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8,467,443 | 2 | 5 | 2. The object priority order compositor as defined by claim 1 , wherein the drawing node list generator comprises an object priority order register, a drawing node extractor, and a field-node extractor. | 2. The object priority order compositor as defined by claim 1 , wherein the drawing node list generator comprises an object priority order register, a drawing node extractor, and a field-node extractor. 5. The object priority order compositor as defined by claim 2 , wherein the field node extractor extracts field-nodes and registers the field-nodes on a sub-list of a drawing node list while searching nodes from a root node of a scene tree. | 0.909091 |
8,641,425 | 1 | 15 | 1. A method of correlating achievements of a student from a first academic course to at least one learning objective of a second academic course for determining course credit in the second academic course from the achievements in the first academic course, the method comprising the steps of: providing a data processing machine having software installed thereon; providing a first learning objective for the first academic course; providing at least one assessment for the first academic course which is correlated to the first learning objective of the first academic course; providing a second learning objective of the second academic course which may be correlated with the first learning objective of the first academic course; determining a first score for the assessment for a predetermined student; using the software on the data processing machine to perform the following steps: (a) weighting the at least one assessment relative to other assessments for the first learning objective of the first academic course, (b) weighting the first learning objective of the first academic course relative to other first academic course learning objectives for correlation of the weighted first learning objective of the first academic course to the second learning objective of the second academic course, (c) determining at least one second score for the first learning objective of the first academic course based on the first score for the at least one assessment and the relative weighting of the at least one assessment associated with the first learning objective of the first academic course, (d) determining a third score for the second learning objective of the second academic course to correlate the weighted first learning objective to the second learning objective, the third score being based on the at least one second score for the first learning objective of the first academic course and the relative weighting of the first academic course learning objective, and (e) generating a report based upon the third score for the second learning objective of the second academic course for the student; and evaluating the third score of the second learning objective to determine whether the student is entitled to credit for the second academic course. | 1. A method of correlating achievements of a student from a first academic course to at least one learning objective of a second academic course for determining course credit in the second academic course from the achievements in the first academic course, the method comprising the steps of: providing a data processing machine having software installed thereon; providing a first learning objective for the first academic course; providing at least one assessment for the first academic course which is correlated to the first learning objective of the first academic course; providing a second learning objective of the second academic course which may be correlated with the first learning objective of the first academic course; determining a first score for the assessment for a predetermined student; using the software on the data processing machine to perform the following steps: (a) weighting the at least one assessment relative to other assessments for the first learning objective of the first academic course, (b) weighting the first learning objective of the first academic course relative to other first academic course learning objectives for correlation of the weighted first learning objective of the first academic course to the second learning objective of the second academic course, (c) determining at least one second score for the first learning objective of the first academic course based on the first score for the at least one assessment and the relative weighting of the at least one assessment associated with the first learning objective of the first academic course, (d) determining a third score for the second learning objective of the second academic course to correlate the weighted first learning objective to the second learning objective, the third score being based on the at least one second score for the first learning objective of the first academic course and the relative weighting of the first academic course learning objective, and (e) generating a report based upon the third score for the second learning objective of the second academic course for the student; and evaluating the third score of the second learning objective to determine whether the student is entitled to credit for the second academic course. 15. The method of claim 1 , wherein the weighting of the first learning objective for the first academic course for correlation to the second learning objective of the second academic course is a Boolean weighting. | 0.669753 |
8,117,136 | 6 | 11 | 6. A method for maintaining relationships between data for use with a mobile device, the method comprising: retrieving a record including a plurality of fields having one or more fields associated with a data request; determining a plurality of associations between the plurality of fields within the record, the plurality of associations describing relationships between the plurality of fields; determining an association score corresponding to each association and a field score corresponding to each field; and displaying a subset of the fields and associations between the subset of fields responsive to the corresponding association score and field score by: comparing each association score and field score to respective threshold values; and responsive to an association score and field score exceeding the respective threshold values, displaying the corresponding association and field. | 6. A method for maintaining relationships between data for use with a mobile device, the method comprising: retrieving a record including a plurality of fields having one or more fields associated with a data request; determining a plurality of associations between the plurality of fields within the record, the plurality of associations describing relationships between the plurality of fields; determining an association score corresponding to each association and a field score corresponding to each field; and displaying a subset of the fields and associations between the subset of fields responsive to the corresponding association score and field score by: comparing each association score and field score to respective threshold values; and responsive to an association score and field score exceeding the respective threshold values, displaying the corresponding association and field. 11. The method of claim 6 , wherein the association comprises a user-defined asymmetric relationship between a first field and a second field. | 0.95254 |
