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9,342,516 | 5 | 6 | 5. The method of claim 1 , wherein the annotation comprises a portion of the voice input received by the one or more audio inputs. | 5. The method of claim 1 , wherein the annotation comprises a portion of the voice input received by the one or more audio inputs. 6. The method of claim 5 , the computing device further comprising one or more video input devices, the method further comprising receiving video input at the one or more video input devices during the playing, the annotation comprising a portion of the video input received by the one or more video input devices. | 0.5 |
9,471,705 | 8 | 13 | 8. A system comprising: one or more processors; and a memory coupled by the processors comprising instructions executable by the processors, the processors being operable when executing the instructions to: receive a request from a client device for a target structured document; in response to the received request from the client device and in a first response phase: access a data structure comprising an entry for the target structured document and one or more first resources associated with the target structured document; for each of one or more of the first resources associated with the target structured document: compute a probability for the first resource that represents a likelihood that the first resource will be included in a response to a future request for the target structured document; compare the probability to a first predetermined threshold; and when the probability exceeds the first predetermined threshold, identify the first resource as a selected first resource for the target structured document; generate a first response portion comprising one or more of: one or more of the selected first resources; or one or more references to one or more of the selected first resources; send the first response portion to the client device; and further in response to the received request from the client device and in a second response phase taking place after sending the first response portion: for each of one or more second resources associated with the target structured document: compute a probability for the second resource that represents a likelihood that the second resource will be included in a response to a future request for the target structured document; compare the probability to a second predetermined threshold; and when the probability exceeds the second predetermined threshold, identify the second resource as a selected second resource for the target structured document; generate a second response portion comprising structured document language code for the target structured document and one or more of: one or more of the selected second resources associated with the target structured document; or one or more references to one or more of the selected second resources; and send the second response portion to the same client device. | 8. A system comprising: one or more processors; and a memory coupled by the processors comprising instructions executable by the processors, the processors being operable when executing the instructions to: receive a request from a client device for a target structured document; in response to the received request from the client device and in a first response phase: access a data structure comprising an entry for the target structured document and one or more first resources associated with the target structured document; for each of one or more of the first resources associated with the target structured document: compute a probability for the first resource that represents a likelihood that the first resource will be included in a response to a future request for the target structured document; compare the probability to a first predetermined threshold; and when the probability exceeds the first predetermined threshold, identify the first resource as a selected first resource for the target structured document; generate a first response portion comprising one or more of: one or more of the selected first resources; or one or more references to one or more of the selected first resources; send the first response portion to the client device; and further in response to the received request from the client device and in a second response phase taking place after sending the first response portion: for each of one or more second resources associated with the target structured document: compute a probability for the second resource that represents a likelihood that the second resource will be included in a response to a future request for the target structured document; compare the probability to a second predetermined threshold; and when the probability exceeds the second predetermined threshold, identify the second resource as a selected second resource for the target structured document; generate a second response portion comprising structured document language code for the target structured document and one or more of: one or more of the selected second resources associated with the target structured document; or one or more references to one or more of the selected second resources; and send the second response portion to the same client device. 13. The system of claim 8 , wherein the first response portion and the second response portion are sent to the client device over a persistent network connection. | 0.798005 |
8,630,856 | 1 | 11 | 1. A computer implemented method for processing language input comprising the steps of: using at least one processor to perform: determining at least two possible meanings for a language input; determining a first probability that a first possible meaning of the at least two possible meanings is a correct interpretation of said language input; determining a second probability that a second possible meaning of the at least two possible meanings is a correct interpretation of said language input computing at least one relative delta computation comprising a value derived at least in part on a difference between at least the first probability and the second probability, the difference divided by a denominator based on the first probability; detecting at least one irregularity within said language input based upon said relative delta computation; and performing at least one programmatic action responsive to detecting said irregularity. | 1. A computer implemented method for processing language input comprising the steps of: using at least one processor to perform: determining at least two possible meanings for a language input; determining a first probability that a first possible meaning of the at least two possible meanings is a correct interpretation of said language input; determining a second probability that a second possible meaning of the at least two possible meanings is a correct interpretation of said language input computing at least one relative delta computation comprising a value derived at least in part on a difference between at least the first probability and the second probability, the difference divided by a denominator based on the first probability; detecting at least one irregularity within said language input based upon said relative delta computation; and performing at least one programmatic action responsive to detecting said irregularity. 11. The method of claim 1 , wherein said first and second probabilities are confidence values, said method further comprising the steps of: receiving a plurality of language inputs; for each language input, determining at least two possible meanings and associated confidence values; plotting at least a portion of said confidence values on a graph; and determining at least one threshold from said graph, wherein said relative delta computation is compared against said threshold when detecting said irregularity. | 0.682716 |
8,005,847 | 17 | 25 | 17. A system comprising: a plurality of modules, each module comprising instructions retained on at least one machine-readable storage medium, that when executed by a machine perform identified operations, wherein the modules comprise: a file access module to access a main file name of a main file and to access a relationship definition associated with a package of data items, the relationship definition identifying a class of relationships between data items included in the package of data items, the relationship definition associating the main file with information stored in a related file, the at least one relationship definition including a template string and a first pattern string; and a matching module to determine whether a first portion of the main file name matches the first pattern string and, based on the determination, selectively to: match the main file name to the first pattern string; substitute a second substring of the main file name into the first pattern string; derive a second pattern string from the template string and the first pattern string having the substituted second substring of the main file name; and identify a related file name that matches the second pattern string; wherein the information stored in the related file is related to information contained in the main file, and wherein the information stored in the related file is for use in rendering the main file without altering the information contained in the main file. | 17. A system comprising: a plurality of modules, each module comprising instructions retained on at least one machine-readable storage medium, that when executed by a machine perform identified operations, wherein the modules comprise: a file access module to access a main file name of a main file and to access a relationship definition associated with a package of data items, the relationship definition identifying a class of relationships between data items included in the package of data items, the relationship definition associating the main file with information stored in a related file, the at least one relationship definition including a template string and a first pattern string; and a matching module to determine whether a first portion of the main file name matches the first pattern string and, based on the determination, selectively to: match the main file name to the first pattern string; substitute a second substring of the main file name into the first pattern string; derive a second pattern string from the template string and the first pattern string having the substituted second substring of the main file name; and identify a related file name that matches the second pattern string; wherein the information stored in the related file is related to information contained in the main file, and wherein the information stored in the related file is for use in rendering the main file without altering the information contained in the main file. 25. The system of claim 17 , wherein the first wildcard symbol is to match a substring of the main file name, the substring including at least one character. | 0.832623 |
9,047,285 | 12 | 13 | 12. The method of claim 1 , wherein the first frame is a benefit frame. | 12. The method of claim 1 , wherein the first frame is a benefit frame. 13. The method of claim 12 , wherein the content of interest is information regarding existing technologies. | 0.517857 |
8,860,752 | 24 | 26 | 24. A method for multimedia scripting in a computer system, comprising the steps of: presenting a script, written in a scripting language, comprising a variable representing one or more multimedia items and a manipulation to be applied to the one or more multimedia items, wherein the scripting language comprises syntax enabling system-wide application of the manipulation; evaluating the script after extraction from a container file at runtime via an interface to one or more programmable processing units communicatively coupled to each other in the computer system; invoking a plurality of processes for manipulating multimedia items in dependence upon the script, each such process associated with one or more multimedia types; processing a first multimedia type with a first process; and processing a second multimedia type with a second process, wherein the script is referenced to render one or more result multimedia items, and is referenced as one or more second original multimedia items in at least one other script for batch manipulation. | 24. A method for multimedia scripting in a computer system, comprising the steps of: presenting a script, written in a scripting language, comprising a variable representing one or more multimedia items and a manipulation to be applied to the one or more multimedia items, wherein the scripting language comprises syntax enabling system-wide application of the manipulation; evaluating the script after extraction from a container file at runtime via an interface to one or more programmable processing units communicatively coupled to each other in the computer system; invoking a plurality of processes for manipulating multimedia items in dependence upon the script, each such process associated with one or more multimedia types; processing a first multimedia type with a first process; and processing a second multimedia type with a second process, wherein the script is referenced to render one or more result multimedia items, and is referenced as one or more second original multimedia items in at least one other script for batch manipulation. 26. The method of claim 24 , wherein a multimedia item comprises a still image. | 0.923002 |
8,555,250 | 1 | 7 | 1. In a computing environment, a processor-implemented method of analyzing dynamic source code, the method comprising: parsing source code to generate one or more syntax trees defining constructs in a body of dynamic language source code; from the one or more syntax trees, extracting identifier information for one or more of the defined constructs; augmenting knowledge about the constructs by at least one of explicit inspection of the body of source code or implied references related to the source code; producing a correlation between identifiers and augmented knowledge; using the identifier information and augmented knowledge, generating metadata about the body of the dynamic language source code, the generated metadata being represented as a symbol table; parsing symbol table to compute metrics about the one or more syntax trees; and providing the metrics about the one or more syntax trees to a user, wherein the metrics provide code correctness analysis, and wherein the correctness analysis indicates parameter count mismatches. | 1. In a computing environment, a processor-implemented method of analyzing dynamic source code, the method comprising: parsing source code to generate one or more syntax trees defining constructs in a body of dynamic language source code; from the one or more syntax trees, extracting identifier information for one or more of the defined constructs; augmenting knowledge about the constructs by at least one of explicit inspection of the body of source code or implied references related to the source code; producing a correlation between identifiers and augmented knowledge; using the identifier information and augmented knowledge, generating metadata about the body of the dynamic language source code, the generated metadata being represented as a symbol table; parsing symbol table to compute metrics about the one or more syntax trees; and providing the metrics about the one or more syntax trees to a user, wherein the metrics provide code correctness analysis, and wherein the correctness analysis indicates parameter count mismatches. 7. The method of claim 1 , wherein the correctness analysis indicates type conflicts discovered during a type inferencing phase as errors. | 0.634921 |
9,245,361 | 1 | 12 | 1. A method for consolidating a glyph of a font, comprising: normalizing a first contour in the glyph and a second contour in the glyph to generate a first normalized contour and a second normalized contour; comparing the first normalized contour to the second normalized contour, comprising at least one of comparing a first number of points of the first normalized contour to a second number of points of the second normalized contour, comparing a first position of points of the first normalized contour to a second position of points of the second normalized contour or comparing a first order of points of the first normalized contour to a second order of points of the second normalized contour; based upon the comparing, determining that the first normalized contour and the second normalized contour comprise at least one of a same number of points, a same position of points or a same order of points indicating that the first contour and the second contour correspond to a common contour; and based upon the determining, replacing the first contour with a first reference to a common simple glyph for the common contour and replacing the second contour with a second reference to the common simple glyph to consolidate the glyph. | 1. A method for consolidating a glyph of a font, comprising: normalizing a first contour in the glyph and a second contour in the glyph to generate a first normalized contour and a second normalized contour; comparing the first normalized contour to the second normalized contour, comprising at least one of comparing a first number of points of the first normalized contour to a second number of points of the second normalized contour, comparing a first position of points of the first normalized contour to a second position of points of the second normalized contour or comparing a first order of points of the first normalized contour to a second order of points of the second normalized contour; based upon the comparing, determining that the first normalized contour and the second normalized contour comprise at least one of a same number of points, a same position of points or a same order of points indicating that the first contour and the second contour correspond to a common contour; and based upon the determining, replacing the first contour with a first reference to a common simple glyph for the common contour and replacing the second contour with a second reference to the common simple glyph to consolidate the glyph. 12. The method of claim 1 , comprising creating a complex glyph comprising: a reference to one or more transformations for the common simple glyph. | 0.634328 |
7,555,718 | 1 | 17 | 1. A method for presenting video search results, including one or more videos, comprising the following steps: a) receiving a set of video search results for the one or more videos, wherein the video search results comprise one or more stories for each of the one or more videos; b) selecting from each story a set of shots; c) selecting from each shot one or more representative keyframes, wherein an area of each keyframe in the collage indicates a relevance of the video search results to the shot selection, wherein the relevance is determined by a combination of a search retrieval score of the shot and a search retrieval score of the story comprising the shot; and d) creating for each story a collage comprising the keyframes, wherein the collage can be used to present the video search results. | 1. A method for presenting video search results, including one or more videos, comprising the following steps: a) receiving a set of video search results for the one or more videos, wherein the video search results comprise one or more stories for each of the one or more videos; b) selecting from each story a set of shots; c) selecting from each shot one or more representative keyframes, wherein an area of each keyframe in the collage indicates a relevance of the video search results to the shot selection, wherein the relevance is determined by a combination of a search retrieval score of the shot and a search retrieval score of the story comprising the shot; and d) creating for each story a collage comprising the keyframes, wherein the collage can be used to present the video search results. 17. The method of claim 1 , wherein the keyframes are scaled and cropped. | 0.841304 |
7,580,924 | 1 | 3 | 1. A method for processing semi-conductor manufacturing data comprising: capturing files from a plurality of manufacturing data sites having semi-conductor manufacturing data; the semi-conductor manufacturing data including test data; the test data indicating electrical characteristics; converting the files into a standard format for storage in a database; building a query from a client device using a plurality of sets of selections, wherein when a first selection is made by the client device in a first set of the sets of selections, a second set of the sets of selections is dynamically changed to only display a displayed set of selections on the client device limited to selections associated with the first selection; checking limits of the test data of the semi-conductor manufacturing data by accessing the database and comparing the test data of the semi-conductor manufacturing data stored in the database with limits therefor; a portion of the limits being user defined limits including a parameter limit associated with the electrical characteristics, the parameter limit being for threshold voltage; alerting the client device, when any stored semi-conductor manufacturing data exceeds one or more limit of the portion of the limits; and generating a report for the client device based on the built query and the converted files stored in the database. | 1. A method for processing semi-conductor manufacturing data comprising: capturing files from a plurality of manufacturing data sites having semi-conductor manufacturing data; the semi-conductor manufacturing data including test data; the test data indicating electrical characteristics; converting the files into a standard format for storage in a database; building a query from a client device using a plurality of sets of selections, wherein when a first selection is made by the client device in a first set of the sets of selections, a second set of the sets of selections is dynamically changed to only display a displayed set of selections on the client device limited to selections associated with the first selection; checking limits of the test data of the semi-conductor manufacturing data by accessing the database and comparing the test data of the semi-conductor manufacturing data stored in the database with limits therefor; a portion of the limits being user defined limits including a parameter limit associated with the electrical characteristics, the parameter limit being for threshold voltage; alerting the client device, when any stored semi-conductor manufacturing data exceeds one or more limit of the portion of the limits; and generating a report for the client device based on the built query and the converted files stored in the database. 3. The method of claim 1 wherein the altering includes sending an email to an email client on the client device. | 0.774194 |
8,856,235 | 1 | 7 | 1. A computer-implemented method comprising: maintaining a user profile for each of a plurality of users of a social networking system, each user profile comprising a set of attributes; selecting a user from the plurality of users; identifying, by a computer, a plurality of interactions between the selected user and one or more of a set of users who are connected to the selected user in the social networking system; inferring, by the computer, a value of an attribute of the user profile for the selected user based on one or more of: topic analysis of the one or more identified interactions and sentiment analysis associated with one or more topics identified in the one or more identified interactions; determining relevant information for the selected user based on the inferred user profile attribute; and sending the relevant information to the selected user. | 1. A computer-implemented method comprising: maintaining a user profile for each of a plurality of users of a social networking system, each user profile comprising a set of attributes; selecting a user from the plurality of users; identifying, by a computer, a plurality of interactions between the selected user and one or more of a set of users who are connected to the selected user in the social networking system; inferring, by the computer, a value of an attribute of the user profile for the selected user based on one or more of: topic analysis of the one or more identified interactions and sentiment analysis associated with one or more topics identified in the one or more identified interactions; determining relevant information for the selected user based on the inferred user profile attribute; and sending the relevant information to the selected user. 7. The computer-implemented method of claim 1 , wherein the set of users is determined to be a subset of users connected to the selected user, the subset determined based on a metric describing a closeness of the user with the selected user. | 0.782098 |
7,624,005 | 10 | 15 | 10. A computer readable storage medium having stored thereon a program, the program being executable by a processor for performing a method, the method comprising: assigning a part of speech identifier to each word in a source string, the source string in a first language; detecting a first sequence of syntactic chunks in the source string, the syntactic chunks each comprising at least one of the words; assigning a syntactic chunk label to each of the detected syntactic chunks in the source string; defining connections between each of the detected syntactic chunks in the source string and at least one syntactic chunk of a sequence of syntactic chunks in a target string, the target string being a parallel translation in a second language of the source string, said defining comprising determining connections based on a chunk mapping table, the chunk mapping table using pre-defined connections based on the assigned syntactic chunk label; mapping each word in the detected syntactic chunks in the source string to each word in the syntactic chunks in the target string, said mapping based on a word mapping table and the part of speech identifier; and translating by a computer an input string in the first language into a translation in the second language based on the chunk mapping table and the word mapping table. | 10. A computer readable storage medium having stored thereon a program, the program being executable by a processor for performing a method, the method comprising: assigning a part of speech identifier to each word in a source string, the source string in a first language; detecting a first sequence of syntactic chunks in the source string, the syntactic chunks each comprising at least one of the words; assigning a syntactic chunk label to each of the detected syntactic chunks in the source string; defining connections between each of the detected syntactic chunks in the source string and at least one syntactic chunk of a sequence of syntactic chunks in a target string, the target string being a parallel translation in a second language of the source string, said defining comprising determining connections based on a chunk mapping table, the chunk mapping table using pre-defined connections based on the assigned syntactic chunk label; mapping each word in the detected syntactic chunks in the source string to each word in the syntactic chunks in the target string, said mapping based on a word mapping table and the part of speech identifier; and translating by a computer an input string in the first language into a translation in the second language based on the chunk mapping table and the word mapping table. 15. The computer readable medium of claim 10 , wherein translating comprises causing a machine to translate phrases. | 0.765182 |
9,152,791 | 10 | 11 | 10. A method of detecting fake antivirus software, said method comprising: collecting keywords that are comprehensible words by scanning memory dumps of a plurality of executing fake antivirus software samples and storing said keywords in a keyword database; identifying an executing process in a computer; retrieving a rule from a rule database, said rule using two or more of said keywords to identify fake software; retrieving said keywords from said keyword database, each of said keywords being indicative of fake antivirus software; applying said rule to said executing process and determining that keywords of said rule match data in said process executing in a memory of said computer by scanning said process in said memory; determining, after said step of applying, that said process is legitimate antivirus software when a digital certificate of said process is valid, when an identification of said process does exist in a white list of valid processes, or when a company name associated with said process does exist in a white list of valid company names; and displaying, on said computer, an indication that said process is legitimate antivirus software based on said applying and said determining. | 10. A method of detecting fake antivirus software, said method comprising: collecting keywords that are comprehensible words by scanning memory dumps of a plurality of executing fake antivirus software samples and storing said keywords in a keyword database; identifying an executing process in a computer; retrieving a rule from a rule database, said rule using two or more of said keywords to identify fake software; retrieving said keywords from said keyword database, each of said keywords being indicative of fake antivirus software; applying said rule to said executing process and determining that keywords of said rule match data in said process executing in a memory of said computer by scanning said process in said memory; determining, after said step of applying, that said process is legitimate antivirus software when a digital certificate of said process is valid, when an identification of said process does exist in a white list of valid processes, or when a company name associated with said process does exist in a white list of valid company names; and displaying, on said computer, an indication that said process is legitimate antivirus software based on said applying and said determining. 11. The method as recited in claim 10 wherein said step of displaying indicates that said process is legitimate antivirus software by not displaying any information concerning said process. | 0.570455 |
