date value for the attribute. | 53. A computer system for resolving ambiguities in date values associated with an attribute of an entity, the computer system comprising: one or more processors; memory; and one or more programs stored in the memory, the one or more programs comprising instructions to: identify a plurality of web documents associated with an attribute of an entity; for each web document in the plurality of web documents, obtain, from the web document, at least two text strings associated with the attribute of the entity; identify one or more date formats for at least two text strings; and assign confidence values to each of the one or more date formats for the at least two text strings based on a number of unknown variables that remain when interpreting the at least one of the one or more at least two text strings using each of the one or more date formats; determine date strings expressed in date formats with highest confidence values for the at least two text strings; and merge subsets of the date strings to obtain a date value for the attribute. 58. The system of claim 53 , wherein the merging of the date formats retains components of the date format for the first text string and components of the date format for the second text string that are not ambiguous. | 0.732164 |
6. A hybrid prediction computer system for processing training data to predict click-through-rates, said hybrid prediction computer system processing training data by: creating a machine learning based model for making a base prediction, said machine learning model constructed using a first set of features in training data; creating a tree-structured statistical table from a second set of features in training data by applying Kalman-filter methods to a tree-structured Markov model to estimate parameters in said tree-structured statistical table; executing said machine learning model using said first set of features from test data; and adding an adjustment factor from said tree-structured statistical table to augment said base prediction from said machine learning based model to predict click-through-rates. | 6. A hybrid prediction computer system for processing training data to predict click-through-rates, said hybrid prediction computer system processing training data by: creating a machine learning based model for making a base prediction, said machine learning model constructed using a first set of features in training data; creating a tree-structured statistical table from a second set of features in training data by applying Kalman-filter methods to a tree-structured Markov model to estimate parameters in said tree-structured statistical table; executing said machine learning model using said first set of features from test data; and adding an adjustment factor from said tree-structured statistical table to augment said base prediction from said machine learning based model to predict click-through-rates. 7. The hybrid prediction computer system as set forth in claim 6 wherein said machine learning based model considers a limited important set of global features. | 0.594743 |
9. The schedule management system according to claim 8 , wherein the reasoning unit performs reasoning using at least one piece of information stored in the database unit to calculate the reasoning weights and updates the reasoning weights to the calculated reasoning weights. | 9. The schedule management system according to claim 8 , wherein the reasoning unit performs reasoning using at least one piece of information stored in the database unit to calculate the reasoning weights and updates the reasoning weights to the calculated reasoning weights. 10. The schedule management system according to claim 9 , wherein the reasoning unit calculates the reasoning weights using a Bayesian network. | 0.892596 |
7. A document layout processing device in which when a hierarchal structure representing logical divisions of a document content is defined as a logical structure and a hierarchal structure representing layout divisions of a document is defined as a layout structure, a structured document containing at least a logical structure of a specific document content and a template specifying constraints for generating a plurality of layout structures means for storing the plurality of layout structures and also for selectively generating a desired layout structure from among the plurality of stored layout structures, and a layout structure corresponding to the logical structure of the specific document content is generated on the basis of the template, the device comprising: intermediate data structure generating means for generating an intermediate data structure which is a hierarchal structure representing simultaneously both template information corresponding to the template and generation history information indicative of a generation history of a layout structure based on the template and also for generating as an initial state of the intermediate data structure a hierarchal structure corresponding to a minimum layout structure common to the plurality of layout structures generatable based on the template; intermediate data structure holding means for holding the intermediate data structure generated by the intermediate data structure generating means; intermediate data structure altering means for altering the intermediate data structure held in the intermediate data structure holding means by referring to the template information and the generation history information held in the intermediate data structure holding means so that the logical structure of the specific document content conforms to the template; and specific layout structure extracting means for extracting a layout structure corresponding to the logical structure of the specific document content from a latest intermediate data structure held in the intermediate data structure holding means. | 7. A document layout processing device in which when a hierarchal structure representing logical divisions of a document content is defined as a logical structure and a hierarchal structure representing layout divisions of a document is defined as a layout structure, a structured document containing at least a logical structure of a specific document content and a template specifying constraints for generating a plurality of layout structures means for storing the plurality of layout structures and also for selectively generating a desired layout structure from among the plurality of stored layout structures, and a layout structure corresponding to the logical structure of the specific document content is generated on the basis of the template, the device comprising: intermediate data structure generating means for generating an intermediate data structure which is a hierarchal structure representing simultaneously both template information corresponding to the template and generation history information indicative of a generation history of a layout structure based on the template and also for generating as an initial state of the intermediate data structure a hierarchal structure corresponding to a minimum layout structure common to the plurality of layout structures generatable based on the template; intermediate data structure holding means for holding the intermediate data structure generated by the intermediate data structure generating means; intermediate data structure altering means for altering the intermediate data structure held in the intermediate data structure holding means by referring to the template information and the generation history information held in the intermediate data structure holding means so that the logical structure of the specific document content conforms to the template; and specific layout structure extracting means for extracting a layout structure corresponding to the logical structure of the specific document content from a latest intermediate data structure held in the intermediate data structure holding means. 