7,958,067 | 22 | 23 | 22. A method for managing medical records, comprising: training a classifier based on a medical diagnosis, wherein the classifier is a transductive classifier, and further comprising training the transductive classifier through iterative calculation using at least one predetermined cost factor, at least one seed document, and the medical records, wherein for each iteration of the calculations the cost factor is adjusted as a function of an expected label value; accessing a plurality of medical records; performing a document classification technique on the medical records using the classifier; and outputting an identifier of at least one of the medical records having a low probability of being associated with the medical diagnosis. | 22. A method for managing medical records, comprising: training a classifier based on a medical diagnosis, wherein the classifier is a transductive classifier, and further comprising training the transductive classifier through iterative calculation using at least one predetermined cost factor, at least one seed document, and the medical records, wherein for each iteration of the calculations the cost factor is adjusted as a function of an expected label value; accessing a plurality of medical records; performing a document classification technique on the medical records using the classifier; and outputting an identifier of at least one of the medical records having a low probability of being associated with the medical diagnosis. 23. The method of claim 22 , wherein the document classification technique includes a transductive process. | 0.871084 |
9,390,383 | 4 | 7 | 4. A system for training a program by optimizing a set of program parameter values, comprising: a first set of computational elements for modeling the program to be trained, the processors receiving an input vector and providing an output vector; a second set of computational elements for optimizing the parameter values using an iterative method, wherein successive updates of the parameter values are provided by a method of conjugate residuals, which computes each of the successive updates by solving a set of linear equations involving a Hessian matrix and a gradient vector; and a control unit for controlling the operations of the first and second sets of computational elements. | 4. A system for training a program by optimizing a set of program parameter values, comprising: a first set of computational elements for modeling the program to be trained, the processors receiving an input vector and providing an output vector; a second set of computational elements for optimizing the parameter values using an iterative method, wherein successive updates of the parameter values are provided by a method of conjugate residuals, which computes each of the successive updates by solving a set of linear equations involving a Hessian matrix and a gradient vector; and a control unit for controlling the operations of the first and second sets of computational elements. 7. A system as in claim 4 , wherein the method of conjugate residuals terminates the β parameter in the method of conjugate residuals has an absolute value that is not less than a pre-determined constant value. | 0.50237 |
8,229,820 | 1 | 8 | 1. A method of distributing market data, the method comprising: (a) transmitting from a computer device a first template defining a plurality of fields; (b) transmitting from the computer device a first market data message having a plurality of fields separated by delimiters and formatted in accordance with the first template; (c) after (b) transmitting from the computer device a second template defining a plurality of fields and being different from the first template; and (d) transmitting from the computer device a second market data message having a plurality of fields separated by delimiters and formatted in accordance with the second template. | 1. A method of distributing market data, the method comprising: (a) transmitting from a computer device a first template defining a plurality of fields; (b) transmitting from the computer device a first market data message having a plurality of fields separated by delimiters and formatted in accordance with the first template; (c) after (b) transmitting from the computer device a second template defining a plurality of fields and being different from the first template; and (d) transmitting from the computer device a second market data message having a plurality of fields separated by delimiters and formatted in accordance with the second template. 8. The method of claim 1 , wherein at least one delimiter is used to identify a repeated group in the first template. | 0.81129 |