9,665,246 | 6 | 9 | 6. A non-transitory computer-readable storage medium comprising instructions that, when executed, cause at least one processor to: determine, based on a first input at a graphical keyboard, a plurality of candidate strings and an initial ranked ordering of the plurality of candidate character strings, wherein a first candidate character string from the plurality of candidate character strings is associated with a highest rank of the initial ranked ordering of the plurality of candidate character strings and a second candidate character string from the plurality of candidate character strings is associated with a second-highest rank of the initial ranked ordering of the plurality of candidate character strings; output, for display, based on the initial ranked ordering of the plurality of candidate character strings, the first candidate character string in a first text suggestion region from a plurality of text suggestion regions and the second candidate character string in a second text suggestion region from the plurality of text suggestion regions, the first text suggestion region being associated with a highest rank of a ranked ordering of the plurality of text suggestion regions and the second text suggestion region being associated with a second-highest rank of the ranked ordering of the plurality of text suggestion regions; receive a second input that selects the first candidate character string from the first text suggestion region; determine, based on a third input at the graphical keyboard, the plurality of candidate strings and a subsequent ranked ordering of the plurality of candidate character strings, the first candidate character string being associated with a second-highest rank of the subsequent ranked ordering of the plurality of candidate character strings and the second candidate character string being associated with a highest rank of the subsequent ranked ordering of the plurality of candidate character strings; associate, based on the subsequent ranked ordering of the plurality of candidate character strings, the first candidate character string with the second text suggestion region; and responsive to determining that the first candidate character string was previously selected from the first text suggestion region when the first candidate character string was previously displayed, output, for display, the first candidate character string in the first text suggestion region. | 6. A non-transitory computer-readable storage medium comprising instructions that, when executed, cause at least one processor to: determine, based on a first input at a graphical keyboard, a plurality of candidate strings and an initial ranked ordering of the plurality of candidate character strings, wherein a first candidate character string from the plurality of candidate character strings is associated with a highest rank of the initial ranked ordering of the plurality of candidate character strings and a second candidate character string from the plurality of candidate character strings is associated with a second-highest rank of the initial ranked ordering of the plurality of candidate character strings; output, for display, based on the initial ranked ordering of the plurality of candidate character strings, the first candidate character string in a first text suggestion region from a plurality of text suggestion regions and the second candidate character string in a second text suggestion region from the plurality of text suggestion regions, the first text suggestion region being associated with a highest rank of a ranked ordering of the plurality of text suggestion regions and the second text suggestion region being associated with a second-highest rank of the ranked ordering of the plurality of text suggestion regions; receive a second input that selects the first candidate character string from the first text suggestion region; determine, based on a third input at the graphical keyboard, the plurality of candidate strings and a subsequent ranked ordering of the plurality of candidate character strings, the first candidate character string being associated with a second-highest rank of the subsequent ranked ordering of the plurality of candidate character strings and the second candidate character string being associated with a highest rank of the subsequent ranked ordering of the plurality of candidate character strings; associate, based on the subsequent ranked ordering of the plurality of candidate character strings, the first candidate character string with the second text suggestion region; and responsive to determining that the first candidate character string was previously selected from the first text suggestion region when the first candidate character string was previously displayed, output, for display, the first candidate character string in the first text suggestion region. 9. The non-transitory computer-readable storage medium of claim 6 , wherein the instructions, when executed, cause the at least one processor to receive the third input by receiving an indication of a touch input detected at a presence-sensitive input device to select the first text suggestion region. | 0.697395 |
10,089,393 | 7 | 11 | 7. A system, comprising: a data storage device; and one or more data processing apparatus that interact with the data storage device and execute instructions that cause the one or more data processing apparatus to perform operations comprising: determining, for a prefix that represents a portion of a search query, one or more completions that yield a completed search query when combined with the prefix, wherein the one or more completions includes a given completion that describes a question; receiving, through a search engine input field presented at a user device, user input including the prefix; identifying, based on the user input, an answer box for the one or more completions, wherein the answer box includes an answer to the question described by the given completion that is included in the one or more completions; and providing, prior to a completed search query being specified by the user input, instructions that cause the answer box to be presented in a display of the user device along with at least some of the one or more completions for which the answer box was identified and the answer to the question described by the given completion, wherein the answer box is a user interface element that presents the one or more completions and the answer to the question described by the given completion while a user enters text input through the search engine input field and before the user submits a search request for the user input. | 7. A system, comprising: a data storage device; and one or more data processing apparatus that interact with the data storage device and execute instructions that cause the one or more data processing apparatus to perform operations comprising: determining, for a prefix that represents a portion of a search query, one or more completions that yield a completed search query when combined with the prefix, wherein the one or more completions includes a given completion that describes a question; receiving, through a search engine input field presented at a user device, user input including the prefix; identifying, based on the user input, an answer box for the one or more completions, wherein the answer box includes an answer to the question described by the given completion that is included in the one or more completions; and providing, prior to a completed search query being specified by the user input, instructions that cause the answer box to be presented in a display of the user device along with at least some of the one or more completions for which the answer box was identified and the answer to the question described by the given completion, wherein the answer box is a user interface element that presents the one or more completions and the answer to the question described by the given completion while a user enters text input through the search engine input field and before the user submits a search request for the user input. 11. The system of claim 7 , wherein the instructions cause the one or more data processing apparatus to perform operations further comprising providing instructions that cause presentation of a given content item in the answer box. | 0.662281 |
8,122,020 | 7 | 11 | 7. A method of enabling a user to obtain focused item recommendations for a user-specified category of items, the method comprising: by one or more computer systems comprising computer hardware: providing a tagging interface that provides functionality for a user to create tags for classifying items represented in an interactive catalog via entry of text strings into tag fields of item detail pages of the catalog, the item detail pages each comprising information descriptive of a catalog item in an interactive catalog of items; persistently storing tag data of the user in a data repository, the tag data specifying tags and tag-item associations created by the user via the tagging interface; outputting for display to the user one or more of the tags created by the user; and generating tag-specific item recommendations, said generating comprising: receiving a user selection of a displayed tag of the one or more tags output for display to the user; programmatically accessing the tag data in response to receiving the user selection of the displayed tag, to thereby identify at least one of the catalog items previously tagged with the displayed tag by the user, without requiring further user interaction, programmatically accessing item relationship data to identify a set of additional items that are related to the catalog items previously tagged by the user with the displayed tag, and selecting at least a portion of the additional items to recommend to the user as the tag-specific item recommendations. | 7. A method of enabling a user to obtain focused item recommendations for a user-specified category of items, the method comprising: by one or more computer systems comprising computer hardware: providing a tagging interface that provides functionality for a user to create tags for classifying items represented in an interactive catalog via entry of text strings into tag fields of item detail pages of the catalog, the item detail pages each comprising information descriptive of a catalog item in an interactive catalog of items; persistently storing tag data of the user in a data repository, the tag data specifying tags and tag-item associations created by the user via the tagging interface; outputting for display to the user one or more of the tags created by the user; and generating tag-specific item recommendations, said generating comprising: receiving a user selection of a displayed tag of the one or more tags output for display to the user; programmatically accessing the tag data in response to receiving the user selection of the displayed tag, to thereby identify at least one of the catalog items previously tagged with the displayed tag by the user, without requiring further user interaction, programmatically accessing item relationship data to identify a set of additional items that are related to the catalog items previously tagged by the user with the displayed tag, and selecting at least a portion of the additional items to recommend to the user as the tag-specific item recommendations. 11. The method of claim 7 , wherein the tagging interface further enables the user to limit the tag-specific item recommendations to selected items within a category of the interactive catalog. | 0.557339 |
9,514,110 | 8 | 11 | 8. A method comprising: receiving, by a system comprising a processor, a request of a first user to perform a task associated with collaborative editing or collaborative reviewing, in collaboration with a second user, of a section of an electronic document during a selected schedule time slot selected by the first user; retrieving, by the system, schedules of the first and second users from electronic calendars associated with the first and second users; ascertaining, by the system, whether the section of the electronic document is unavailable due to prior selection for collaborative editing or collaborative reviewing by another user during the selected schedule time slot; in response to ascertaining that the section of the electronic document is unavailable, proposing, by the system, a different schedule time slot for the collaborative editing or the collaborative reviewing of the section of the electronic document by the first user in collaboration with the second user; determining, by the system in response to ascertaining that the section of the electronic document is available, availability of the first and second users during the selected schedule time slot based on the retrieved schedules, and, in response to the determining, providing permissions, to perform the task on the section of the electronic document, to the first user for the selected schedule time slot. | 8. A method comprising: receiving, by a system comprising a processor, a request of a first user to perform a task associated with collaborative editing or collaborative reviewing, in collaboration with a second user, of a section of an electronic document during a selected schedule time slot selected by the first user; retrieving, by the system, schedules of the first and second users from electronic calendars associated with the first and second users; ascertaining, by the system, whether the section of the electronic document is unavailable due to prior selection for collaborative editing or collaborative reviewing by another user during the selected schedule time slot; in response to ascertaining that the section of the electronic document is unavailable, proposing, by the system, a different schedule time slot for the collaborative editing or the collaborative reviewing of the section of the electronic document by the first user in collaboration with the second user; determining, by the system in response to ascertaining that the section of the electronic document is available, availability of the first and second users during the selected schedule time slot based on the retrieved schedules, and, in response to the determining, providing permissions, to perform the task on the section of the electronic document, to the first user for the selected schedule time slot. 11. The method as claimed in claim 8 , further comprising: ascertaining whether the selected schedule time slot for performing the task complies with a chronological order in which the task has to be performed; and generating a response for the first user to select a new schedule time slot in response to ascertaining the selected schedule time slot does not to comply with the chronological order. | 0.5 |
7,861,253 | 18 | 33 | 18. A multi-level business intelligence system interface client for use with at least one productivity client comprising: a GUI layer; an API layer; a productivity client adapter layer; and a kernel layer, wherein each time a report is executed by the business intelligence system for the at least one productivity client, persistence information is stored by the multi-level interface client in a file of the productivity client containing the report. | 18. A multi-level business intelligence system interface client for use with at least one productivity client comprising: a GUI layer; an API layer; a productivity client adapter layer; and a kernel layer, wherein each time a report is executed by the business intelligence system for the at least one productivity client, persistence information is stored by the multi-level interface client in a file of the productivity client containing the report. 33. The interface client according to claim 18 , wherein the productivity client is a client selected from the group consisting of a word processing client, a database client, a spreadsheet client, and a presentation client. | 0.672515 |
8,959,475 | 1 | 3 | 1. A computer program product embodied in a computer readable storage memory for intelligently and efficiently connecting with people and assets involved in projects, the computer program product comprising the programming instructions for: generating a first semantic graph for a first project based on resources within said first project, wherein said first semantic graph builds a relationship among entities of said first project; generating a second semantic graph for a second project based on resources within said second project, wherein said second semantic graph builds a relationship among entities of said second project, wherein said second project is a different project from said first project or a different version of said first project; receiving a selection of said first and said second semantic graphs representing different projects or versions of a same project; comparing and merging differences between said first and said second semantic graphs; and generating a third semantic graph that illustrates said differences between said first and said second semantic graphs, wherein said third semantic graph comprises nodes representing said entities of said first and said second projects, wherein said differences comprise one or more of the following: personnel and development processes. | 1. A computer program product embodied in a computer readable storage memory for intelligently and efficiently connecting with people and assets involved in projects, the computer program product comprising the programming instructions for: generating a first semantic graph for a first project based on resources within said first project, wherein said first semantic graph builds a relationship among entities of said first project; generating a second semantic graph for a second project based on resources within said second project, wherein said second semantic graph builds a relationship among entities of said second project, wherein said second project is a different project from said first project or a different version of said first project; receiving a selection of said first and said second semantic graphs representing different projects or versions of a same project; comparing and merging differences between said first and said second semantic graphs; and generating a third semantic graph that illustrates said differences between said first and said second semantic graphs, wherein said third semantic graph comprises nodes representing said entities of said first and said second projects, wherein said differences comprise one or more of the following: personnel and development processes. 3. The computer program product as recited in claim 1 further comprising the programming instructions for: displaying available communication channels and social communities in response to receiving an indication that a cursor is hovering over a node in said third semantic graph. | 0.70894 |
8,332,386 | 1 | 4 | 1. A method comprising: storing bonds that reflect relationships between searchable items; wherein the searchable items include a plurality of documents; wherein the bonds are not stored as part of the searchable items; wherein the bonds do not reflect storage location relationships between said searchable items; receiving a search request to perform a search for searchable items that are associated with a keyword, wherein the search request specifies the keyword; determining that a particular searchable item is to be the starting point of the search; responding to said search request by performing the search relative to the particular searchable item that is to be used as a starting point for said search, wherein performing the search includes at least one of: (a) ranking documents that match said search based, at least in part, on the minimum number of bonds that have to be traversed from the particular searchable item to arrive at each document, wherein the documents that match said search include a first document and a second document, wherein the minimum number of bonds that have to be traversed from said particular searchable to arrive at said first document is different than the minimum number of bonds that have to be traversed from said particular searchable item to arrive at said second document; (b) searching against documents that are within one bond of said particular searchable item; and if the number of documents, within one bond of said particular searchable item, that match the search is less than a target number, then expanding the search to documents that are within two bonds of said particular searchable item; and repeatedly expanding the search to documents that are one further bond away from the particular searchable item until at least the target number of documents that match the search have been identified; or (c) sequentially performing a series of searches relative to said particular searchable item until a predetermined maximum number of bonds from said particular searchable item has been reached, wherein the series of searches includes a first search and one or more subsequent searches, wherein each search of said one or more subsequent searches is performed against documents that are at a greater minimum number of bonds that have to be traversed from said particular searchable item than the documents that were searched in any search that preceded said search in said series; wherein the method is performed by one or more computing devices. | 1. A method comprising: storing bonds that reflect relationships between searchable items; wherein the searchable items include a plurality of documents; wherein the bonds are not stored as part of the searchable items; wherein the bonds do not reflect storage location relationships between said searchable items; receiving a search request to perform a search for searchable items that are associated with a keyword, wherein the search request specifies the keyword; determining that a particular searchable item is to be the starting point of the search; responding to said search request by performing the search relative to the particular searchable item that is to be used as a starting point for said search, wherein performing the search includes at least one of: (a) ranking documents that match said search based, at least in part, on the minimum number of bonds that have to be traversed from the particular searchable item to arrive at each document, wherein the documents that match said search include a first document and a second document, wherein the minimum number of bonds that have to be traversed from said particular searchable to arrive at said first document is different than the minimum number of bonds that have to be traversed from said particular searchable item to arrive at said second document; (b) searching against documents that are within one bond of said particular searchable item; and if the number of documents, within one bond of said particular searchable item, that match the search is less than a target number, then expanding the search to documents that are within two bonds of said particular searchable item; and repeatedly expanding the search to documents that are one further bond away from the particular searchable item until at least the target number of documents that match the search have been identified; or (c) sequentially performing a series of searches relative to said particular searchable item until a predetermined maximum number of bonds from said particular searchable item has been reached, wherein the series of searches includes a first search and one or more subsequent searches, wherein each search of said one or more subsequent searches is performed against documents that are at a greater minimum number of bonds that have to be traversed from said particular searchable item than the documents that were searched in any search that preceded said search in said series; wherein the method is performed by one or more computing devices. 4. The method of claim 1 wherein performing the search includes ranking the searchable items that match said search based, at least in part, on the minimum number of bonds that have to be traversed between the searchable items and the particular searchable item, wherein the documents that match said search include a first document and a second document, wherein the minimum number of bonds that have to be traversed from said particular searchable to arrive at said first document is different than the minimum number of bonds that have to be traversed from said particular searchable to arrive at said second document. | 0.5 |
7,822,762 | 12 | 13 | 12. The method of claim 11 , further comprising automatically gathering data associated with the entity based on, at least in part, an objective, goal, mission, task, or purpose of the entity. | 12. The method of claim 11 , further comprising automatically gathering data associated with the entity based on, at least in part, an objective, goal, mission, task, or purpose of the entity. 13. The method of claim 12 , further comprising automatically gathering the data associated with the entity from a plurality of sources. | 0.5 |
7,715,933 | 8 | 10 | 8. A recording medium comprising: a plurality of text sub data units included in text data to be presented with main data and recorded separately from the main data, the plurality of text sub data units being preloaded into a memory before presented and the preloaded text sub data units being processed based on attribute information which includes language information regarding a font of the text sub data units; and a navigation information file including navigation information for accessing the main data and including linking information that links the main data and the text sub data units, the navigation information file being separated from the text sub data units, wherein the navigation information file further includes language information specifying a language code of the text sub data units, and an index number of said text sub data units and, wherein the navigation information file further includes coding information for the main data and affecting the text data, and wherein the text data is processed based on the coding information. | 8. A recording medium comprising: a plurality of text sub data units included in text data to be presented with main data and recorded separately from the main data, the plurality of text sub data units being preloaded into a memory before presented and the preloaded text sub data units being processed based on attribute information which includes language information regarding a font of the text sub data units; and a navigation information file including navigation information for accessing the main data and including linking information that links the main data and the text sub data units, the navigation information file being separated from the text sub data units, wherein the navigation information file further includes language information specifying a language code of the text sub data units, and an index number of said text sub data units and, wherein the navigation information file further includes coding information for the main data and affecting the text data, and wherein the text data is processed based on the coding information. 10. The recording medium of claim 8 , wherein the main data includes audio data. | 0.924386 |
8,005,294 | 5 | 6 | 5. An unconstrained cursive character handwritten word recognition system, comprising a processor including: an image processing module operable to process an image of a handwritten word of one or more characters, wherein the processing of the imaged word includes segmenting the imaged word into a finite number of segments and determining a sequence of the segments using an over-segmentation-relabeling algorithm, wherein each character includes one or more consecutive segments; a feature extraction module operable to derive a feature vector to represent feature information of one segment or a combination of several consecutive segments; and a classification module operable to determine an optimal string of one or more characters as composing the imaged word, wherein the classification module uses a continuous-discrete hybrid probability modeling of features toy determine a final symbol probability of whether a given feature vector is indicative of a given distinct character, wherein, in the continuous-discrete hybrid probability modeling of N features, the features N are separated into a first group N 1 and a second group N 2 , features of the first group N 1 are distributed using a continuous probability model to obtain a continuous distribution probability measure, features of the second group N 2 , are distributed using a discrete probability model given by equation (1) b j ( O ) = ∏ i = 1 N 2 P ( s i ) ( 1 ) wherein, in Equation (1), b j (O) is the discrete symbol probability distribution of the features of the second group N 2 for an observation O composed of the one segment or the combination of several consecutive segments, wherein P(s i ) is the probability of s i , and s i is the i-th feature of the observation, and wherein the continuous distribution probability measure and the discrete distribution probability measure b j (O) are multiplied and normalized to obtain the final symbol probability of whether a given feature vector is indicative of a given distinct character. | 5. An unconstrained cursive character handwritten word recognition system, comprising a processor including: an image processing module operable to process an image of a handwritten word of one or more characters, wherein the processing of the imaged word includes segmenting the imaged word into a finite number of segments and determining a sequence of the segments using an over-segmentation-relabeling algorithm, wherein each character includes one or more consecutive segments; a feature extraction module operable to derive a feature vector to represent feature information of one segment or a combination of several consecutive segments; and a classification module operable to determine an optimal string of one or more characters as composing the imaged word, wherein the classification module uses a continuous-discrete hybrid probability modeling of features toy determine a final symbol probability of whether a given feature vector is indicative of a given distinct character, wherein, in the continuous-discrete hybrid probability modeling of N features, the features N are separated into a first group N 1 and a second group N 2 , features of the first group N 1 are distributed using a continuous probability model to obtain a continuous distribution probability measure, features of the second group N 2 , are distributed using a discrete probability model given by equation (1) b j ( O ) = ∏ i = 1 N 2 P ( s i ) ( 1 ) wherein, in Equation (1), b j (O) is the discrete symbol probability distribution of the features of the second group N 2 for an observation O composed of the one segment or the combination of several consecutive segments, wherein P(s i ) is the probability of s i , and s i is the i-th feature of the observation, and wherein the continuous distribution probability measure and the discrete distribution probability measure b j (O) are multiplied and normalized to obtain the final symbol probability of whether a given feature vector is indicative of a given distinct character. 6. The handwritten word recognition system of claim 5 , wherein the image processing module includes means for slant normalization and noise reduction. | 0.671739 |