9. A device according to claim 7, wherein the intermediate data structure generating means, when the template is represented by generation rules of a grammar from which hierarchal structures are derived, generates nodes in association with non-terminal symbols which appear in the generation rules and terminal symbols including operators and also generates as the intermediate data structure a tree structure configured by the nodes said nodes including an immediately subordinate node; the intermediate data structure holding means, in a node among the nodes, which is being generated in association with the operator, holds operator type information indicative of an operator type and manipulation history information indicative of a history of addition or deletion of the immediately subordinate node to or from the node being generated; and the intermediate data structure altering means executes the addition or deletion of a node to or from the tree structure based on the operator type information and the manipulation history information so that the logical structure of the specific document content conforms to the template. | 0.5 |
1. A computer implemented method for inferring a probability of a first inference relating to a drug, the computer implemented method comprising: importing additional data into the plurality of data, wherein the additional data initially is not associated with metadata and the additional data does not conform to the dimensions of the database; conforming the additional data to the dimensions of the database; associating metadata and a key with each datum of the additional data; receiving a query at a database regarding a fact related to the drug, wherein the first inference is absent from the database, wherein the database comprises a plurality of divergent data, wherein the plurality of divergent data includes a plurality of cohort data, wherein each datum of the database is conformed to the dimensions of the database, wherein each datum of the plurality of data has associated metadata and an associated key, wherein the associated metadata comprises data regarding cohorts associated with the corresponding datum, data regarding hierarchies associated with the corresponding datum, data regarding a corresponding source of the datum, and data regarding probabilities associated with integrity, reliability, and importance of each associated datum; establishing the fact as a frame of reference for the query; determining an I th set of rules using a J th set of rules, wherein the J th set of rules, wherein J=1 is the first iteration of a recursion process and I-1 is the J th iteration of the recursion process, wherein the I th set of rules is a first set of rules, and wherein the J th set of rules is a second set of rules; applying the first set of rules to the query, wherein the first set of rules are determined for the query according to the second set of rules, wherein the frame of reference serves as an anchor for generating associations among the plurality of cohort data and is used to determine rules for limiting the plurality of divergent data that is searched, wherein the first set of rules determine how the plurality of divergent data are to be compared to the fact, and wherein the first set of rules determine a search space for the query; executing the query to create the first inference and the probability of the first inference, wherein the probability of the first inference is determined from comparing the plurality of data according to the first set of rules, wherein the probability of the first inference is based on factors selected from the group consisting of: a timing of the plurality of data according to the first set of rules, a source of the plurality of data according to the first set of rules, a trustworthiness of the plurality of data according to the first set of rules, a relevance of the plurality of data according to the first set of rules the plurality of data according to the first set of rules, a reliability of the plurality of data according to the first set of rules, an importance of the plurality of data according to the first set of rules, a data integrity of the plurality of data according to the first set of rules, and cohort information of the plurality of data according to the first set of rules, wherein a combination of the factors has a synergistic effect on the probability of the first inference; and storing the probability of the first inference, wherein subsequently viewing the first inference is accessible to individuals having one of a set of different security access clearances based on the probability of the first inference having a higher or lower threshold of certainty probabilities of inferences when the inference implicates medical privacy laws, wherein first ones the individuals having a first one of the set of different security access clearances are permitted to viewing the first inference, and, wherein second ones the individuals having a second one of the set of different security access clearances are not permitted to viewing the first inference. | 1. A computer implemented method for inferring a probability of a first inference relating to a drug, the computer implemented method comprising: importing additional data into the plurality of data, wherein the additional data initially is not associated with metadata and the additional data does not conform to the dimensions of the database; conforming the additional data to the dimensions of the database; associating metadata and a key with each datum of the additional data; receiving a query at a database regarding a fact related to the drug, wherein the first inference is absent from the database, wherein the database comprises a plurality of divergent data, wherein the plurality of divergent data includes a plurality of cohort data, wherein each datum of the database is conformed to the dimensions of the database, wherein each datum of the plurality of data has associated metadata and an associated key, wherein the associated metadata comprises data regarding cohorts associated with the corresponding datum, data regarding hierarchies associated with the corresponding datum, data regarding a corresponding source of the datum, and data regarding probabilities associated with integrity, reliability, and importance of each associated datum; establishing the fact as a frame of reference for the query; determining an I th set of rules using a J th set of rules, wherein the J th set of rules, wherein J=1 is the first iteration of a recursion process and I-1 is the J th iteration of the recursion process, wherein the I th set of rules is a first set of rules, and wherein the J th set of rules is a second set of rules; applying the first set of rules to the query, wherein the first set of rules are determined for the query according to the second set of rules, wherein the frame of reference serves as an anchor for generating associations among the plurality of cohort data and is used to determine rules for limiting the plurality of divergent data that is searched, wherein the first set of rules determine how the plurality of divergent data are to be compared to the fact, and wherein the first set of rules determine a search space for the query; executing the query to create the first inference and the probability of the first inference, wherein the probability of the first inference is