10,157,195 | 2 | 3 | 2. The system of claim 1 , wherein the transformation engine comprises a computer programmed to execute object-oriented transformation rules stored in a memory. | 2. The system of claim 1 , wherein the transformation engine comprises a computer programmed to execute object-oriented transformation rules stored in a memory. 3. The system of claim 2 , wherein the computer is programmed to: use common transformation rules common to transform the extracted attribute data in the first format from the source system into the second format of the target system, wherein the transformation rules includes rules that are common to all subtype classes of a parent class associated with the target system used to populate attributes of a parent class and each common attribute of each subtype class inherited from the parent class, and use unique transformation rules to transform attribute data from the first format from the source system into attribute data having the second format of the target system, wherein the unique transformation rules are used to populate attributes of an object representing the subtype class, and repeating for each subtype class usage of the unique transformation rules to transform attribute data having the first format from the source system into attribute data having the second format of the target system, and wherein the unique transformation facilitates population of attribute data of the subtype class. | 0.5 |
7,587,308 | 1 | 16 | 1. A computer-implemented method executed by a processor that performs operations for reducing ambiguities present in electronically stored words, the operations comprising: receiving a plurality of characters in electronic form, the received plurality of characters corresponding to a sequence of words and including an ambiguous word that has one or more characters whose value is substantially uncertain; comparing at least some of the words in the sequence to a first ontology, the first ontology defining a plurality of nodes, each node being associated with a word, and each node being connected to at least one other node by a link, each link being associated with a concept that relates the words associated with the nodes connected by the link in a predetermined context, wherein at least some of the nodes are associated with non-textual image information that identifies an enhancement to character-based text. | 1. A computer-implemented method executed by a processor that performs operations for reducing ambiguities present in electronically stored words, the operations comprising: receiving a plurality of characters in electronic form, the received plurality of characters corresponding to a sequence of words and including an ambiguous word that has one or more characters whose value is substantially uncertain; comparing at least some of the words in the sequence to a first ontology, the first ontology defining a plurality of nodes, each node being associated with a word, and each node being connected to at least one other node by a link, each link being associated with a concept that relates the words associated with the nodes connected by the link in a predetermined context, wherein at least some of the nodes are associated with non-textual image information that identifies an enhancement to character-based text. 16. The method of claim 1 , wherein the non-textual image information that identifies an enhancement to character-based text comprises color information. | 0.938604 |
8,321,517 | 1 | 5 | 1. A method for processing emails, comprising: receiving a correction request including an identifier of an original email and an incorrect recipient; in response to the correction request, creating a correction record including the identifier of the original email and the incorrect recipient; in response to receiving relevant emails of the original email, determining whether recipients of the relevant emails include the incorrect recipient; in response to determining that a sender of the correction request is not the incorrect recipient, sending the correction request to the incorrect recipient; receiving from the incorrect recipient acknowledgment to the correction request; in response to receiving from the incorrect recipient the acknowledgment to the correction request, determining the correction record to be valid; and in response to determining that recipients of the relevant emails include the incorrect recipient and in response to determining the correction record to be valid, processing the relevant emails based on the correction record. | 1. A method for processing emails, comprising: receiving a correction request including an identifier of an original email and an incorrect recipient; in response to the correction request, creating a correction record including the identifier of the original email and the incorrect recipient; in response to receiving relevant emails of the original email, determining whether recipients of the relevant emails include the incorrect recipient; in response to determining that a sender of the correction request is not the incorrect recipient, sending the correction request to the incorrect recipient; receiving from the incorrect recipient acknowledgment to the correction request; in response to receiving from the incorrect recipient the acknowledgment to the correction request, determining the correction record to be valid; and in response to determining that recipients of the relevant emails include the incorrect recipient and in response to determining the correction record to be valid, processing the relevant emails based on the correction record. 5. The method according to claim 1 , wherein processing the relevant emails based on the correction record comprising: sending information related to the relevant emails to a correct recipient or a sender of the relevant emails based on the correction record. | 0.71663 |
7,945,581 | 28 | 32 | 28. The system of claim 27 , further comprising a query server operably connected to the master node and being adapted to: generate intermediary source code from query source code, the query source code representing at least one database operation using the database and wherein the query source code is formatted based in part on a query-based programming language; and compile the intermediary source code to generate the at least one executable. | 28. The system of claim 27 , further comprising a query server operably connected to the master node and being adapted to: generate intermediary source code from query source code, the query source code representing at least one database operation using the database and wherein the query source code is formatted based in part on a query-based programming language; and compile the intermediary source code to generate the at least one executable. 32. The system of claim 28 , further comprising a work-unit reporting module operably connected to the query server and being adapted to store at least a portion of the final query results. | 0.647388 |
4,454,610 | 1 | 7 | 1. Apparatus for the classification of patterns, comprising: unclassified representation generating means for generating a representation of at least part of a function of a position-invariant transform of each unclassified pattern presented thereto; transform function representation element value signal set producing means for producing from each of said unclassified representations a set of element value signals each of which represents the value of a property of one of a predetermined constellation of elements thereof; and correlating means for correlating each set of said transform function representation element value signals with at least one set of reference signals; said elements of said unclassified representations being selected in accordance with a statistical property of a plurality of reference representations each of which represents at least part of said function of said position-invariant transform of a reference pattern, and each one of said plurality of reference patterns being a member of a subset of said plurality of reference patterns all of the members of which are cosignificative and also being a member of a subset of said plurality of reference patterns all of the members of which are co-original. | 1. Apparatus for the classification of patterns, comprising: unclassified representation generating means for generating a representation of at least part of a function of a position-invariant transform of each unclassified pattern presented thereto; transform function representation element value signal set producing means for producing from each of said unclassified representations a set of element value signals each of which represents the value of a property of one of a predetermined constellation of elements thereof; and correlating means for correlating each set of said transform function representation element value signals with at least one set of reference signals; said elements of said unclassified representations being selected in accordance with a statistical property of a plurality of reference representations each of which represents at least part of said function of said position-invariant transform of a reference pattern, and each one of said plurality of reference patterns being a member of a subset of said plurality of reference patterns all of the members of which are cosignificative and also being a member of a subset of said plurality of reference patterns all of the members of which are co-original. 7. Apparatus for the classification of patterns as claimed in claim 1 in which said statistical property is the signification variance matrix of said plurality of reference representations. | 0.505236 |
9,249,287 | 1 | 2 | 1. A document evaluation apparatus, realized by a computer, for evaluating a document using a set of sample documents having a first feature vector consisting of a plurality of feature values and evaluation values of the documents, comprising: a processor, wherein the processor is configured to classify the set of sample documents, based on a missing pattern indicating a set of indices whose feature values are missing in the first feature vector; use feature values that are not missing in the first feature vector and the evaluation values to learn, for each classification, a first function for calculating a first score which is a weighted evaluation value for each classification; compute a feature value corresponding to each classification using the first score, and generate a second feature vector having the computed feature values; use the second feature vector and the evaluation values to learn a second function for calculating a second score for evaluating a document targeted for evaluation; and measure an appearance frequency of the sample documents for each missing pattern, based on the set of sample documents, and match a missing pattern whose appearance frequency is less than or equal to a set threshold to a missing pattern that is most similar to said missing pattern and whose appearance frequency is greater than the threshold. | 1. A document evaluation apparatus, realized by a computer, for evaluating a document using a set of sample documents having a first feature vector consisting of a plurality of feature values and evaluation values of the documents, comprising: a processor, wherein the processor is configured to classify the set of sample documents, based on a missing pattern indicating a set of indices whose feature values are missing in the first feature vector; use feature values that are not missing in the first feature vector and the evaluation values to learn, for each classification, a first function for calculating a first score which is a weighted evaluation value for each classification; compute a feature value corresponding to each classification using the first score, and generate a second feature vector having the computed feature values; use the second feature vector and the evaluation values to learn a second function for calculating a second score for evaluating a document targeted for evaluation; and measure an appearance frequency of the sample documents for each missing pattern, based on the set of sample documents, and match a missing pattern whose appearance frequency is less than or equal to a set threshold to a missing pattern that is most similar to said missing pattern and whose appearance frequency is greater than the threshold. 2. The document evaluation apparatus according to claim 1 , wherein the processor is further configured to receive input of a set of documents targeted for evaluation, compute the second score for each document based on the second function, and rank the documents based on the second scores. | 0.5 |
7,698,080 | 32 | 34 | 32. The method of claim 1 , wherein determining whether sample identity information can be obtained comprises: determining, for each candidate, a range of expected values of the measured spectral information based on the reference information; determining a range of measurement values based on the measured spectral information and an estimate of a variability of the measured spectral information; and determining, for each candidate, an overlap between the range of expected values of the measured spectral information and the range of measurement values. | 32. The method of claim 1 , wherein determining whether sample identity information can be obtained comprises: determining, for each candidate, a range of expected values of the measured spectral information based on the reference information; determining a range of measurement values based on the measured spectral information and an estimate of a variability of the measured spectral information; and determining, for each candidate, an overlap between the range of expected values of the measured spectral information and the range of measurement values. 34. The method of claim 32 , wherein the sample identity information comprises an identification of the sample as a mixture of components if a non-zero overlap exists between the range of expected values and the measurement values for more than one of the candidates. | 0.5 |
7,885,904 | 1 | 2 | 1. A user-interface method of selecting and presenting a subset of content items of a first dataspace in which at least one content item of the subset is selected at least in part based on content preferences of the user learned from the user selecting content of a second dataspace, the method comprising: providing access to a first collection of content items of a first dataspace, each content item of the first collection having at least one associated descriptive term to describe the content item; providing access to a second collection of content items of a second dataspace, each content item of the second collection having at least one associated descriptive term to describe the content item; receiving selection actions of content items of the second collection from the user; a computer system determining a user preference signature by analyzing the descriptive terms of the selected content items of the second collection to learn the content preferences of the user for the content of the second dataspace; determining a relationship between the content items of the first dataspace and the content items of the second dataspace, the relationship defining which learned user content preferences of the user preference signature are relevant to the content items of the first dataspace; and subsequent to learning the content preferences of the user, selecting and presenting to the user at least one content item of the first dataspace based on the learned content preferences of the user determined to be relevant to the content items of the first dataspace. | 1. A user-interface method of selecting and presenting a subset of content items of a first dataspace in which at least one content item of the subset is selected at least in part based on content preferences of the user learned from the user selecting content of a second dataspace, the method comprising: providing access to a first collection of content items of a first dataspace, each content item of the first collection having at least one associated descriptive term to describe the content item; providing access to a second collection of content items of a second dataspace, each content item of the second collection having at least one associated descriptive term to describe the content item; receiving selection actions of content items of the second collection from the user; a computer system determining a user preference signature by analyzing the descriptive terms of the selected content items of the second collection to learn the content preferences of the user for the content of the second dataspace; determining a relationship between the content items of the first dataspace and the content items of the second dataspace, the relationship defining which learned user content preferences of the user preference signature are relevant to the content items of the first dataspace; and subsequent to learning the content preferences of the user, selecting and presenting to the user at least one content item of the first dataspace based on the learned content preferences of the user determined to be relevant to the content items of the first dataspace. 2. The method of claim 1 , further comprising: analyzing the descriptive terms associated with the content items of the second collection selected by the user to identify sets of actions resulting in the selection of similar content items, wherein similarity is determined by comparing the descriptive terms associated with any one of the selected content items with any of the previously selected content items; and analyzing the date, day, and time of at least two of the individual selection actions of the sets of actions to learn periodicities of the user actions resulting in the selections of similar content items, wherein the periodicity corresponding to a particular set of actions for selecting similar content items indicates the amount of time between the user selection actions of the set; wherein the selecting of the at least one content item of the first dataspace for presentation to the user is further based on at least one of the learned periodicities of the sets of actions resulting in the selection of similar content items. | 0.5 |
7,509,346 | 9 | 10 | 9. The system of claim 1 , the association component utilizes a match confidence to associate the shape to at least one type. | 9. The system of claim 1 , the association component utilizes a match confidence to associate the shape to at least one type. 10. The system of claim 9 , the match confidence utilizes an adjustable threshold to determine valid matches to utilize. | 0.62963 |
7,761,700 | 2 | 3 | 2. The system of claim 1 , wherein the means for providing the markup language code further comprises means for providing user data, the user data being operatively associated with the user logon process. | 2. The system of claim 1 , wherein the means for providing the markup language code further comprises means for providing user data, the user data being operatively associated with the user logon process. 3. The system of claim 2 , wherein the user data includes data selected from a set comprising a list of users, a text identifier, a graphical identifier, a password enabled identifier, and password hint data, and related user information data. | 0.5 |
9,990,582 | 10 | 11 | 10. The information processing system of claim 9 , wherein: the first cognitive graph vector comprises a plurality of first cognitive graph vector indices extending along the first cognitive graph vector away from a cognitive graph nexus; the second cognitive graph vector comprises a plurality of second cognitive graph vector indices extending along the second cognitive graph vector away from the cognitive graph nexus; the third cognitive graph vector comprises a plurality of third cognitive graph vector indices extending along the third cognitive graph vector away from the cognitive graph nexus; the limitation comprises limiting the first set of data to data within a first certain index of the plurality of first cognitive graph vector indices; the refining comprising limiting the second set of data to data within a second certain index of the second cognitive graph vector indices and data within a third; and the refining further comprising limiting the third set of data to data within a third certain index of the third cognitive graph vector indices. | 10. The information processing system of claim 9 , wherein: the first cognitive graph vector comprises a plurality of first cognitive graph vector indices extending along the first cognitive graph vector away from a cognitive graph nexus; the second cognitive graph vector comprises a plurality of second cognitive graph vector indices extending along the second cognitive graph vector away from the cognitive graph nexus; the third cognitive graph vector comprises a plurality of third cognitive graph vector indices extending along the third cognitive graph vector away from the cognitive graph nexus; the limitation comprises limiting the first set of data to data within a first certain index of the plurality of first cognitive graph vector indices; the refining comprising limiting the second set of data to data within a second certain index of the second cognitive graph vector indices and data within a third; and the refining further comprising limiting the third set of data to data within a third certain index of the third cognitive graph vector indices. 11. The information processing system of claim 10 , wherein: at least some of the first cognitive graph vector indices, second cognitive graph vector indices and third vector graph indices are different magnitudes. | 0.513636 |
8,694,444 | 7 | 8 | 7. The apparatus of claim 1 wherein the machine learning method includes repeating the MT-DT learning operation for different subsets of a training set to generate a set of learned MT-DT's, and the constructing comprises: constructing the MT predictor as a weighted combination of outputs of the set of MT-DT's. | 7. The apparatus of claim 1 wherein the machine learning method includes repeating the MT-DT learning operation for different subsets of a training set to generate a set of learned MT-DT's, and the constructing comprises: constructing the MT predictor as a weighted combination of outputs of the set of MT-DT's. 8. The apparatus of claim 7 wherein the constructing includes: combining the outputs of the MT-DT's using a multi-task adaptive boosting (MT-AdaBoost) algorithm. | 0.5 |
7,620,896 | 1 | 3 | 1. A computer implemented method for adding an intelligent agenda to a plurality of slides in a slide presentation program stored in a memory connected to a computer, the computer implemented method comprising: loading a configuration program and an intelligent agenda program into the memory, wherein the intelligent agenda program is adapted to interface with the slide presentation program and to respond to a plurality of user inputs to a graphical user interface of the configuration program; responsive to a user invoking an options menu on the slide presentation program and selecting an intelligent agenda option on the options menu, creating an intelligent agenda for the plurality of slides by accessing a plurality of titles from the plurality of slides in the slide presentation program to create an outline, adapting the outline to be displayed in a corner on each slide of the plurality of slides, and further adapting the outline to track a user's progression through a presentation of the plurality of slides by a pointer that automatically moves to a title in the outline corresponding to a currently displayed slide; further responsive to the user selecting the intelligent agenda option on the options menu, displaying the graphical user interface on a display of the computer; responsive to a first set of the plurality of user inputs to the graphical user interface, introducing a user configurable line to each slide of the plurality of slides, the user configurable line connecting to each of two contiguous border lines of each slide of the plurality of slides to define a corner section containing the outline on each slide of the plurality of slides; and responsive to a second set of the plurality of user inputs to the graphical user interface, limiting a number of displayed lines of the outline to a line limitation and allowing the user to select either a first display option or a second display option, wherein, when the user selects the first display option, the intelligent agenda is modified to display only the title in the outline corresponding to the currently displayed slide, a preceding title located immediately before the title, and a following title located immediately after the title, and when the user selects the second display option, the intelligent agenda is modified to display a portion of the plurality of titles that precede the title subject to the line limitation, and then only when the line limitation has not been met, displaying a portion of the plurality of titles that follow the title until the line limitation is met. | 1. A computer implemented method for adding an intelligent agenda to a plurality of slides in a slide presentation program stored in a memory connected to a computer, the computer implemented method comprising: loading a configuration program and an intelligent agenda program into the memory, wherein the intelligent agenda program is adapted to interface with the slide presentation program and to respond to a plurality of user inputs to a graphical user interface of the configuration program; responsive to a user invoking an options menu on the slide presentation program and selecting an intelligent agenda option on the options menu, creating an intelligent agenda for the plurality of slides by accessing a plurality of titles from the plurality of slides in the slide presentation program to create an outline, adapting the outline to be displayed in a corner on each slide of the plurality of slides, and further adapting the outline to track a user's progression through a presentation of the plurality of slides by a pointer that automatically moves to a title in the outline corresponding to a currently displayed slide; further responsive to the user selecting the intelligent agenda option on the options menu, displaying the graphical user interface on a display of the computer; responsive to a first set of the plurality of user inputs to the graphical user interface, introducing a user configurable line to each slide of the plurality of slides, the user configurable line connecting to each of two contiguous border lines of each slide of the plurality of slides to define a corner section containing the outline on each slide of the plurality of slides; and responsive to a second set of the plurality of user inputs to the graphical user interface, limiting a number of displayed lines of the outline to a line limitation and allowing the user to select either a first display option or a second display option, wherein, when the user selects the first display option, the intelligent agenda is modified to display only the title in the outline corresponding to the currently displayed slide, a preceding title located immediately before the title, and a following title located immediately after the title, and when the user selects the second display option, the intelligent agenda is modified to display a portion of the plurality of titles that precede the title subject to the line limitation, and then only when the line limitation has not been met, displaying a portion of the plurality of titles that follow the title until the line limitation is met. 3. The computer implemented method of claim 1 , where the intelligent agenda program is further adapted to modify the outline to display a duration associated with each slide of the plurality of slides represented within the outline via the graphical user interface on the display of the computer as a portion of the outline. | 0.622093 |
8,914,376 | 7 | 8 | 7. The method of claim 1 , the method also comprising: performing a plurality of machine learning iterations on said N electronic documents wherein said iterations determine relevance of documents to at least one issue in said set of issues; determining at least one relevance determination quality criterion characterizing current relevance determination performance; and estimating a cost effectiveness of continued iterations on said at least N electronic documents vs. termination thereof. | 7. The method of claim 1 , the method also comprising: performing a plurality of machine learning iterations on said N electronic documents wherein said iterations determine relevance of documents to at least one issue in said set of issues; determining at least one relevance determination quality criterion characterizing current relevance determination performance; and estimating a cost effectiveness of continued iterations on said at least N electronic documents vs. termination thereof. 8. The method according to claim 7 wherein said estimating includes estimating at least one relevance determination quality criterion of future relevance determination performance assuming continued iterations. | 0.709945 |
4,639,877 | 15 | 16 | 15. A computer system in accordance with claim 14 wherein said speech processor includes: a time delay mechanism within said processor control programs and logic arranged to cause said speech processor to pause for a variable length of time in response to the scanning of certain patterns of the command data entries within said command memory. | 15. A computer system in accordance with claim 14 wherein said speech processor includes: a time delay mechanism within said processor control programs and logic arranged to cause said speech processor to pause for a variable length of time in response to the scanning of certain patterns of the command data entries within said command memory. 16. A computer system in accordance with claim 15 wherein said time delay mechanism includes: a counting mechanism that is initialized to a count value equal to the numeric value of at least a portion of one of said command data entries in said command memory and that counts the passage of a number of fixed-length time intervals equal to the count value. | 0.5 |
7,970,773 | 5 | 6 | 5. The computer system of claim 1 , wherein aligning the text of the documents of the identified set of similar documents to identify potential variation-phrase pairs comprises determining an individual score for each potential variation-phrase pair according to the alignment of the first and second documents from which each potential variation-phrase pair was derived. | 5. The computer system of claim 1 , wherein aligning the text of the documents of the identified set of similar documents to identify potential variation-phrase pairs comprises determining an individual score for each potential variation-phrase pair according to the alignment of the first and second documents from which each potential variation-phrase pair was derived. 6. The computer system of claim 5 , wherein the computer system is further configured to generate a cumulative score for each of the potential variation-phrase pairs in the variation-phrase set according to the individual scores of each potential variation-phrase pair from each first and second document in the document corpus having the potential variation-phrase pair. | 0.5 |
9,690,982 | 10 | 12 | 10. A system for using self-referential movement data compressed by principal joint variable analysis to identify a movement of a human object or a non human object relating to betting or game activity, the system comprising: a classifier configured to receive a stream of reference frames from a detector unit, the stream of reference frames comprising a set of self-referential movement data points provided in three dimensions, each self-referential movement data point identifying locations or positions of one or more parts of a body of the human object or the non human object with respect to a reference point on the body of the human object or the non human object with respect to a particular dimension of the three dimensions; the classifier configured to determine that a subset of the set of self-referential movement data points is sufficient to recognize a reference movement relating to betting or game activity; the classifier configured to generate a feature matrix, each row of the feature matrix (i) representative of a particular location or position of the one or more parts of the body, and (ii) having at least three cells, each cell storing a self-referential movement data point of the set of self-referential movement data points corresponding to the particular location or position of the one or more parts of the body in one of the three dimensions; the classifier configured to transform the feature matrix into a compressed feature matrix using a principal joint variable analysis function at a pre-defined variance threshold, collapsing the feature matrix by reducing the three-dimensional data set into a two-dimensional data set or a single-dimensional data set, the compressed feature matrix maintaining only the rows of the feature matrix having a corresponding variance greater than the pre-defined variance threshold; a data storage configured to store the compressed feature matrix representative of the reference movement; and a recognizer configured to receive a new stream of frames including new self-referential movement data points, each new self-referential movement data point identifying a location of a part of a body of a new human object or the new non human object with respect to the reference point on the body of the new human object or the new non human object and recognizing that the movement of the new human object or the new non human object corresponds to the reference movement when the new self-referential movement data points corresponding to the compressed feature matrix only vary from the data set of the compressed feature matrix within a pre-defined recognition threshold. | 10. A system for using self-referential movement data compressed by principal joint variable analysis to identify a movement of a human object or a non human object relating to betting or game activity, the system comprising: a classifier configured to receive a stream of reference frames from a detector unit, the stream of reference frames comprising a set of self-referential movement data points provided in three dimensions, each self-referential movement data point identifying locations or positions of one or more parts of a body of the human object or the non human object with respect to a reference point on the body of the human object or the non human object with respect to a particular dimension of the three dimensions; the classifier configured to determine that a subset of the set of self-referential movement data points is sufficient to recognize a reference movement relating to betting or game activity; the classifier configured to generate a feature matrix, each row of the feature matrix (i) representative of a particular location or position of the one or more parts of the body, and (ii) having at least three cells, each cell storing a self-referential movement data point of the set of self-referential movement data points corresponding to the particular location or position of the one or more parts of the body in one of the three dimensions; the classifier configured to transform the feature matrix into a compressed feature matrix using a principal joint variable analysis function at a pre-defined variance threshold, collapsing the feature matrix by reducing the three-dimensional data set into a two-dimensional data set or a single-dimensional data set, the compressed feature matrix maintaining only the rows of the feature matrix having a corresponding variance greater than the pre-defined variance threshold; a data storage configured to store the compressed feature matrix representative of the reference movement; and a recognizer configured to receive a new stream of frames including new self-referential movement data points, each new self-referential movement data point identifying a location of a part of a body of a new human object or the new non human object with respect to the reference point on the body of the new human object or the new non human object and recognizing that the movement of the new human object or the new non human object corresponds to the reference movement when the new self-referential movement data points corresponding to the compressed feature matrix only vary from the data set of the compressed feature matrix within a pre-defined recognition threshold. 12. The system of claim 10 , wherein the recognizer further identifies, within a second threshold of greater accuracy than the first threshold of accuracy, that at least a second new self-referential movement data point matches at least a second self-referential movement data point of the compressed feature matrix. | 0.721831 |
8,396,582 | 1 | 10 | 1. An autonomous biologically based learning system, comprising: a manufacturing tool that produces an asset; a drift component that modifies a manufacturing recipe processed by the manufacturing tool at least in part using a set of driving variables and a predetermined probability distribution function to generate one or more adjusted manufacturing recipes for the asset, wherein the set of driving variables determine a particular sequence to modify a set of recipe parameters associated with the manufacturing recipe; an objective autonomous learning engine that infers one or more functions for the manufacturing tool based on the modified manufacturing recipe processed by the manufacturing tool, wherein the one or more functions predict asset output metrics for the produced asset; and an autonomous optimization engine that extracts a set of updated recipe parameters from a set of input measurements and the one or more inferred functions to generate an adjusted recipe within a predefined tolerance of a target value for the asset output metrics. | 1. An autonomous biologically based learning system, comprising: a manufacturing tool that produces an asset; a drift component that modifies a manufacturing recipe processed by the manufacturing tool at least in part using a set of driving variables and a predetermined probability distribution function to generate one or more adjusted manufacturing recipes for the asset, wherein the set of driving variables determine a particular sequence to modify a set of recipe parameters associated with the manufacturing recipe; an objective autonomous learning engine that infers one or more functions for the manufacturing tool based on the modified manufacturing recipe processed by the manufacturing tool, wherein the one or more functions predict asset output metrics for the produced asset; and an autonomous optimization engine that extracts a set of updated recipe parameters from a set of input measurements and the one or more inferred functions to generate an adjusted recipe within a predefined tolerance of a target value for the asset output metrics. 10. The system of claim 1 , further comprising a component that renders a user interface to configure at least one of the set of driving variables. | 0.839168 |
8,468,146 | 1 | 7 | 1. A computer implemented method for creating a search index on cloud database, the method comprising the steps of: providing one or more inputs for creating a plurality of indexes on documents stored in the cloud database, wherein the one or more inputs include at least in part a first value representing number of documents to be assigned a single index; determining total number of documents stored in the cloud database, wherein the total number of documents is represented by a second value; estimating total number of indexes to be created based on the first value and the second value; executing a loop to create plurality of indexes on documents for a predetermined number of iterations, wherein the predetermined number of iterations correspond to the estimated value; and merging the plurality of indexes to create a single index, wherein the single index facilitates a user to search the documents stored in the cloud database. | 1. A computer implemented method for creating a search index on cloud database, the method comprising the steps of: providing one or more inputs for creating a plurality of indexes on documents stored in the cloud database, wherein the one or more inputs include at least in part a first value representing number of documents to be assigned a single index; determining total number of documents stored in the cloud database, wherein the total number of documents is represented by a second value; estimating total number of indexes to be created based on the first value and the second value; executing a loop to create plurality of indexes on documents for a predetermined number of iterations, wherein the predetermined number of iterations correspond to the estimated value; and merging the plurality of indexes to create a single index, wherein the single index facilitates a user to search the documents stored in the cloud database. 7. The computer implemented method of claim 1 , wherein the loop includes one or more inputs, the one or more inputs being the database name representing the database, start key and end key associated with the documents stored in the database. | 0.676862 |
8,732,479 | 1 | 9 | 1. In a client system comprising at least one processor, at least one memory, and at least one communication interface, a computer-implemented method for backing up a user file stored in the at least one memory, the method comprising: A) generating, via the at least one processor of the client system, a plurality of file segments each corresponding to a portion of the user file; B) encrypting, via the at least one processor, each of the plurality of file segments; C) determining, via the at least one processor, mapping information and storage identifying information for each of the plurality of encrypted file segments, the mapping information comprising a location address in storage of a second system, different from the client system, where the corresponding encrypted file segment will be stored; D) updating, via the at least one processor, a backup status file associated with the user file with the plurality of mapping information and storage identifying information for each of the corresponding plurality of encrypted file segments; E) transmitting the plurality of encrypted file segments to the second system for backup, while keeping metadata of the user file at the client device in the backup status file; and F) subsequently retrieving the plurality of encrypted file segments from the second system for restoration, the encrypted file segments requested via the mapping information and storage identifying information in the backup status file, the metadata used to structurally reconstruct the client file system. | 1. In a client system comprising at least one processor, at least one memory, and at least one communication interface, a computer-implemented method for backing up a user file stored in the at least one memory, the method comprising: A) generating, via the at least one processor of the client system, a plurality of file segments each corresponding to a portion of the user file; B) encrypting, via the at least one processor, each of the plurality of file segments; C) determining, via the at least one processor, mapping information and storage identifying information for each of the plurality of encrypted file segments, the mapping information comprising a location address in storage of a second system, different from the client system, where the corresponding encrypted file segment will be stored; D) updating, via the at least one processor, a backup status file associated with the user file with the plurality of mapping information and storage identifying information for each of the corresponding plurality of encrypted file segments; E) transmitting the plurality of encrypted file segments to the second system for backup, while keeping metadata of the user file at the client device in the backup status file; and F) subsequently retrieving the plurality of encrypted file segments from the second system for restoration, the encrypted file segments requested via the mapping information and storage identifying information in the backup status file, the metadata used to structurally reconstruct the client file system. 9. The method of claim 1 , wherein a first set of the plurality of file segments are stored on a first storage volume, and a second set of the plurality of file segments are stored on a second storage volume, and wherein the mapping information for each file segment identifies the storage volume for each file segment. | 0.5 |
7,966,352 | 17 | 18 | 17. A computer-implemented method of context harvesting from selected content using a computer system having a processor, memory, and data storage subsystems, the method: receiving a path drawn on a display via user input, the drawn path defining boundaries of a selected on-screen region of the display, the selected on-screen region comprising a plurality of pixels, wherein a displayed content of the selected on-screen region includes textual data and underlying data; capturing the plurality of pixels and associated underlying text and associated links to embedded information of the on-screen region by capturing only complete characters or words; storing the captured pixels as an image file; automatically extracting a character or word from the textual data and extracting complete sentences based upon punctuation as context information in response to determining that the displayed content of the on-screen region includes the textual data via the computer system; pointing a first pointer from the context information to the displayed content; automatically extracting a property of the underlying data as additional context information in response to determining that the displayed content of the on-screen region includes the underlying data via the computer system, the property comprising at least one of: a file name, a file identifier, a uniform resource locator (URL), a uniform resource identifier (URI), a folder name, or meta-data; determining a window associated with the selected on-screen region, and automatically extracting a uniform resource identifier (URI) from a name property or a value property of the window as additional context information; pointing a second pointer from the additional context information to the displayed content; and storing the extracted context information and additional context information in association with the image file via the data storage subsystem, such that the context information is accessible when viewing the image file. | 17. A computer-implemented method of context harvesting from selected content using a computer system having a processor, memory, and data storage subsystems, the method: receiving a path drawn on a display via user input, the drawn path defining boundaries of a selected on-screen region of the display, the selected on-screen region comprising a plurality of pixels, wherein a displayed content of the selected on-screen region includes textual data and underlying data; capturing the plurality of pixels and associated underlying text and associated links to embedded information of the on-screen region by capturing only complete characters or words; storing the captured pixels as an image file; automatically extracting a character or word from the textual data and extracting complete sentences based upon punctuation as context information in response to determining that the displayed content of the on-screen region includes the textual data via the computer system; pointing a first pointer from the context information to the displayed content; automatically extracting a property of the underlying data as additional context information in response to determining that the displayed content of the on-screen region includes the underlying data via the computer system, the property comprising at least one of: a file name, a file identifier, a uniform resource locator (URL), a uniform resource identifier (URI), a folder name, or meta-data; determining a window associated with the selected on-screen region, and automatically extracting a uniform resource identifier (URI) from a name property or a value property of the window as additional context information; pointing a second pointer from the additional context information to the displayed content; and storing the extracted context information and additional context information in association with the image file via the data storage subsystem, such that the context information is accessible when viewing the image file. 18. The computer-implemented method of claim 17 , further comprising: digitizing movements of a stylus across the display in order to receive an annotation; and obtaining additional context information based on the received annotation, the additional context information being automatically stored in association with the image file. | 0.5 |
8,825,478 | 4 | 6 | 4. A computer program product for visualizing content of a meeting having a plurality of segments, the computer program product comprising: one or more computer-readable tangible storage devices and program instructions stored on at least one of the one or more storage devices, the program instructions comprising: program instructions to receive audio content and supplemental content; for each segment of the plurality of segments of the meeting: program instructions to mark a starting point of the segment; program instructions to convert the audio content into text with speech recognition software; program instructions to generate a word cloud summarizing the text from the segment; program instructions to include in the word cloud one or more text words from the supplemental content; and program instructions to identify an ending point of the segment. | 4. A computer program product for visualizing content of a meeting having a plurality of segments, the computer program product comprising: one or more computer-readable tangible storage devices and program instructions stored on at least one of the one or more storage devices, the program instructions comprising: program instructions to receive audio content and supplemental content; for each segment of the plurality of segments of the meeting: program instructions to mark a starting point of the segment; program instructions to convert the audio content into text with speech recognition software; program instructions to generate a word cloud summarizing the text from the segment; program instructions to include in the word cloud one or more text words from the supplemental content; and program instructions to identify an ending point of the segment. 6. The computer program product of claim 4 wherein the supplemental content consists of at least one of: an agenda related to the audio content, speaker notes, and presentation material related to the audio content. | 0.878256 |
8,092,501 | 4 | 5 | 4. The spinal rod of claim 3 , wherein the ball bore is threaded and adapted to secure the ball-shaped mount to a threaded post of a bone anchor. | 4. The spinal rod of claim 3 , wherein the ball bore is threaded and adapted to secure the ball-shaped mount to a threaded post of a bone anchor. 5. The spinal rod of claim 4 , wherein the ball-shaped mount has a tool engagement feature which is adapted to permit the ball-shaped mount to engage and be turned by a tool to secure the ball bore to a threaded post of a bone anchor. | 0.5 |
8,290,960 | 1 | 2 | 1. A computer-implemented method comprising: selecting a trust factor from a plurality of trust factors included in a trust index repository, the plurality of trust factors based on a plurality of trust-based needs corresponding to an organization; receiving a trust metaphor to associate with the selected trust factor, wherein the trust metaphor includes a plurality of context values; receiving one or more range values; associating the trust metaphor, context values, and range values with the selected trust factor; receiving, from a trust data consumer, a request corresponding to a trust factor metadata score that is associated with the selected trust factor; retrieving the trust factor metadata score, the trust factor metadata score corresponding to a plurality of fact data that includes one or more selected data sources and one or more selected facts; matching the trust factor metadata score with one of the plurality of one or more range values, the matching resulting in one of the context values being selected based on the retrieved trust factor metadata score; and providing the selected context value to the data consumer. | 1. A computer-implemented method comprising: selecting a trust factor from a plurality of trust factors included in a trust index repository, the plurality of trust factors based on a plurality of trust-based needs corresponding to an organization; receiving a trust metaphor to associate with the selected trust factor, wherein the trust metaphor includes a plurality of context values; receiving one or more range values; associating the trust metaphor, context values, and range values with the selected trust factor; receiving, from a trust data consumer, a request corresponding to a trust factor metadata score that is associated with the selected trust factor; retrieving the trust factor metadata score, the trust factor metadata score corresponding to a plurality of fact data that includes one or more selected data sources and one or more selected facts; matching the trust factor metadata score with one of the plurality of one or more range values, the matching resulting in one of the context values being selected based on the retrieved trust factor metadata score; and providing the selected context value to the data consumer. 2. The method of claim 1 wherein at least one of the selected trust factor is a column-based trust factor, wherein the method further comprises: selecting a column of data from a data source included in the trust index repository; analyzing the selected column of data using the column-based trust factor, the analyzing resulting in a column-based atomic trust factor score that is used in the matching. | 0.5 |
9,152,388 | 6 | 9 | 6. A system comprising: at least one programmable processor; and a machine-readable medium storing instructions that, when executed by the at least one programmable processor, cause the at least one programmable processor to perform operations comprising: receiving a user scripting input via a script editor displayed to a user on a displayed of a computing device, the user scripting input comprising a script using defining a subset language of a standardized scripting language, the subset language being simplified relative to the standardized scripting language while retaining a syntax of the standardized scripting language and comprising a tailored grammar matching features of the subset language without being a subset of a grammar of the standardized scripting language, each of the subset language features being defined within the tailored grammar of the subset to be compatible with a specification of the standardized scripting language, the user scripting input creating a user interface feature accessing data in one or more objects; determining a type for a variable entered as part of the scripting input during input of a character string of the scripting input, the determining comprising use of a subset-specific type system for the subset of the standardized scripting language, the subset-specific type system providing a type inference capability that accesses information about an underlying data structure of the one or more objects, the subset-specific type system adding to a defined type system for the standardized scripting language; and querying a type library to display to the user assistance information for resolving the character string to a correct type and description compatible with the underlying data structure. | 6. A system comprising: at least one programmable processor; and a machine-readable medium storing instructions that, when executed by the at least one programmable processor, cause the at least one programmable processor to perform operations comprising: receiving a user scripting input via a script editor displayed to a user on a displayed of a computing device, the user scripting input comprising a script using defining a subset language of a standardized scripting language, the subset language being simplified relative to the standardized scripting language while retaining a syntax of the standardized scripting language and comprising a tailored grammar matching features of the subset language without being a subset of a grammar of the standardized scripting language, each of the subset language features being defined within the tailored grammar of the subset to be compatible with a specification of the standardized scripting language, the user scripting input creating a user interface feature accessing data in one or more objects; determining a type for a variable entered as part of the scripting input during input of a character string of the scripting input, the determining comprising use of a subset-specific type system for the subset of the standardized scripting language, the subset-specific type system providing a type inference capability that accesses information about an underlying data structure of the one or more objects, the subset-specific type system adding to a defined type system for the standardized scripting language; and querying a type library to display to the user assistance information for resolving the character string to a correct type and description compatible with the underlying data structure. 9. A system as in claim 6 , wherein the user interface feature comprises one or more of a chart and a table. | 0.72449 |