determined from comparing the plurality of data according to the first set of rules, wherein the probability of the first inference is based on factors selected from the group consisting of: a timing of the plurality of data according to the first set of rules, a source of the plurality of data according to the first set of rules, a trustworthiness of the plurality of data according to the first set of rules, a relevance of the plurality of data according to the first set of rules the plurality of data according to the first set of rules, a reliability of the plurality of data according to the first set of rules, an importance of the plurality of data according to the first set of rules, a data integrity of the plurality of data according to the first set of rules, and cohort information of the plurality of data according to the first set of rules, wherein a combination of the factors has a synergistic effect on the probability of the first inference; and storing the probability of the first inference, wherein subsequently viewing the first inference is accessible to individuals having one of a set of different security access clearances based on the probability of the first inference having a higher or lower threshold of certainty probabilities of inferences when the inference implicates medical privacy laws, wherein first ones the individuals having a first one of the set of different security access clearances are permitted to viewing the first inference, and, wherein second ones the individuals having a second one of the set of different security access clearances are not permitted to viewing the first inference. 15. The computer implemented method of claim 1 wherein the additional data is imported according to a technique selected from the group consisting of federation and extraction, transformation, and loading. | 0.570439 |
11. A method for electronically executing an electronic document, comprising: receiving, from a single computer, first identification information associated with a first signatory user; receiving, from the single computer, second identification information associated with a second signatory user; receiving, from the single computer, third identification information associated with a notary user; identifying, on a display, at least one unexecuted electronic document that requires execution by the first and second signatory users; receiving a first user command from the single computer identifying the assent of the first signatory user to the execution of the at least one unexecuted electronic document; receiving a second user command from the single computer identifying the assent of the second signatory user to the execution of the at least one unexecuted electronic document; receiving a third user command from the single computer identifying the presence of a notary user with the first signatory user; in response to receiving the first user command and the second user command, electronically executing the at least one unexecuted document by applying official electronic notarization indicia associated with the notary user to the at least one unexecuted document to create at least one electronically executed document, the official electronic notarization indicia certifying the presence of the notary user at the execution of the at least one executed and certified electronic document by the first user and identifying the notary user as a registered and valid notary meeting at least one jurisdictional requirement. | 11. A method for electronically executing an electronic document, comprising: receiving, from a single computer, first identification information associated with a first signatory user; receiving, from the single computer, second identification information associated with a second signatory user; receiving, from the single computer, third identification information associated with a notary user; identifying, on a display, at least one unexecuted electronic document that requires execution by the first and second signatory users; receiving a first user command from the single computer identifying the assent of the first signatory user to the execution of the at least one unexecuted electronic document; receiving a second user command from the single computer identifying the assent of the second signatory user to the execution of the at least one unexecuted electronic document; receiving a third user command from the single computer identifying the presence of a notary user with the first signatory user; in response to receiving the first user command and the second user command, electronically executing the at least one unexecuted document by applying official electronic notarization indicia associated with the notary user to the at least one unexecuted document to create at least one electronically executed document, the official electronic notarization indicia certifying the presence of the notary user at the execution of the at least one executed and certified electronic document by the first user and identifying the notary user as a registered and valid notary meeting at least one jurisdictional requirement. 14. The method of claim 11 , wherein: receiving the first user command comprises identifying that the first user used an input device associated with the first computer to click on a first signature box within the at least one unexecuted electronic document; and receiving the second user command comprises identifying that the second user used the input device associated with the first computer to click on a second signature box within the at least one unexecuted electronic document. | 0.839727 |
13. A system, implemented in one or more configured computer systems, for automatically registering domain names, the system comprising: a data store configured to store specific computer-executable instructions; and one or more computing devices in communication with the data store, the one or more computing devices configured to execute the specific computer-executable instructions to at least: identify a domain name source from information automatically retrieved from one or more network resources; analyze data from the domain name source for a statistically improbable phrase or an atomic term; identify a domain name candidate from the statistically improbable phrase or the atomic term; calculate a value representing a significance of the domain name candidate; determine that the value satisfies a threshold indicating that the domain name candidate is desirable; and automatically register the domain name candidate as a domain name. | 13. A system, implemented in one or more configured computer systems, for automatically registering domain names, the system comprising: a data store configured to store specific computer-executable instructions; and one or more computing devices in communication with the data store, the one or more computing devices configured to execute the specific computer-executable instructions to at least: identify a domain name source from information automatically retrieved from one or more network resources; analyze data from the domain name source for a statistically improbable phrase or an atomic term; identify a domain name candidate from the statistically improbable phrase or the atomic term; calculate a value representing a significance of the domain name candidate; determine that the value satisfies a threshold indicating that the domain name candidate is desirable; and automatically register the domain name candidate as a domain name. 17. The system of claim 13 , wherein the one or more computing devices are further configured to monetize the domain name. | 0.704339 |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.