8,312,038 | 1 | 6 | 1. A method for programmatically providing a user interface for forming a query, comprising: displaying a first row of query criteria, wherein the query criteria comprises a first operand, a second operand, and a comparative operator for comparing the first operand with the second operand; displaying a second row of query criteria, the second row being logically connected to the first row by a first Boolean connector; displaying a third row of query criteria, the third row being logically connected to the second row by a second Boolean connector; displaying a fourth row of query criteria; displaying the second row and third row together in a bounded area, wherein the bounded area indicates that the second row and the third row are on a child level from the first row, and that the query criteria from the second row and third row will be evaluated together before the query criteria from the first row; in response to a user drag-and-drop operation comprising moving the fourth row within the bounded area, nesting the fourth row of query criteria with the second row and third row of query criteria; and executing a query comprising evaluating the query criteria from the second row, third row, and fourth row together before evaluating the query criteria from the first row, wherein nested query criteria are evaluated before query criteria that are not nested. | 1. A method for programmatically providing a user interface for forming a query, comprising: displaying a first row of query criteria, wherein the query criteria comprises a first operand, a second operand, and a comparative operator for comparing the first operand with the second operand; displaying a second row of query criteria, the second row being logically connected to the first row by a first Boolean connector; displaying a third row of query criteria, the third row being logically connected to the second row by a second Boolean connector; displaying a fourth row of query criteria; displaying the second row and third row together in a bounded area, wherein the bounded area indicates that the second row and the third row are on a child level from the first row, and that the query criteria from the second row and third row will be evaluated together before the query criteria from the first row; in response to a user drag-and-drop operation comprising moving the fourth row within the bounded area, nesting the fourth row of query criteria with the second row and third row of query criteria; and executing a query comprising evaluating the query criteria from the second row, third row, and fourth row together before evaluating the query criteria from the first row, wherein nested query criteria are evaluated before query criteria that are not nested. 6. The method of claim 1 , further comprising displaying a delete button in each of the first, second and third rows, wherein, in response to a user selection of the delete button, the corresponding row is removed. | 0.5 |
9,910,887 | 1 | 14 | 1. A method comprising, by one or more computing devices: receiving, from a client system of a first user of an online social network, a search query input comprising a character string having a first number of characters; accessing one or more verticals, each vertical being external to the client system and storing one or more objects of a particular object-type of a plurality of object types associated with the online social network, wherein: if the first number is less than or equal to a first threshold number, then accessing one or more first verticals, wherein each first vertical stores objects of a first object-type; and if the first number is greater than the first threshold number, then accessing the one or more first verticals and one or more second verticals, wherein each second vertical stores objects of a second object-type different than the first object-type; searching each accessed vertical to identify one or more objects associated with the vertical that substantially match the character string; and sending, to the client system of the first user, one or more references to one or more of the identified objects, respectively. | 1. A method comprising, by one or more computing devices: receiving, from a client system of a first user of an online social network, a search query input comprising a character string having a first number of characters; accessing one or more verticals, each vertical being external to the client system and storing one or more objects of a particular object-type of a plurality of object types associated with the online social network, wherein: if the first number is less than or equal to a first threshold number, then accessing one or more first verticals, wherein each first vertical stores objects of a first object-type; and if the first number is greater than the first threshold number, then accessing the one or more first verticals and one or more second verticals, wherein each second vertical stores objects of a second object-type different than the first object-type; searching each accessed vertical to identify one or more objects associated with the vertical that substantially match the character string; and sending, to the client system of the first user, one or more references to one or more of the identified objects, respectively. 14. The method of claim 1 , further comprising: receiving a selection of one of the references from the first user; and sending the object corresponding to the reference to the first user. | 0.796976 |
7,522,046 | 37 | 48 | 37. An article comprising a machine-readable medium storing instructions operable to cause a physical-document monitoring device comprising one or more machines to perform operations comprising: determining whether a state of a document has been sensed with a sensor coupled to a document coupling device; determining the document state with a computer coupled to the sensor and the document coupling device; and generating a wireless message to send a representation of the document state to a remote device. | 37. An article comprising a machine-readable medium storing instructions operable to cause a physical-document monitoring device comprising one or more machines to perform operations comprising: determining whether a state of a document has been sensed with a sensor coupled to a document coupling device; determining the document state with a computer coupled to the sensor and the document coupling device; and generating a wireless message to send a representation of the document state to a remote device. 48. The article of claim 37 , wherein the physical document comprises a paper-based document. | 0.823864 |
7,925,656 | 13 | 19 | 13. A computer program product comprising a computer recordable medium having a computer readable program recorded thereon, wherein the computer readable program, when executed on a computing device, causes the computing device to: receive a hierarchical query; separate the hierarchical query into a plurality of query legs; perform an index scan for evaluating the hierarchical query against at least one index of at least one hierarchically structured electronic document by processing a query leg on the at least one index of the at least one hierarchically structured electronic document to determine if a condition of the query leg is met by at least one node in the at least one index of the at least one hierarchically structured electronic document, wherein if at least one node in the at least one index of the at least one hierarchically structured electronic document satisfies the condition of the query leg, an entry in at least one hash table is populated with information regarding the at least one node; generate results of the hierarchical query based on content of the at least one hash table; and return the results of the hierarchical query to an originator of the hierarchical query, wherein the at least one hash table comprises a BUILD hash table and a PROBE hash table, wherein the BUILD hash table is used to store document nodes matching a predicate of the query leg and to buffer document nodes satisfying extraction nodes of the query leg, and wherein the PROBE hash table stores document nodes satisfying predicates from query legs evaluated prior to a current query leg being evaluated. | 13. A computer program product comprising a computer recordable medium having a computer readable program recorded thereon, wherein the computer readable program, when executed on a computing device, causes the computing device to: receive a hierarchical query; separate the hierarchical query into a plurality of query legs; perform an index scan for evaluating the hierarchical query against at least one index of at least one hierarchically structured electronic document by processing a query leg on the at least one index of the at least one hierarchically structured electronic document to determine if a condition of the query leg is met by at least one node in the at least one index of the at least one hierarchically structured electronic document, wherein if at least one node in the at least one index of the at least one hierarchically structured electronic document satisfies the condition of the query leg, an entry in at least one hash table is populated with information regarding the at least one node; generate results of the hierarchical query based on content of the at least one hash table; and return the results of the hierarchical query to an originator of the hierarchical query, wherein the at least one hash table comprises a BUILD hash table and a PROBE hash table, wherein the BUILD hash table is used to store document nodes matching a predicate of the query leg and to buffer document nodes satisfying extraction nodes of the query leg, and wherein the PROBE hash table stores document nodes satisfying predicates from query legs evaluated prior to a current query leg being evaluated. 19. The computer program product of claim 13 , wherein each query leg is a linear hierarchy. | 0.883838 |
9,824,313 | 9 | 13 | 9. A computer program product for determining compliance of content with a content policy, the computer program product comprising a non-transitory computer-readable storage medium containing computer program code for: receiving a content item comprising text and one or more images; extracting a plurality of text signals from the text; extracting a plurality of image signals from the one or more images; inputting the plurality of text signals and the plurality of image signals into a two-tier classifier system by inputting the plurality of text signals into a text classifier model of a first tier of the two-tier classifier system, inputting the plurality of image signals into an image classifier model of the first tier of the two tier-classifier system, and inputting output classifications of the text classifier model and of the image classifier model into a second-tier classifier model, the second-tier classifier outputting a confidence value expressing likelihood of compliance with a content policy of an online system; comparing the confidence value against a pre-defined threshold value; and based on the comparison, assigning a compliance classification to the content item. | 9. A computer program product for determining compliance of content with a content policy, the computer program product comprising a non-transitory computer-readable storage medium containing computer program code for: receiving a content item comprising text and one or more images; extracting a plurality of text signals from the text; extracting a plurality of image signals from the one or more images; inputting the plurality of text signals and the plurality of image signals into a two-tier classifier system by inputting the plurality of text signals into a text classifier model of a first tier of the two-tier classifier system, inputting the plurality of image signals into an image classifier model of the first tier of the two tier-classifier system, and inputting output classifications of the text classifier model and of the image classifier model into a second-tier classifier model, the second-tier classifier outputting a confidence value expressing likelihood of compliance with a content policy of an online system; comparing the confidence value against a pre-defined threshold value; and based on the comparison, assigning a compliance classification to the content item. 13. The computer program product of claim 9 , wherein the plurality of image signals comprises an indication that the one or more images contains a face. | 0.81872 |
8,861,856 | 1 | 4 | 1. A method for determining a logical structure of a document, the method comprising: acquiring at least one image of pages of the document; identifying one or more blocks in the image of the document; generating at least one document hypothesis for the whole document; for each document hypothesis, verifying said document hypothesis on each page; correcting or discarding said document hypothesis in case of disconfirming said document hypothesis on the respective page; selecting programmatically as a best document hypothesis the document hypothesis that has a best degree of correspondence with one or more block hypotheses for the document on pages of the document; and forming a representation of the document based on the best document hypothesis. | 1. A method for determining a logical structure of a document, the method comprising: acquiring at least one image of pages of the document; identifying one or more blocks in the image of the document; generating at least one document hypothesis for the whole document; for each document hypothesis, verifying said document hypothesis on each page; correcting or discarding said document hypothesis in case of disconfirming said document hypothesis on the respective page; selecting programmatically as a best document hypothesis the document hypothesis that has a best degree of correspondence with one or more block hypotheses for the document on pages of the document; and forming a representation of the document based on the best document hypothesis. 4. The method of claim 1 , wherein the generating the at least one document hypothesis for the document includes generating a plurality of hypotheses in order of differing probabilities. | 0.71988 |
8,856,142 | 11 | 12 | 11. The method of claim 10 , further comprising: generating, by a processor, a matrix of hyperlinks within the multi-dimensional search space, each hyperlink having coordinates within the search space defining a relevance of the hyperlink to each of the one or more initial search parameters and the plurality of related search parameters. | 11. The method of claim 10 , further comprising: generating, by a processor, a matrix of hyperlinks within the multi-dimensional search space, each hyperlink having coordinates within the search space defining a relevance of the hyperlink to each of the one or more initial search parameters and the plurality of related search parameters. 12. The method of claim 11 , wherein the plurality of vertices of the multi-dimensional search space represents the one or more initial search parameters and the plurality of related search parameters employed in a search. | 0.759219 |
8,214,354 | 20 | 28 | 20. An apparatus for enforcing a referential integrity constraint between a constraint set of objects and constrained object having values that are terms of an ontology in a relational database management system, the objects in the constraint set being possible values in the constrained object and the relational database management system being implemented in a workstation having a processor and a storage device to which the processor has access and the apparatus comprising: the storage device having an association between the constrained object and the constraint set of objects, the constraint set of objects having been returned by an ontology query that is executed in the relational database system and returns the objects in the constraint set to be used to define the referential integrity constraint, wherein different constraint sets can be derived by querying the same ontology, the constrained object comprising a constrained column that is defined in the relational database management system; the constraint set associated with the constrained object is a term object with a column, values in the column being the members of the constraint set; and a referential integrity constraint for the constrained object which references the column in the term object; and the processor for executing a constraint enforcer which, when an operation in the relational database management system adds a value to the constrained object, permits the operation only if the added value is a member of the constraint set, and when a modification is performed on the ontology in the relational database management system that results in a different set of values being returned by the ontology query, altering one or more values in the constrained object that are not contained in the different set of values. | 20. An apparatus for enforcing a referential integrity constraint between a constraint set of objects and constrained object having values that are terms of an ontology in a relational database management system, the objects in the constraint set being possible values in the constrained object and the relational database management system being implemented in a workstation having a processor and a storage device to which the processor has access and the apparatus comprising: the storage device having an association between the constrained object and the constraint set of objects, the constraint set of objects having been returned by an ontology query that is executed in the relational database system and returns the objects in the constraint set to be used to define the referential integrity constraint, wherein different constraint sets can be derived by querying the same ontology, the constrained object comprising a constrained column that is defined in the relational database management system; the constraint set associated with the constrained object is a term object with a column, values in the column being the members of the constraint set; and a referential integrity constraint for the constrained object which references the column in the term object; and the processor for executing a constraint enforcer which, when an operation in the relational database management system adds a value to the constrained object, permits the operation only if the added value is a member of the constraint set, and when a modification is performed on the ontology in the relational database management system that results in a different set of values being returned by the ontology query, altering one or more values in the constrained object that are not contained in the different set of values. 28. The apparatus set forth in claim 20 wherein: the association between the constrained object and the constraint set can specify that the relational database management system will not perform an operation that results in the different constraint set. | 0.623512 |
8,200,663 | 35 | 36 | 35. A user interface component of a computing device, comprising: an indicator of a reference item; an indicator of one or more review items including a review item unranked for the reference item or a review item ranked for the reference item when determining that the unranked review item is associated with the reference item; and voting controls for indicating an opinion regarding pertinence of the review items to the reference item, the voting controls causing a ranking of the review items associated with the reference item to be adjusted when activated. | 35. A user interface component of a computing device, comprising: an indicator of a reference item; an indicator of one or more review items including a review item unranked for the reference item or a review item ranked for the reference item when determining that the unranked review item is associated with the reference item; and voting controls for indicating an opinion regarding pertinence of the review items to the reference item, the voting controls causing a ranking of the review items associated with the reference item to be adjusted when activated. 36. The user interface component of claim 35 , wherein the indicators comprise one or more of a URL, text, an image, audio, and video. | 0.733068 |
8,874,555 | 10 | 17 | 10. A system comprising: one or more computers, programmed to perform operations comprising: calculating one or more time trend statistics for a plurality of quality of result statistics for a first document as a search result for a first query, each of the quality of result statistics corresponding to a different time period, the one or more time trend statistics estimating changes in the quality of result statistics over time, wherein each of the one or more time trend statistics include a quality of result difference between a first quality of result statistic for a first document as a search result for a first query during a first time period and a second quality of result statistic for the first document as a search result the first query during a second time period; generating a first modified quality of result statistic by modifying a first quality of result statistic for the first document as a search result for the first query by a factor, where the factor is based on the one or more time trend statistics; and providing the first modified quality of result statistic as an input to a document ranking process for the first document and the first query. | 10. A system comprising: one or more computers, programmed to perform operations comprising: calculating one or more time trend statistics for a plurality of quality of result statistics for a first document as a search result for a first query, each of the quality of result statistics corresponding to a different time period, the one or more time trend statistics estimating changes in the quality of result statistics over time, wherein each of the one or more time trend statistics include a quality of result difference between a first quality of result statistic for a first document as a search result for a first query during a first time period and a second quality of result statistic for the first document as a search result the first query during a second time period; generating a first modified quality of result statistic by modifying a first quality of result statistic for the first document as a search result for the first query by a factor, where the factor is based on the one or more time trend statistics; and providing the first modified quality of result statistic as an input to a document ranking process for the first document and the first query. 17. The system of claim 10 , wherein each quality of result statistic is an estimate of a respective percentage of users that found the first document relevant to the first query out of a first total number of users who viewed the first document as a search result for the first query during a respective time period. | 0.751956 |
9,009,197 | 10 | 11 | 10. One or more computing devices programmed to execute a method to determine a unified compliance framework, the method comprising: accessing a first set of citations, wherein each citation is associated with content, and wherein each citation of the first set has a position in a hierarchy; assigning a parent ID and a sort ID to each citation, wherein the parent ID indicates a respective citation's position in the hierarchy relative to an ancestor and descendent citations in the hierarchy, and wherein the sort ID indicates a respective citation's hierarchy relative to siblings in the hierarchy; populating a citation table having one or more rows for each citation in the first set, wherein each row at least includes: the parent ID, and the sort ID assigned to each respective citation in the first set; and accessing one or more rows in the citation table as the basis for an audit question, based on the position of the citation within the hierarchy of the one or more citations, wherein the position is based on the parent ID and sort ID associated with the citation. | 10. One or more computing devices programmed to execute a method to determine a unified compliance framework, the method comprising: accessing a first set of citations, wherein each citation is associated with content, and wherein each citation of the first set has a position in a hierarchy; assigning a parent ID and a sort ID to each citation, wherein the parent ID indicates a respective citation's position in the hierarchy relative to an ancestor and descendent citations in the hierarchy, and wherein the sort ID indicates a respective citation's hierarchy relative to siblings in the hierarchy; populating a citation table having one or more rows for each citation in the first set, wherein each row at least includes: the parent ID, and the sort ID assigned to each respective citation in the first set; and accessing one or more rows in the citation table as the basis for an audit question, based on the position of the citation within the hierarchy of the one or more citations, wherein the position is based on the parent ID and sort ID associated with the citation. 11. The one or more computing devices of claim 10 , further comprising identifying, in the first set of citations content that includes at least one noun-verb pair; assigning a unique control ID to represent each unique noun-verb pair from the first set of citations; assigning a unique noun ID to represent each unique noun the first set of citations; generating a table having a plurality of rows, wherein each row is assigned a citation ID; indicating in each row: portions of the content, data identifying the citation that corresponds to the content, the control ID that corresponds to the content, and one or more unique noun IDs that correspond with to the content. | 0.762712 |
8,131,647 | 1 | 22 | 1. A computer-implemented method for providing an annotation of a digital work, comprising: under control of instructions that are executed by one or more computing devices: receiving multiple annotations from different authors for particular content in a digital work; storing the annotations in association with the digital work; providing a list of abbreviated versions of the annotations to a user desiring to access one or more of the annotations, wherein the list presents the annotations in an order determined by reference to a criterion; receiving an authorization credential from a user desiring to access one or more of the annotations; and if the authorization credential is valid, providing a full version of one or more of the annotations of the digital work to the user in context with regard to the digital work. | 1. A computer-implemented method for providing an annotation of a digital work, comprising: under control of instructions that are executed by one or more computing devices: receiving multiple annotations from different authors for particular content in a digital work; storing the annotations in association with the digital work; providing a list of abbreviated versions of the annotations to a user desiring to access one or more of the annotations, wherein the list presents the annotations in an order determined by reference to a criterion; receiving an authorization credential from a user desiring to access one or more of the annotations; and if the authorization credential is valid, providing a full version of one or more of the annotations of the digital work to the user in context with regard to the digital work. 22. The method of claim 1 , further comprising submitting the authorization credential to a validation system that generates an indication of the credential's validity. | 0.64557 |
8,843,359 | 9 | 10 | 9. The method of claim 1 , wherein the translation output is sent to a second communication device. | 9. The method of claim 1 , wherein the translation output is sent to a second communication device. 10. The method of claim 9 , wherein the translation output is sent as an audio signal. | 0.516854 |
9,104,755 | 12 | 13 | 12. The ontology enhancement system according to claim 10 , further comprising: an user terminal, comprising an input module for providing an user to input at least the input information request. | 12. The ontology enhancement system according to claim 10 , further comprising: an user terminal, comprising an input module for providing an user to input at least the input information request. 13. The ontology enhancement system according to claim 12 , further comprising: a network, wherein the server and the user terminal are linked to each other through the network. | 0.5 |
9,514,113 | 1 | 5 | 1. A method comprising: accessing, at one or more computing devices, a document; identifying a plurality of sentences of the document, each sentence identified based on punctuation in the document; and executing, for each of the sentences of the document and using the one or more computing devices, a document modification operation that includes: generating a ranking score for each of a plurality of passages from external documents, wherein the ranking score is based at least on a degree of semantic similarity of each of the plurality of passages from the external documents with respect to the sentence of the document, modifying the sentence to include a footnote link for the sentence in the document, the footnote link including a link to the external document having a highest ranked passage therein if the ranking score of the highest ranked passage with respect to the sentence exceeds a threshold value, and skipping modification of the sentence if the ranking score of the highest ranked passage with respect to the sentence does not exceed the threshold value. | 1. A method comprising: accessing, at one or more computing devices, a document; identifying a plurality of sentences of the document, each sentence identified based on punctuation in the document; and executing, for each of the sentences of the document and using the one or more computing devices, a document modification operation that includes: generating a ranking score for each of a plurality of passages from external documents, wherein the ranking score is based at least on a degree of semantic similarity of each of the plurality of passages from the external documents with respect to the sentence of the document, modifying the sentence to include a footnote link for the sentence in the document, the footnote link including a link to the external document having a highest ranked passage therein if the ranking score of the highest ranked passage with respect to the sentence exceeds a threshold value, and skipping modification of the sentence if the ranking score of the highest ranked passage with respect to the sentence does not exceed the threshold value. 5. The method of claim 1 , wherein the one or more computing devices include a server computing device and a client computing device, generating the ranking score for each of a plurality of passages from external documents is performed at the client computing device based on data received from the server computing device, and modifying the sentence to include the footnote link is performed at the client computing device. | 0.547009 |
7,546,295 | 16 | 17 | 16. The apparatus of claim 11 , further comprising: said processor generating a set a recommendations that may be applied to a search; and for a given user who may be anonymous, and a given search query, said processor using said recommendations to refine and augment a resulting search. | 16. The apparatus of claim 11 , further comprising: said processor generating a set a recommendations that may be applied to a search; and for a given user who may be anonymous, and a given search query, said processor using said recommendations to refine and augment a resulting search. 17. The apparatus of claim 16 , further comprising: said processor driving said recommendations not just by individual uses, but by the use of communities, leveraging the wisdom of crowds and community emergent behavior. | 0.5 |
7,555,470 | 2 | 3 | 2. An apparatus, comprising: a plurality of input devices for collecting data related to human subjects, each input device of said plurality of input devices being configured based upon a protocol, said data being collected during a course of research testing of a human response to a pre-determined stimuli, said protocol including at least one question or request for information regarding physical well being or mental well being; a server device relatively remote from said input devices for receiving said data, said server device storing said protocol; a communication link between each input device and said server device; a processor including software configured to determine trend a amongst a plurality of human subjects based on said data in real-time with receipt of said data related to the plurality of human subjects from said plurality of input devices and generate an updated protocol to be sent to said subset of said plurality of human subjects; and a presentation device relatively remote from said server device and coupled to said processor, wherein said presentation device is configured to present said data in response to said updated protocol, said updated protocol being modified from said protocol to include at least one additional question or request for information regarding physical well being or mental well being to correlate different parameters of the data received from each device of said plurality of devices. | 2. An apparatus, comprising: a plurality of input devices for collecting data related to human subjects, each input device of said plurality of input devices being configured based upon a protocol, said data being collected during a course of research testing of a human response to a pre-determined stimuli, said protocol including at least one question or request for information regarding physical well being or mental well being; a server device relatively remote from said input devices for receiving said data, said server device storing said protocol; a communication link between each input device and said server device; a processor including software configured to determine trend a amongst a plurality of human subjects based on said data in real-time with receipt of said data related to the plurality of human subjects from said plurality of input devices and generate an updated protocol to be sent to said subset of said plurality of human subjects; and a presentation device relatively remote from said server device and coupled to said processor, wherein said presentation device is configured to present said data in response to said updated protocol, said updated protocol being modified from said protocol to include at least one additional question or request for information regarding physical well being or mental well being to correlate different parameters of the data received from each device of said plurality of devices. 3. The apparatus as claimed in claim 2 , wherein said plurality of input devices collects data based upon said protocol. | 0.617834 |
9,223,831 | 7 | 8 | 7. The system of claim 1 , wherein the sentiment analysis feature extraction processing module utilizes lexical analysis, supervised machine learning sentiment, and topic analysis to compute the analytical summaries. | 7. The system of claim 1 , wherein the sentiment analysis feature extraction processing module utilizes lexical analysis, supervised machine learning sentiment, and topic analysis to compute the analytical summaries. 8. The system of claim 7 , wherein the sentiment analysis feature extraction processing module further directs the central processing unit to calculate the average score of the application's rated features. | 0.5 |
9,798,724 | 9 | 15 | 9. A system for document discovery, comprising: a data repository storing a plurality of electronic documents, wherein each of the plurality of electronic documents comprises searchable metadata; a computer processor connected to the data repository that: receives a scan of a physical copy of a document comprising a non-text object; determines a first tag for the non-text object by comparing the non-text object with a plurality of templates comprising a plurality of tags, wherein the first tag defines a portion of the non-text object in an original file and specifies a type of the non-text object and a formatting attribute of the non-text object; generates, based on the first tag, non-text object metadata comprising composition information comprising the type and the formatting attribute for the non-text object; searches the plurality of electronic documents stored in the data repository with a search query comprising the non-text object metadata; compares the non-text object metadata with the searchable metadata; and provides a location of the original file to a user when the non-text object metadata in the search query matches the searchable metadata of the original file. | 9. A system for document discovery, comprising: a data repository storing a plurality of electronic documents, wherein each of the plurality of electronic documents comprises searchable metadata; a computer processor connected to the data repository that: receives a scan of a physical copy of a document comprising a non-text object; determines a first tag for the non-text object by comparing the non-text object with a plurality of templates comprising a plurality of tags, wherein the first tag defines a portion of the non-text object in an original file and specifies a type of the non-text object and a formatting attribute of the non-text object; generates, based on the first tag, non-text object metadata comprising composition information comprising the type and the formatting attribute for the non-text object; searches the plurality of electronic documents stored in the data repository with a search query comprising the non-text object metadata; compares the non-text object metadata with the searchable metadata; and provides a location of the original file to a user when the non-text object metadata in the search query matches the searchable metadata of the original file. 15. The system of claim 9 , wherein the data repository is part of an enterprise content management (ECM) system. | 0.880297 |
7,801,887 | 29 | 42 | 29. A computer-readable medium having stored thereon a data structure for processing documents in a document database, the computer-readable medium comprising: a first data field for generating an initial ranking of retrieved documents using an information retrieval system and based upon a user search query provided by a user; a second data field for displaying to the user the initial ranking of the retrieved documents; a third data field for permitting user selection of a plurality of vocabulary words based upon occurrences thereof in at least some of the retrieved documents; a fourth data field for generating respective relevancies of the user-selected vocabulary words; a fifth data field for generating a re-ranking of the retrieved documents based on the generated respective relevancies of the vocabulary words; a sixth data field for displaying for the user the re-ranking of the documents, and for each document being displayed, also displaying its initial ranking; and a seventh data field for generating the plurality of vocabulary words based upon occurrences thereof in at least some of the retrieved documents before generating the initial ranking of retrieved documents. | 29. A computer-readable medium having stored thereon a data structure for processing documents in a document database, the computer-readable medium comprising: a first data field for generating an initial ranking of retrieved documents using an information retrieval system and based upon a user search query provided by a user; a second data field for displaying to the user the initial ranking of the retrieved documents; a third data field for permitting user selection of a plurality of vocabulary words based upon occurrences thereof in at least some of the retrieved documents; a fourth data field for generating respective relevancies of the user-selected vocabulary words; a fifth data field for generating a re-ranking of the retrieved documents based on the generated respective relevancies of the vocabulary words; a sixth data field for displaying for the user the re-ranking of the documents, and for each document being displayed, also displaying its initial ranking; and a seventh data field for generating the plurality of vocabulary words based upon occurrences thereof in at least some of the retrieved documents before generating the initial ranking of retrieved documents. 42. A computer-readable medium according to claim 29 further comprising a nineteenth data field for displaying the re-ranked documents. | 0.84375 |
8,850,332 | 13 | 16 | 13. A computer program product, comprising: a computer usable storage medium having stored therein computer usable program code, the computer usable program code, when executed in a web page authoring system having a user input system and an editing screen display for displaying a representation of a tag associated with a display artifact represented on the editing screen display, causes the web page authorizing system to perform: receiving a user action input selecting a reference point on the editing screen display for a web page being authored; setting a reference area on the editing screen display enclosing the selected reference point; selecting the display object closest to the reference point as a reference display artifact from among display artifacts in the reference area; selecting a tag associated with the reference display artifact from among tags associated with the display artifacts in the reference area; and drawing a first rectangle on the editing screen display artifact and a second, larger rectangle enclosing said first rectangle, a space between said first and second rectangles representing the selected tag, wherein the selected tag, associated with the first rectangle and the selected display artifact, includes an open tag and a corresponding close tag. | 13. A computer program product, comprising: a computer usable storage medium having stored therein computer usable program code, the computer usable program code, when executed in a web page authoring system having a user input system and an editing screen display for displaying a representation of a tag associated with a display artifact represented on the editing screen display, causes the web page authorizing system to perform: receiving a user action input selecting a reference point on the editing screen display for a web page being authored; setting a reference area on the editing screen display enclosing the selected reference point; selecting the display object closest to the reference point as a reference display artifact from among display artifacts in the reference area; selecting a tag associated with the reference display artifact from among tags associated with the display artifacts in the reference area; and drawing a first rectangle on the editing screen display artifact and a second, larger rectangle enclosing said first rectangle, a space between said first and second rectangles representing the selected tag, wherein the selected tag, associated with the first rectangle and the selected display artifact, includes an open tag and a corresponding close tag. 16. The computer program product of claim 13 , wherein the selected tag related to the reference display is at least one of a sibling tag, a parent tag, and a child tag. | 0.5 |
9,110,980 | 11 | 17 | 11. A non-transitory computer-readable medium having stored thereon a program code, the program code executable by a processor to: extract a first feature set associated with an input data string comprising one or more first ideographic elements, wherein extracting the first feature set comprises extracting a first shape feature from the input data string; extract a second feature set associated with a candidate string comprising one or more second ideographic elements, wherein extracting the second feature set comprises extracting a second shape feature from the candidate string; and determine a match score of the candidate string based on the first and second feature sets. | 11. A non-transitory computer-readable medium having stored thereon a program code, the program code executable by a processor to: extract a first feature set associated with an input data string comprising one or more first ideographic elements, wherein extracting the first feature set comprises extracting a first shape feature from the input data string; extract a second feature set associated with a candidate string comprising one or more second ideographic elements, wherein extracting the second feature set comprises extracting a second shape feature from the candidate string; and determine a match score of the candidate string based on the first and second feature sets. 17. The computer-readable medium of claim 11 wherein the first shape feature comprises the first ideographic element and the second shape feature comprises the second ideographic element. | 0.710526 |
8,806,320 | 1 | 3 | 1. A method, comprising: receiving, via a network, a media selection in connection with a first media associated with a media file; receiving, via the network, a media selection in connection with a second media associated with a media file; receiving, via the network, a multi-sync request associated with the media selection for the first media and the media selection for the second media; and when the multi-sync request is a time-based multi-sync request, then receive, via the network, a selection of a segment of the first media and a selection of a segment of the second media; automatically detect whether a duration of the segment of the first media is equal to a duration of the segment of the second media; when the duration of the segment of the first media is detected as being equal to the duration of the segment of the second media, then automatically enable time-based synching as a default to generate a dynamic media link and multi-sync data based on the selection of the segment of the first media and the selection of the segment of the second media, without affecting an integrity of the first media and an integrity of the second media, the dynamic media link being a hyperlink; send the multi-sync data such that the multi-sync data is stored in a relational database at a relational database server after the multi-sync data is generated; and send, via the network, the dynamic media link such that the segment of the first media and the segment of the second media are displayed and synchronously played side-by-side in a user-editable form based on the multi-sync data stored in the relational database, after receiving an indication that the dynamic media link was selected, the user-editable form being received from a media server, the user-editable form allowing a user to edit synchronization points between the first media and the second media. | 1. A method, comprising: receiving, via a network, a media selection in connection with a first media associated with a media file; receiving, via the network, a media selection in connection with a second media associated with a media file; receiving, via the network, a multi-sync request associated with the media selection for the first media and the media selection for the second media; and when the multi-sync request is a time-based multi-sync request, then receive, via the network, a selection of a segment of the first media and a selection of a segment of the second media; automatically detect whether a duration of the segment of the first media is equal to a duration of the segment of the second media; when the duration of the segment of the first media is detected as being equal to the duration of the segment of the second media, then automatically enable time-based synching as a default to generate a dynamic media link and multi-sync data based on the selection of the segment of the first media and the selection of the segment of the second media, without affecting an integrity of the first media and an integrity of the second media, the dynamic media link being a hyperlink; send the multi-sync data such that the multi-sync data is stored in a relational database at a relational database server after the multi-sync data is generated; and send, via the network, the dynamic media link such that the segment of the first media and the segment of the second media are displayed and synchronously played side-by-side in a user-editable form based on the multi-sync data stored in the relational database, after receiving an indication that the dynamic media link was selected, the user-editable form being received from a media server, the user-editable form allowing a user to edit synchronization points between the first media and the second media. 3. The method of claim 1 , wherein, if a request is received to preview the multi-synced media, sending a signal such that a preview synchronized media screen with both multi-synced media is displayed. | 0.904286 |
9,348,813 | 1 | 9 | 1. A text analysis system comprising: a natural language input unit for enabling a user to input a free text in a natural language; a natural language processing unit for processing at least a portion of the free text while it is being inputted, to obtain an explicit representation of semantics entailed by the free text; an explicit information input unit for enabling the user to input explicit information relating to the explicit representation of semantics; and a user interface for providing a user with simultaneous access to both the natural language input unit and the explicit information input unit; wherein the explicit information input unit is arranged for enabling the user to confirm or reject the explicit representation of the semantics; and wherein the user interface is arranged for providing the user with an alternative explicit representation upon receipt of a rejection of the explicit representation of the semantics. | 1. A text analysis system comprising: a natural language input unit for enabling a user to input a free text in a natural language; a natural language processing unit for processing at least a portion of the free text while it is being inputted, to obtain an explicit representation of semantics entailed by the free text; an explicit information input unit for enabling the user to input explicit information relating to the explicit representation of semantics; and a user interface for providing a user with simultaneous access to both the natural language input unit and the explicit information input unit; wherein the explicit information input unit is arranged for enabling the user to confirm or reject the explicit representation of the semantics; and wherein the user interface is arranged for providing the user with an alternative explicit representation upon receipt of a rejection of the explicit representation of the semantics. 9. The system according to claim 1 , comprising an algorithm improvement unit for improving a natural language processing algorithm that is used by the natural language processing unit, based on the explicit information inputted by the user. | 0.655714 |
8,739,129 | 28 | 33 | 28. One or more non-transitory computer-readable media storing computer-executable instructions executable by a processor, the media storing one or more instructions for: identifying a call chain of methods that are called during an execution of a graphical model, the graphical model under control of a multi-domain unified debugger that enables the debugging of the graphical model, where: the graphical model includes: a first entity associated with a first modeling domain of a plurality of different types of modeling domains, and a second entity associated with a second modeling domain of the plurality of different types of modeling domains, the different types of modeling domains include a statechart domain, a time-based block diagram domain, a physical system domain, a data flow diagram domain, a unified modeling language domain, a discrete event modeling domain, or a compiled code domain, the call chain indicates an execution order of the methods associated with the first entity and the second entity, the first entity and the second entity are associated with a programming interface, and the programming interface allows access to information associated with the first entity and the second entity; transferring information associated with the first entity via the programming interface to the multi-domain unified debugger after executing the first entity; generating a first domain-specific debugger view of the graphical model, the generated first domain-specific debugger view consistent with the first modeling domain, the generating first domain-specific debugger view being based on the transferring information associated with the first entity; transferring information associated with the second entity via the programming interface to the debugger after executing the second entity; generating a second domain-specific debugger view of the graphical model, the generated second domain-specific debugger view consistent with the second modeling domain, the generating the second domain-specific debugger being based on the transferring information associated with the second entity; displaying the first domain-specific debugger view on a display device, where the first domain-specific debugger view is displayed: after the first entity is executed, concurrently with the identified call chain, and using a first user interface element; automatically transitioning to an updated domain display, the transitioning occurring when the second entity is executing; and displaying the second domain-specific debugger view on the display device, where the second domain-specific debugger view is displayed: based on the transitioning, after the second entity is executed, concurrently with the identified call chain, and using a second user interface element. | 28. One or more non-transitory computer-readable media storing computer-executable instructions executable by a processor, the media storing one or more instructions for: identifying a call chain of methods that are called during an execution of a graphical model, the graphical model under control of a multi-domain unified debugger that enables the debugging of the graphical model, where: the graphical model includes: a first entity associated with a first modeling domain of a plurality of different types of modeling domains, and a second entity associated with a second modeling domain of the plurality of different types of modeling domains, the different types of modeling domains include a statechart domain, a time-based block diagram domain, a physical system domain, a data flow diagram domain, a unified modeling language domain, a discrete event modeling domain, or a compiled code domain, the call chain indicates an execution order of the methods associated with the first entity and the second entity, the first entity and the second entity are associated with a programming interface, and the programming interface allows access to information associated with the first entity and the second entity; transferring information associated with the first entity via the programming interface to the multi-domain unified debugger after executing the first entity; generating a first domain-specific debugger view of the graphical model, the generated first domain-specific debugger view consistent with the first modeling domain, the generating first domain-specific debugger view being based on the transferring information associated with the first entity; transferring information associated with the second entity via the programming interface to the debugger after executing the second entity; generating a second domain-specific debugger view of the graphical model, the generated second domain-specific debugger view consistent with the second modeling domain, the generating the second domain-specific debugger being based on the transferring information associated with the second entity; displaying the first domain-specific debugger view on a display device, where the first domain-specific debugger view is displayed: after the first entity is executed, concurrently with the identified call chain, and using a first user interface element; automatically transitioning to an updated domain display, the transitioning occurring when the second entity is executing; and displaying the second domain-specific debugger view on the display device, where the second domain-specific debugger view is displayed: based on the transitioning, after the second entity is executed, concurrently with the identified call chain, and using a second user interface element. 33. The non-transitory computer-readable media of claim 28 further comprising one or more instructions for: maintaining the call chain in a database. | 0.516234 |
8,914,368 | 1 | 10 | 1. A method comprising: creating, by a computer system including one or more processors executing a cross-service tagging mashup application, connections to multiple applications, each application having multiple entities; determining, by the computer system, relationships between said entities within a single application and between entities across multiple applications; associating, by the computer system, a tag with a selected one of said entities in a first one of said multiple applications; identifying, by the computer system, based on said determined relationships, entities in other applications besides said first application that are related to said selected entity, the first one of said applications including at least one of an email application, a blogging application and a website-bookmarking application, and said other applications include at least a different one of the email application, the blogging application and the website-bookmarking application; and propagating, by the computer system, said tag across the multiple applications by associating said tag with said identified entities in said other applications, the propagating said tag including generating the tag in the other applications such that displaying one of the identified entities in the multiple applications results in displaying the tag together with the one of the identified entities. | 1. A method comprising: creating, by a computer system including one or more processors executing a cross-service tagging mashup application, connections to multiple applications, each application having multiple entities; determining, by the computer system, relationships between said entities within a single application and between entities across multiple applications; associating, by the computer system, a tag with a selected one of said entities in a first one of said multiple applications; identifying, by the computer system, based on said determined relationships, entities in other applications besides said first application that are related to said selected entity, the first one of said applications including at least one of an email application, a blogging application and a website-bookmarking application, and said other applications include at least a different one of the email application, the blogging application and the website-bookmarking application; and propagating, by the computer system, said tag across the multiple applications by associating said tag with said identified entities in said other applications, the propagating said tag including generating the tag in the other applications such that displaying one of the identified entities in the multiple applications results in displaying the tag together with the one of the identified entities. 10. The method of claim 1 , wherein the first one of said applications is an email application, said other applications include at least one of a blogging application and a bookmarking application, propagating said tag across the multiple applications includes displaying on the at least one of the blogging application and the bookmarking application an indicator that said tag has been applied to the email message without providing to a user of the blogging application or the bookmarking application access to an email message to which said tag has been applied. | 0.5 |
9,020,923 | 1 | 6 | 1. A method, comprising: providing, via a computing device, a window of a first webpage providing a user interface to search music, the user interface comprising a plurality of search tools including a keyword search tool having a first user interface element configured to receive a set of keywords from a user, the keyword search tool configured to identify a set of music search results based on matching with the keywords, and a set of filter tools having at least one second user interface element separate from the first user interface element and configured to receive a set of filtering options, the set of filter tools configured to filter the set of music search results based on the filter options specified in the at least one second user interface element, wherein the at least one second user interface element of the set of filter tools is capable of receiving the set of filtering options without the user providing input to the first user interface element of the keyword search tool, wherein the keyword search tool is configured to cause the set of filter tools to apply filter options corresponding to keywords received from the user in the keyword search tool, and wherein the set of filter tools are configured to cause the keyword search tool to receive keywords corresponding to filter options applied by user in the set filter tools; providing, by the computing device within the window of the first webpage, the set of music search results as a list of items in the first webpage, each of the items corresponding to a music piece; when an item in the list corresponding to a search result of the set of music search results is selected by the user in the user interface, expanding the item within the list corresponding to the selected music search result within the window of the first webpage to present, inside the list at a location near the selected search result, an explore-more button, an action button, an audio player showing a waveform representing audio of the selected music search result, a description of the selected music search result, and artwork associated with the search result; updating search results within the window without leaving the first webpage when the filter tools are used to modify search criteria; and in response to a user selection of the explore-more button, determining acoustic attributes of a piece of music in the search result in which the explore-more button is presented, the acoustic attributes based on numerical measurements of audio signals in the piece of music, initiating a new search using at least the acoustic attributes, wherein the acoustic attributes cannot be searched using the keyword search tool, and presenting results of the new search in a list ordered according to a degree of acoustic similarity with the piece of music. | 1. A method, comprising: providing, via a computing device, a window of a first webpage providing a user interface to search music, the user interface comprising a plurality of search tools including a keyword search tool having a first user interface element configured to receive a set of keywords from a user, the keyword search tool configured to identify a set of music search results based on matching with the keywords, and a set of filter tools having at least one second user interface element separate from the first user interface element and configured to receive a set of filtering options, the set of filter tools configured to filter the set of music search results based on the filter options specified in the at least one second user interface element, wherein the at least one second user interface element of the set of filter tools is capable of receiving the set of filtering options without the user providing input to the first user interface element of the keyword search tool, wherein the keyword search tool is configured to cause the set of filter tools to apply filter options corresponding to keywords received from the user in the keyword search tool, and wherein the set of filter tools are configured to cause the keyword search tool to receive keywords corresponding to filter options applied by user in the set filter tools; providing, by the computing device within the window of the first webpage, the set of music search results as a list of items in the first webpage, each of the items corresponding to a music piece; when an item in the list corresponding to a search result of the set of music search results is selected by the user in the user interface, expanding the item within the list corresponding to the selected music search result within the window of the first webpage to present, inside the list at a location near the selected search result, an explore-more button, an action button, an audio player showing a waveform representing audio of the selected music search result, a description of the selected music search result, and artwork associated with the search result; updating search results within the window without leaving the first webpage when the filter tools are used to modify search criteria; and in response to a user selection of the explore-more button, determining acoustic attributes of a piece of music in the search result in which the explore-more button is presented, the acoustic attributes based on numerical measurements of audio signals in the piece of music, initiating a new search using at least the acoustic attributes, wherein the acoustic attributes cannot be searched using the keyword search tool, and presenting results of the new search in a list ordered according to a degree of acoustic similarity with the piece of music. 6. The method of claim 1 , wherein the action button comprises a download button enabling downloading of the music. | 0.640625 |
8,065,584 | 7 | 10 | 7. An encoding apparatus including hardware, the encoding apparatus comprising: a scrambler to scramble a first occurrence of a data word to produce a first scrambled data word; a coder to block encode said first scrambled data word to produce a first code word; the scrambler to scramble a second occurrence of said data word to produce a second scrambled data word; and the coder to block encode said second scrambled data word to produce a second code word, wherein said second code word and said first code word are different from one another. | 7. An encoding apparatus including hardware, the encoding apparatus comprising: a scrambler to scramble a first occurrence of a data word to produce a first scrambled data word; a coder to block encode said first scrambled data word to produce a first code word; the scrambler to scramble a second occurrence of said data word to produce a second scrambled data word; and the coder to block encode said second scrambled data word to produce a second code word, wherein said second code word and said first code word are different from one another. 10. The encoding apparatus of claim 7 , wherein at least one of the first code word and the second code word comprises a pattern of symbols which occurs at only one position relative to a boundary between code words within a stream of code words. | 0.61442 |
9,256,826 | 1 | 5 | 1. One or more computer-readable storage media comprising instructions stored thereon that, responsive to execution by a computing device, cause the computing device to perform operations comprising: collecting posts to a social network and responses to each post by a social network community; binning the posts into different post classes based on a number of the responses to each post, the binning comprising normalizing the number of the responses to each post by dividing the number of the responses to each post by a total number of subscribers of the social network community, generating a histogram of the normalized number of responses to each post, applying Otsu thresholding techniques to the histogram to identify one or more boundaries between distributions of the posts in the histogram, and binning at least one of the distributions of the posts into a popular post class that includes posts that received a high number of responses; extracting features from the posts, the extracted features including at least one non-textual feature; and forming a prediction model by applying a learning model to the different classes of posts and the extracted features. | 1. One or more computer-readable storage media comprising instructions stored thereon that, responsive to execution by a computing device, cause the computing device to perform operations comprising: collecting posts to a social network and responses to each post by a social network community; binning the posts into different post classes based on a number of the responses to each post, the binning comprising normalizing the number of the responses to each post by dividing the number of the responses to each post by a total number of subscribers of the social network community, generating a histogram of the normalized number of responses to each post, applying Otsu thresholding techniques to the histogram to identify one or more boundaries between distributions of the posts in the histogram, and binning at least one of the distributions of the posts into a popular post class that includes posts that received a high number of responses; extracting features from the posts, the extracted features including at least one non-textual feature; and forming a prediction model by applying a learning model to the different classes of posts and the extracted features. 5. The one or more computer-readable storage media of claim 1 , wherein the extracted features further include at least one textual feature. | 0.78979 |
8,493,208 | 10 | 17 | 10. A non-transitory computer readable medium having instructions recorded thereon which, when executed by a processor, cause the processor to perform a method comprising: periodically sampling a user environment via a set of environmental sensors integrated into a communication device; deriving a set of environmental circumstances based at least partially on an output of the set of environmental sensors; and comparing the derived set of environmental circumstances to a set of templates to determine if there is a matching template, wherein if more than one of the templates matches the derived set of environmental circumstances, a best-match template is determined, and wherein if there is a matching template, an action script is executed. | 10. A non-transitory computer readable medium having instructions recorded thereon which, when executed by a processor, cause the processor to perform a method comprising: periodically sampling a user environment via a set of environmental sensors integrated into a communication device; deriving a set of environmental circumstances based at least partially on an output of the set of environmental sensors; and comparing the derived set of environmental circumstances to a set of templates to determine if there is a matching template, wherein if more than one of the templates matches the derived set of environmental circumstances, a best-match template is determined, and wherein if there is a matching template, an action script is executed. 17. The non-transitory computer readable medium of claim 10 , wherein the environmental circumstances include a non-occurrence of an expected event. | 0.764331 |
8,938,461 | 1 | 21 | 1. A computer implemented method for organizing documents into nodes, in which a node represents a group of near equivalent documents, said computer implemented method comprising: (i) providing a plurality of original documents, each of the original documents comprising a header and a body text, and wherein said header comprises at least one header parameter and wherein said body text comprises text; (ii) selecting a document from among said plurality of original documents and associating the selected document with a node; (iii) comparing a fingerprint of said selected document to previously stored fingerprints of other documents from amongst said plurality of original documents, and in the case of a match between the fingerprints, merging the node associated with said selected document with a node associated with a matching document having a fingerprint matching the fingerprint of said selected document; (iv) searching in text order through said body text of said selected document to locate a first instance of header-type text within said selected document, wherein said header-type text contains at least one header parameter; (v) constructing a presumed document from a subset of the body text of said selected document, the constructed presumed document having (a) a header that includes one or more parameters from said header-type text located within said body text of said selected document, irrespective of whether the subject parameter of the header of said presumed document is the same as the subject parameter of the header of said selected document, and (b) body text that includes the text of said selected document located after said header-type text in said body of said selected document, and associating said presumed document with a node; (vi) comparing a fingerprint of said presumed document to the previously stored fingerprint of at least one other document from among said plurality of original documents and in the case of a match between the fingerprints, merging a node associated with said presumed document with a node associated with a matching document having a fingerprint matching the fingerprint of said presumed document; and (vii) if the comparing of (vi) does not result in a match, processing repeatedly a remainder of the body text of said selected document for successive instances of header-type text according to step (iv), and for each successive instance of the header-type text, constructing a corresponding presumed document according to step (v), and comparing for any matching documents to the corresponding presumed document according to step (vi), said processing of steps (iv)-(vi) is repeatedly performed until a match is found in step (vi) or until no new instances of header-type text are found in step (iv), wherein each fingerprint comprises a representation of a corresponding document, and the plurality of nodes are arranged in terms of more than one tree, each tree comprising at least one node from the plurality of nodes, each tree comprising at least a root node and at least a leaf node, a root node being a node that is not a descendant of any other node, and a leaf node being a node that has no descendent nodes, a node not being prohibited from being both a root node and a leaf node, all nodes that are descendant from the root node are contained by the tree, each node being associated with either: (1) one of the original documents and any matching document thereof or (2) one of the presumed documents and any matching document thereof. | 1. A computer implemented method for organizing documents into nodes, in which a node represents a group of near equivalent documents, said computer implemented method comprising: (i) providing a plurality of original documents, each of the original documents comprising a header and a body text, and wherein said header comprises at least one header parameter and wherein said body text comprises text; (ii) selecting a document from among said plurality of original documents and associating the selected document with a node; (iii) comparing a fingerprint of said selected document to previously stored fingerprints of other documents from amongst said plurality of original documents, and in the case of a match between the fingerprints, merging the node associated with said selected document with a node associated with a matching document having a fingerprint matching the fingerprint of said selected document; (iv) searching in text order through said body text of said selected document to locate a first instance of header-type text within said selected document, wherein said header-type text contains at least one header parameter; (v) constructing a presumed document from a subset of the body text of said selected document, the constructed presumed document having (a) a header that includes one or more parameters from said header-type text located within said body text of said selected document, irrespective of whether the subject parameter of the header of said presumed document is the same as the subject parameter of the header of said selected document, and (b) body text that includes the text of said selected document located after said header-type text in said body of said selected document, and associating said presumed document with a node; (vi) comparing a fingerprint of said presumed document to the previously stored fingerprint of at least one other document from among said plurality of original documents and in the case of a match between the fingerprints, merging a node associated with said presumed document with a node associated with a matching document having a fingerprint matching the fingerprint of said presumed document; and (vii) if the comparing of (vi) does not result in a match, processing repeatedly a remainder of the body text of said selected document for successive instances of header-type text according to step (iv), and for each successive instance of the header-type text, constructing a corresponding presumed document according to step (v), and comparing for any matching documents to the corresponding presumed document according to step (vi), said processing of steps (iv)-(vi) is repeatedly performed until a match is found in step (vi) or until no new instances of header-type text are found in step (iv), wherein each fingerprint comprises a representation of a corresponding document, and the plurality of nodes are arranged in terms of more than one tree, each tree comprising at least one node from the plurality of nodes, each tree comprising at least a root node and at least a leaf node, a root node being a node that is not a descendant of any other node, and a leaf node being a node that has no descendent nodes, a node not being prohibited from being both a root node and a leaf node, all nodes that are descendant from the root node are contained by the tree, each node being associated with either: (1) one of the original documents and any matching document thereof or (2) one of the presumed documents and any matching document thereof. 21. The computer implemented method of claim 1 , wherein said (vii) includes: for each of said instances, constructing said corresponding presumed document irrespective of whether the subject parameter of the header of said corresponding presumed document is the same as the subject parameter of the header of said original selected document or of previous constructed presumed document. | 0.844703 |
10,078,376 | 6 | 7 | 6. The method according to claim 1 , wherein the steps of capturing, displaying said captured image, converting, and displaying said recognized character text are executed repetitively until a certain keypress is detected to end said repetitive execution, wherein the respective latest recognized text in the part of the respective latest captured image is analyzed for new text in regards to a previously recognized character text that is output to said application requesting said input text, whereupon a control command is generated for the selection of the new text as the recognized character text, and said new text is output to said application requesting said input text. | 6. The method according to claim 1 , wherein the steps of capturing, displaying said captured image, converting, and displaying said recognized character text are executed repetitively until a certain keypress is detected to end said repetitive execution, wherein the respective latest recognized text in the part of the respective latest captured image is analyzed for new text in regards to a previously recognized character text that is output to said application requesting said input text, whereupon a control command is generated for the selection of the new text as the recognized character text, and said new text is output to said application requesting said input text. 7. The method according to claim 6 , wherein said control command for the selection of said recognized character text is generated automatically via a detection algorithm, wherein said detection algorithm recognizes whether said recognized character text in a previously captured image and in the current captured image are the same. | 0.5 |
10,027,778 | 15 | 16 | 15. The system of claim 13 , wherein the operations further comprise: responsive to receiving the endorsement of the particular skill from the first member, increasing a calculated skill rank for the second member for the particular skill. | 15. The system of claim 13 , wherein the operations further comprise: responsive to receiving the endorsement of the particular skill from the first member, increasing a calculated skill rank for the second member for the particular skill. 16. The system of claim 15 , wherein an amount with which the calculated skill rank for the second member is increased for the particular skill depends on a skill rank of the first member for the particular skill. | 0.5 |
9,836,503 | 11 | 12 | 11. A computer program product embodied in a non-transitory computer readable medium, the computer readable medium having stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a set of acts, the set of acts comprising: receiving a SQL database query language statement on a SQL database, wherein the SQL database query language statement comprises one or more SQL query clauses and a table function that transforms at least one SPARQL endpoint into a row source for the SQL database to integrate local relational data with non-local RDF data retrieved from the one or more SPARQL endpoints, and a SPARQL query string and at least one SPARQL endpoint of one or more SPARQL endpoints are embedded in the table function of SQL database query language statement; executing the SQL database query language statement including the table function on the SQL database to transform the at least one SPARQL endpoint into a row source for the SQL database, wherein during execution of the SQL database query language statement on the SQL database, executing the one or more SQL query clauses on the SQL database; sending at least the SPARQL query string embedded in the SQL database query language statement to the at least one SPARQL endpoint that is embedded in the SQL database query language statement, wherein the at least one SPARQL endpoint is identified by parsing at least the table function in the SQL database query language statement that has been executed; receiving query results from the at least one SPARQL endpoint, at least a portion of the query results corresponding to the non-local RDF data; converting the non-local RDF data into a relational data format to generate converted RDF data; and combining the converted RDF data with relational data from a local relational database. | 11. A computer program product embodied in a non-transitory computer readable medium, the computer readable medium having stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a set of acts, the set of acts comprising: receiving a SQL database query language statement on a SQL database, wherein the SQL database query language statement comprises one or more SQL query clauses and a table function that transforms at least one SPARQL endpoint into a row source for the SQL database to integrate local relational data with non-local RDF data retrieved from the one or more SPARQL endpoints, and a SPARQL query string and at least one SPARQL endpoint of one or more SPARQL endpoints are embedded in the table function of SQL database query language statement; executing the SQL database query language statement including the table function on the SQL database to transform the at least one SPARQL endpoint into a row source for the SQL database, wherein during execution of the SQL database query language statement on the SQL database, executing the one or more SQL query clauses on the SQL database; sending at least the SPARQL query string embedded in the SQL database query language statement to the at least one SPARQL endpoint that is embedded in the SQL database query language statement, wherein the at least one SPARQL endpoint is identified by parsing at least the table function in the SQL database query language statement that has been executed; receiving query results from the at least one SPARQL endpoint, at least a portion of the query results corresponding to the non-local RDF data; converting the non-local RDF data into a relational data format to generate converted RDF data; and combining the converted RDF data with relational data from a local relational database. 12. The computer program product of claim 11 , further comprising instructions which, when executed by the processor, further cause the processor to execute the set of acts, and the set of acts further comprising receiving at least some RDF data from the one or more remote SPARQL endpoints. | 0.570796 |
8,688,603 | 5 | 10 | 5. A computer-implemented method, comprising: based on reference data that includes individuals of a population and labels that each indicate whether a given individual meets one or more specific criteria, generating a first machine learning model for determining whether individuals meet said specific criteria; identifying one or more false positive individuals, wherein a given false positive individual is an individual that the first machine learning model indicates as having meeting said specific criteria and which is labeled within the reference data as not meeting said specific criteria; selecting one or more of the identified false positive individuals as candidates to be corrected within the reference data; and subsequent to a correction of the reference data associated with one or more of the selected false positive individuals, generating based on the corrected reference data a new machine learning model for determining whether individuals meet said specific criteria. | 5. A computer-implemented method, comprising: based on reference data that includes individuals of a population and labels that each indicate whether a given individual meets one or more specific criteria, generating a first machine learning model for determining whether individuals meet said specific criteria; identifying one or more false positive individuals, wherein a given false positive individual is an individual that the first machine learning model indicates as having meeting said specific criteria and which is labeled within the reference data as not meeting said specific criteria; selecting one or more of the identified false positive individuals as candidates to be corrected within the reference data; and subsequent to a correction of the reference data associated with one or more of the selected false positive individuals, generating based on the corrected reference data a new machine learning model for determining whether individuals meet said specific criteria. 10. The computer-implemented method of claim 5 , wherein the first machine learning model and the new machine model are different types of machine learning models. | 0.874422 |
9,218,803 | 1 | 6 | 1. A method comprising: receiving, on a device having a processor, text from a user for conversion to speech via a text-to-speech process; identifying, via the processor, a primary speech segment in a primary speech database which does not meet a need of the text-to-speech process; identifying, via the processor, a replacement speech segment which satisfies the need in a secondary speech database; and adding replacement speech segment to the primary database such that the primary database meets the need of the text-to-speech process. | 1. A method comprising: receiving, on a device having a processor, text from a user for conversion to speech via a text-to-speech process; identifying, via the processor, a primary speech segment in a primary speech database which does not meet a need of the text-to-speech process; identifying, via the processor, a replacement speech segment which satisfies the need in a secondary speech database; and adding replacement speech segment to the primary database such that the primary database meets the need of the text-to-speech process. 6. The method of claim 1 , wherein the primary speech segment comprises one of diphones, triphones, and phonemes. | 0.881053 |
9,721,005 | 6 | 7 | 6. The method of claim 1 , wherein customizing, by the processor, components of the QA system to answer questions from a viewpoint of the requested persona comprises: retrieving one or more personality attributes for the requested persona and one or more annotations associated with the one or more personality attributes; performing a search of a corpus of content based on the one or more annotations to identify portions of content having associated annotations that match at least one of the one or more annotations; and selecting at least a sub-set of the identified portions of content having associated annotations that match at least one of the one or more annotations as a persona-specific corpus. | 6. The method of claim 1 , wherein customizing, by the processor, components of the QA system to answer questions from a viewpoint of the requested persona comprises: retrieving one or more personality attributes for the requested persona and one or more annotations associated with the one or more personality attributes; performing a search of a corpus of content based on the one or more annotations to identify portions of content having associated annotations that match at least one of the one or more annotations; and selecting at least a sub-set of the identified portions of content having associated annotations that match at least one of the one or more annotations as a persona-specific corpus. 7. The method of claim 6 , wherein a portion of content in the identified portions of content is selected for inclusion in the at least a sub-set of the identified portions of content based on a degree of matching of annotations associated with the portion of content to the one or more annotations associated with the one or more personality attributes. | 0.5 |
7,899,669 | 22 | 23 | 22. Speech recognition apparatus of claim 19 , comprising a task manager for assigning speech recognition means to application programs and determining the application program which corresponds to the speech recognition means with the best scoring speech recognition hypothesis. | 22. Speech recognition apparatus of claim 19 , comprising a task manager for assigning speech recognition means to application programs and determining the application program which corresponds to the speech recognition means with the best scoring speech recognition hypothesis. 23. Speech recognition apparatus of claim 22 , wherein the task manager is configured to notify the application program assigned to the best scoring speech recognition means with the recognition results of the best scoring speech recognition means. | 0.5 |
7,536,380 | 1 | 8 | 1. A method for optimizing a database query with look ahead predicate generation, the method comprising: (a) initiating processing of a database query on a computer that includes a processor and a memory, including fetching at least one record from a database based upon the database query; (b) performing look ahead predicate generation for the database query after processing of the database query has been initiated and at least one record has been fetched from the database, wherein performing look ahead predicate generation generates at least one additional predicate to the database query; and (c) fetching a least one additional record from the database based upon the database query using results of the look ahead predicate generation. | 1. A method for optimizing a database query with look ahead predicate generation, the method comprising: (a) initiating processing of a database query on a computer that includes a processor and a memory, including fetching at least one record from a database based upon the database query; (b) performing look ahead predicate generation for the database query after processing of the database query has been initiated and at least one record has been fetched from the database, wherein performing look ahead predicate generation generates at least one additional predicate to the database query; and (c) fetching a least one additional record from the database based upon the database query using results of the look ahead predicate generation. 8. The method of claim 1 , further comprising determining whether or not performing look ahead predicate generation for the database query improved the processing time for the database query. | 0.5 |
8,468,442 | 1 | 8 | 1. A method of viewing information associated with business and/or financial data, comprising: parsing a document to retrieve information associated with business and/or financial data, the document including the business and/or financial data and the associated information; processing the associated information to identify at least one sentence included in the associated information; and for each sentence of the at least one sentence identified in the processing, identifying a topic of the sentence, wherein identifying a topic of the sentence comprises: comparing the sentence to at least one category in a taxonomy corresponding to the business and/or financial data to determine whether the topic of the sentence corresponds to the at least one category of the taxonomy corresponding to the business and/or financial data, each category of the at least one category in the taxonomy corresponding to a meaning of a type of business data or a type of financial data; assigning, based at least in part on the comparing, at least one association strength to the sentence, each of the at least one association strength indicating a likelihood that the topic of the sentence actually corresponds to one of the at least one category in the taxonomy; filtering the at least one association strength to determine one or more categories of the at least one category in the taxonomy with which to match the sentence; and outputting the sentence matched, based at least in part on the filtering, with the one or more categories of the at least one category in the taxonomy; wherein: the business and/or financial data of the document comprises a plurality of items of business and/or financial content, each item of the plurality of items being categorized according to at least one second category of a second set of categories; a sentence of the at least one sentence relates to at least one item of the plurality of items; outputting the sentence matched with the one or more categories comprises outputting the sentence matched with a first category of the at least one category of the taxonomy; one or more categories of the at least one category in the taxonomy correspond to one or more second categories of the second set of categories; and the method further comprises: evaluating the first category of the taxonomy with which the sentence is matched to determine an item of the plurality of items to which the sentence is related, associating the item of the plurality of items with at least one portion of a structured document, and associating the sentence with the at least one portion of the structured document. | 1. A method of viewing information associated with business and/or financial data, comprising: parsing a document to retrieve information associated with business and/or financial data, the document including the business and/or financial data and the associated information; processing the associated information to identify at least one sentence included in the associated information; and for each sentence of the at least one sentence identified in the processing, identifying a topic of the sentence, wherein identifying a topic of the sentence comprises: comparing the sentence to at least one category in a taxonomy corresponding to the business and/or financial data to determine whether the topic of the sentence corresponds to the at least one category of the taxonomy corresponding to the business and/or financial data, each category of the at least one category in the taxonomy corresponding to a meaning of a type of business data or a type of financial data; assigning, based at least in part on the comparing, at least one association strength to the sentence, each of the at least one association strength indicating a likelihood that the topic of the sentence actually corresponds to one of the at least one category in the taxonomy; filtering the at least one association strength to determine one or more categories of the at least one category in the taxonomy with which to match the sentence; and outputting the sentence matched, based at least in part on the filtering, with the one or more categories of the at least one category in the taxonomy; wherein: the business and/or financial data of the document comprises a plurality of items of business and/or financial content, each item of the plurality of items being categorized according to at least one second category of a second set of categories; a sentence of the at least one sentence relates to at least one item of the plurality of items; outputting the sentence matched with the one or more categories comprises outputting the sentence matched with a first category of the at least one category of the taxonomy; one or more categories of the at least one category in the taxonomy correspond to one or more second categories of the second set of categories; and the method further comprises: evaluating the first category of the taxonomy with which the sentence is matched to determine an item of the plurality of items to which the sentence is related, associating the item of the plurality of items with at least one portion of a structured document, and associating the sentence with the at least one portion of the structured document. 8. The method of claim 1 , wherein: the plurality of items of business and/or financial content are arranged in a tabular format in the document; and the method further comprises categorizing the plurality of items arranged in the tabular format according to the second set of categories. | 0.84953 |
9,582,587 | 10 | 13 | 10. A computer implemented method comprising: searching, based in part on a term identifier associated with a term, a first portion of a user-term index comprising time-ordered database shards of records for post identifiers of posts that include the term and that are associated with connections of a user, where the user-term index comprises shards of records that are searched from newest to oldest, the searching comprising: matching the term identifier to corresponding term identifiers in shards of records within the selected portion, the selected portion including user identifiers associated with connections of the user, and wherein the matching identifies post identifiers for posts that include the term and are associated with a connection of the user; and retrieving posts from an index using the identified post identifiers, the retrieved posts for presentation to the user. | 10. A computer implemented method comprising: searching, based in part on a term identifier associated with a term, a first portion of a user-term index comprising time-ordered database shards of records for post identifiers of posts that include the term and that are associated with connections of a user, where the user-term index comprises shards of records that are searched from newest to oldest, the searching comprising: matching the term identifier to corresponding term identifiers in shards of records within the selected portion, the selected portion including user identifiers associated with connections of the user, and wherein the matching identifies post identifiers for posts that include the term and are associated with a connection of the user; and retrieving posts from an index using the identified post identifiers, the retrieved posts for presentation to the user. 13. The computer implemented method of claim 10 , wherein searching, based in part on a term identifier associated with a term, the first portion of the user-term index comprising time-ordered database shards of records for post identifiers of posts that include the term and that are associated with connections of a user comprises: performing a hash function to associate the user identifier to a particular partition of the user-term index; and selecting the particular partition of the user-term index. | 0.5 |
8,433,718 | 21 | 27 | 21. The method according to claim 13 , further comprising the step of translating the text string, if the text string is in a language differing from the first language, to produce a translated text string in the first language. | 21. The method according to claim 13 , further comprising the step of translating the text string, if the text string is in a language differing from the first language, to produce a translated text string in the first language. 27. The method according to claim 21 , wherein the step of translating is by at least one of human translation and machine translation. | 0.677033 |
8,037,070 | 10 | 11 | 10. The method of claim 9 , wherein the conversations comprise conversations over telephones, the method further comprising: sensing a contextual piece of information within the audio stream or as related to a status of one or more of the telephones; and inserting the contextual piece of information with the search string to narrow the automatically-submitted search query to the search engine, to thereby affect the search results thereof. | 10. The method of claim 9 , wherein the conversations comprise conversations over telephones, the method further comprising: sensing a contextual piece of information within the audio stream or as related to a status of one or more of the telephones; and inserting the contextual piece of information with the search string to narrow the automatically-submitted search query to the search engine, to thereby affect the search results thereof. 11. The method of claim 10 , wherein the status of the telephone comprises a location detected by a global positioning system (GPS) device located within the telephone, a time stamp, or a combination thereof. | 0.5 |
9,335,893 | 10 | 12 | 10. An apparatus comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following, determine a context associated with each of a plurality of groups, each of the groups comprising items; determine a current context at a device, wherein the current context specifies one or more time conditions and one or more geographic locations associated with the device; cause, at least in part, a generation of at least one relevance metric for the each group based, at least in part, on one or more factors associated with the items; determine a multi-dimensional relevance metric of each item of the each group; cause, at least in part, a generation of a group image for the each group based on a number M of images associated with the number M of items that are determined to have a multi-dimensional relevance metric value above a threshold value; in response to a prompt presented for input to indicate at least one of the items, cause, at least in part, a presentation of at least two group images, wherein each of the group images includes a collage of representative images of the each presented group, and the each presented group matches a group multi-dimensional relevance metric threshold value based on the at least one relevance metric; in response to one or more user inputs, generate a new group of items using the at least one of the items and one or more of the at least two group images; cause, at least in part, a presentation of the new group; determine an update to the multi-dimensional relevance metric of the each item based, at least in part, on a decay factor; update the collage of representative images of the each presented group based, at least in part, on the update to the multi-dimensional relevance metric of the each item; and cause, at least in part, an updated presentation of the at least two group images based on the updated collage of representative images of the each presented group, wherein the representative images of the each presented group are determined as most relevant to the current context at the device, and wherein the presented group images are identical-sized and distinguishable from each other. | 10. An apparatus comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following, determine a context associated with each of a plurality of groups, each of the groups comprising items; determine a current context at a device, wherein the current context specifies one or more time conditions and one or more geographic locations associated with the device; cause, at least in part, a generation of at least one relevance metric for the each group based, at least in part, on one or more factors associated with the items; determine a multi-dimensional relevance metric of each item of the each group; cause, at least in part, a generation of a group image for the each group based on a number M of images associated with the number M of items that are determined to have a multi-dimensional relevance metric value above a threshold value; in response to a prompt presented for input to indicate at least one of the items, cause, at least in part, a presentation of at least two group images, wherein each of the group images includes a collage of representative images of the each presented group, and the each presented group matches a group multi-dimensional relevance metric threshold value based on the at least one relevance metric; in response to one or more user inputs, generate a new group of items using the at least one of the items and one or more of the at least two group images; cause, at least in part, a presentation of the new group; determine an update to the multi-dimensional relevance metric of the each item based, at least in part, on a decay factor; update the collage of representative images of the each presented group based, at least in part, on the update to the multi-dimensional relevance metric of the each item; and cause, at least in part, an updated presentation of the at least two group images based on the updated collage of representative images of the each presented group, wherein the representative images of the each presented group are determined as most relevant to the current context at the device, and wherein the presented group images are identical-sized and distinguishable from each other. 12. An apparatus of claim 10 , wherein the current context further specifies at least one of a user identifier, an application executed on the device, a network service communicating with the device, a keyword from a sample of text, a topic for the sample of text, one or more network conditions, one or more applications being executed on the device, or a combination thereof. | 0.649628 |
9,990,432 | 1 | 8 | 1. A method, comprising the steps of: receiving, by a server computer communicatively coupled to a network and comprising at least one processor executing specific computer-executable instructions within a memory, 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; tokenizing, by the server computer, the domain name search string; identifying, by the server computer, a search string token within the domain name search string as a concept seed; executing, by the server computer, a first database command to create a data record storing the search string token in association with a concept id; executing, by the server computer, a crawl of: a domain name search log, or at least one domain name system (DNS) zone file; tokenizing, by the server computer, at least one domain name text string within the domain name search log or the at least one DNS zone file; identifying, by the server computer, within the at least one domain name text string, at least one synonym or translation of the search string token; executing, by the server computer, 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; identifying, by the server computer, 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; generating, by the server computer, 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 transmitting, by the server computer, the second GUI to the client computer for display. | 1. A method, comprising the steps of: receiving, by a server computer communicatively coupled to a network and comprising at least one processor executing specific computer-executable instructions within a memory, 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; tokenizing, by the server computer, the domain name search string; identifying, by the server computer, a search string token within the domain name search string as a concept seed; executing, by the server computer, a first database command to create a data record storing the search string token in association with a concept id; executing, by the server computer, a crawl of: a domain name search log, or at least one domain name system (DNS) zone file; tokenizing, by the server computer, at least one domain name text string within the domain name search log or the at least one DNS zone file; identifying, by the server computer, within the at least one domain name text string, at least one synonym or translation of the search string token; executing, by the server computer, 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; identifying, by the server computer, 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; generating, by the server computer, 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 transmitting, by the server computer, the second GUI to the client computer for display. 8. The method of claim 1 , wherein recommending the at least one available domain name further comprises the steps of: identifying, by the server computer, at least one interchangeable term; generating, by the server computer, an interchangeable term dictionary from the at least one interchangeable term; and ranking, by the server computer, the at least one available domain name according to the interchangeable term dictionary. | 0.506865 |
8,279,343 | 1 | 20 | 1. A summary content generation device, which uses digital broadcast signals having video data and subtitle data to generate summary content for a broadcast program, comprising: a subtitle character string extraction unit, for extracting a subtitle character string from subtitle data contained in said digital broadcast signals; a still image extraction unit, for extracting one still image corresponding to said subtitle character string from video data contained in said digital broadcast signals; and a summary content generation unit, for generating summary content, which displays, on a screen, said extracted subtitle character string together with said corresponding extracted still image, wherein said summary content generation unit decides the timing for switching display of the plurality of subtitle character strings and still images comprised by said summary content, based on the subtitle character strings. | 1. A summary content generation device, which uses digital broadcast signals having video data and subtitle data to generate summary content for a broadcast program, comprising: a subtitle character string extraction unit, for extracting a subtitle character string from subtitle data contained in said digital broadcast signals; a still image extraction unit, for extracting one still image corresponding to said subtitle character string from video data contained in said digital broadcast signals; and a summary content generation unit, for generating summary content, which displays, on a screen, said extracted subtitle character string together with said corresponding extracted still image, wherein said summary content generation unit decides the timing for switching display of the plurality of subtitle character strings and still images comprised by said summary content, based on the subtitle character strings. 20. The summary content generation device according to claim 1 , comprising anchor shot detection unit, which analyzes video data comprised by said digital broadcast signals and judges whether anchor shots, which are video intervals in which a main newscaster appears in a news program in video data comprised by said digital broadcast signals, appear in images, and wherein said still image extraction unit extracts images in which anchor shots appear as said still images. | 0.561111 |
9,477,991 | 71 | 72 | 71. The method of claim 53 , wherein the at least one identification of the geographic context region is determined via a communication system operated by a network provider and utilized by at least one of the plurality of computing devices. | 71. The method of claim 53 , wherein the at least one identification of the geographic context region is determined via a communication system operated by a network provider and utilized by at least one of the plurality of computing devices. 72. The method of claim 71 , wherein the communication system includes at least one of a wireless access point and a wired access point; and wherein the at least one identification of the geographic context region is determined via a location of the at least one of the wireless access point and the wired access point. | 0.5 |
9,257,120 | 1 | 2 | 1. A computer-implemented method comprising: receiving, by a first user device, an audio signal encoding an utterance; obtaining, by the first user device, a first speaker model that is specific to a first user of the first user device; determining, by the first user device, that a second user device used by a second user is co-located with the first user device; in response to determining that the second user device used by the second user is co-located with the first user device and receiving, by the first user device, the audio signal encoding the utterance, determining, by the first user device, whether the first user device has one or more settings that allow the first user device access to a second speaker model that is specific to the second user; in response to determining that the first user device has one or more settings that allow the first user device access to the second speaker model, obtaining, by the first user device from the second user device used by the second user, a second speaker model that is specific to the second user; determining, by the first user device, that the utterance was spoken by the first user using the first speaker model that is specific to the first user of the first user device and the second speaker model that is specific to the second user associated with the second user device; analyzing the audio signal to identify a command included in the utterance in response to determining that the utterance was spoken by the first user; and performing an action that corresponds with the command. | 1. A computer-implemented method comprising: receiving, by a first user device, an audio signal encoding an utterance; obtaining, by the first user device, a first speaker model that is specific to a first user of the first user device; determining, by the first user device, that a second user device used by a second user is co-located with the first user device; in response to determining that the second user device used by the second user is co-located with the first user device and receiving, by the first user device, the audio signal encoding the utterance, determining, by the first user device, whether the first user device has one or more settings that allow the first user device access to a second speaker model that is specific to the second user; in response to determining that the first user device has one or more settings that allow the first user device access to the second speaker model, obtaining, by the first user device from the second user device used by the second user, a second speaker model that is specific to the second user; determining, by the first user device, that the utterance was spoken by the first user using the first speaker model that is specific to the first user of the first user device and the second speaker model that is specific to the second user associated with the second user device; analyzing the audio signal to identify a command included in the utterance in response to determining that the utterance was spoken by the first user; and performing an action that corresponds with the command. 2. The method of claim 1 wherein determining, by the first user device, that the second user device is used by the second user co-located with the first user device comprises determining, by the first user device, that the second user device is co-located in a physical area near a physical location of the first user device. | 0.756006 |
8,762,398 | 2 | 6 | 2. The method according to claim 1 , wherein the designing the XML document comprises: defining an XML document including a preset structure and preset data using the user-defined tags; and separating the XML document into an XML document for normal data mapping, which is used when mapping the normal text, and an XML document for repetitive data mapping, which is used when mapping the repetitive text. | 2. The method according to claim 1 , wherein the designing the XML document comprises: defining an XML document including a preset structure and preset data using the user-defined tags; and separating the XML document into an XML document for normal data mapping, which is used when mapping the normal text, and an XML document for repetitive data mapping, which is used when mapping the repetitive text. 6. The method according to claim 2 , wherein at the defining the XML document, when attributes of the XML document are defined, the attributes are indicated such that names of the attributes are combined with the user-defined tag values. | 0.680593 |
6,094,506 | 23 | 26 | 23. The method of claim 17 wherein a characteristic of a sample pattern represents a pair of coordinates describing a starting point and an ending point of a stroke of the sample pattern. | 23. The method of claim 17 wherein a characteristic of a sample pattern represents a pair of coordinates describing a starting point and an ending point of a stroke of the sample pattern. 26. The method of claim 23 wherein the probability table is a position feature probability table. | 0.5 |
8,812,495 | 11 | 18 | 11. The method of claim 10 , wherein the step of determining a quality of recognition of one or more entities in the search content further comprises the steps of: identifying one or more content elements in the search content; assigning all content elements an expected weight corresponding to complete recognition; determining for each content element, a knowledge base entity corresponding thereto; decrementing the expected weight associated with a particular content element when it is determined that the particular content element is indirectly resolved, in accordance with one or more knowledge base components, to the corresponding knowledge base entity; and combining the weights for all content elements that are associated with a single knowledge entity corresponding to the knowledge base. | 11. The method of claim 10 , wherein the step of determining a quality of recognition of one or more entities in the search content further comprises the steps of: identifying one or more content elements in the search content; assigning all content elements an expected weight corresponding to complete recognition; determining for each content element, a knowledge base entity corresponding thereto; decrementing the expected weight associated with a particular content element when it is determined that the particular content element is indirectly resolved, in accordance with one or more knowledge base components, to the corresponding knowledge base entity; and combining the weights for all content elements that are associated with a single knowledge entity corresponding to the knowledge base. 18. The method of claim 11 , wherein the step of determining a quality of recognition of one or more associations in the search content further comprises the steps of: identifying one or more associations between each of the plurality of single knowledge entities corresponding to the knowledge base; and decrementing the expected weight associated with each of the plurality of single knowledge entities corresponding to a particular association when it is determined that the association is not directly resolved. | 0.5 |
8,600,750 | 18 | 20 | 18. Logic encoded on tangible non-transitory media for execution by a processor, and when executed operable to: analyze a corpus of audio data and transcription data corresponding to the audio data to determine a plurality of speaker types from the corpus of audio data and the corresponding transcription data; train for automatic speech recognition of each of the plurality of speaker types; receive audio data from an associated source; determine a selected one of the plurality of speaker types based on the audio data received from the associated source and a transcription of the audio received from the associated source; and selectively transcribe the audio data received from the associated source based on the selected one of the plurality of speaker types. | 18. Logic encoded on tangible non-transitory media for execution by a processor, and when executed operable to: analyze a corpus of audio data and transcription data corresponding to the audio data to determine a plurality of speaker types from the corpus of audio data and the corresponding transcription data; train for automatic speech recognition of each of the plurality of speaker types; receive audio data from an associated source; determine a selected one of the plurality of speaker types based on the audio data received from the associated source and a transcription of the audio received from the associated source; and selectively transcribe the audio data received from the associated source based on the selected one of the plurality of speaker types. 20. The logic of claim 18 , wherein logic is further operable to: re-determine speaker types from the corpus of audio data and corresponding transcription data; re-train the automatic speech recognition; and re-determine the speaker type for the source. | 0.546595 |
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