7,856,446 | 11 | 15 | 11. A method for identifying, extracting using a processor, capturing, and leveraging expertise and knowledge comprising: observing, between and among peers and experts who show high affinity with regard to any of the users, assets, and topics/terms, a user heartbeat which comprises user activity, including mouse movement and stable pauses on an asset, while the asset is in a user display foreground; based upon the observing, detecting whether the asset is or is not useful for a given user; employing automatic techniques to extract patterns from at least the detecting whether the asset is or is not useful for a given user; and learning affinities between and among any of the users, assets and topics/terms from the extracted patterns for any of: automatically determining each user's peer group, predicting a desired destination of the users in a navigation context, calculating an expert and peer impact factor, an asset impact factor, or rareness, determining importance of an asset and/or expertise that individuals possess, without asking the individuals directly, automatically disambiguating query terms in an online search, and/or effecting a predictive query by suggesting search terms to the users or automatically inserting search terms into user queries to expand a search, wherein learning affinities includes applying a formula to lower weights of the term as the terms become associated with more assets. | 11. A method for identifying, extracting using a processor, capturing, and leveraging expertise and knowledge comprising: observing, between and among peers and experts who show high affinity with regard to any of the users, assets, and topics/terms, a user heartbeat which comprises user activity, including mouse movement and stable pauses on an asset, while the asset is in a user display foreground; based upon the observing, detecting whether the asset is or is not useful for a given user; employing automatic techniques to extract patterns from at least the detecting whether the asset is or is not useful for a given user; and learning affinities between and among any of the users, assets and topics/terms from the extracted patterns for any of: automatically determining each user's peer group, predicting a desired destination of the users in a navigation context, calculating an expert and peer impact factor, an asset impact factor, or rareness, determining importance of an asset and/or expertise that individuals possess, without asking the individuals directly, automatically disambiguating query terms in an online search, and/or effecting a predictive query by suggesting search terms to the users or automatically inserting search terms into user queries to expand a search, wherein learning affinities includes applying a formula to lower weights of the term as the terms become associated with more assets. 15. The method of claim 11 , further comprising: analyzing all observations; and via the analysis, generating a set of recommendations comprising distilled experiences from a community of users; wherein the recommendations age over time and are discarded if they have relatively little value; and wherein recommendations which are most valuable based on repeated usage are stored into a long term memory. | 0.664452 |
7,634,756 | 7 | 15 | 7. The article of manufacture of claim 5 , wherein the method further comprises: beginning execution of the joiner algorithm associated with the joiner component when a first consistent joiner input set arrives at the joiner input terminals, the first consistent joiner input set corresponding to any consistent splitter output set generated by the splitter algorithm associated with the associated splitter component; continuing execution of the joiner algorithm as and when a second consistent joiner input set arrives at the joiner input terminal the second consistent joiner input set corresponding to any other consistent splitter output set generated by the splitter algorithm associated with the associated splitter component; and completing execution of the joiner algorithm after all consistent joiner input sets have arrived at the joiner input terminals, the all consistent joiner input sets corresponding to all consistent splitter output sets generated by the splitter algorithm associated with the associated splitter component. | 7. The article of manufacture of claim 5 , wherein the method further comprises: beginning execution of the joiner algorithm associated with the joiner component when a first consistent joiner input set arrives at the joiner input terminals, the first consistent joiner input set corresponding to any consistent splitter output set generated by the splitter algorithm associated with the associated splitter component; continuing execution of the joiner algorithm as and when a second consistent joiner input set arrives at the joiner input terminal the second consistent joiner input set corresponding to any other consistent splitter output set generated by the splitter algorithm associated with the associated splitter component; and completing execution of the joiner algorithm after all consistent joiner input sets have arrived at the joiner input terminals, the all consistent joiner input sets corresponding to all consistent splitter output sets generated by the splitter algorithm associated with the associated splitter component. 15. The system of claim 7 further comprising: a first circuit configured to execute the joiner algorithm associated with the at least one joiner when a first consistent joiner input set arrives at the joiner input terminals, the first consistent joiner input set corresponding to a first in order consistent splitter output set generated by the splitter algorithm; a second circuit configured to continue to execute the joiner algorithm as and when a next consistent joiner input set arrives at the joiner input terminals, the next consistent joiner input set corresponding to a next in order consistent splitter output set generated by the splitter algorithm associated with the at least one splitter; and a third circuit configured to complete execution of the joiner algorithm after all consistent joiner input sets have arrived in corresponding order at the joiner input terminals, the all consistent joiner input sets corresponding to all consistent splitter output sets generated by the splitter algorithm associated with the associated at least one splitter. | 0.773115 |
9,910,842 | 1 | 2 | 1. A method for predicting a location of a field on a form based on an image of the form, the method comprising: receiving, by a computer system, binary data that represents the image of the form; creating a data structure based on the binary data by: analyzing the binary data, by the computer system, to determine a plurality of grid points, wherein the plurality of grid points define a plurality of rectangular boxes, and wherein each corner of each of the rectangular boxes is coincident with a grid point of the plurality of grid points; creating the data structure, by the computer system, so that each element of the data structure maps to a different one of the rectangular boxes; identifying, by the computer system, a plurality of visible line segments of the image by executing an image analysis algorithm that reads the binary data, that identifies visual data based on the binary data, and that analyzes the visual data to determine which of the visual data represent line segments; and for each line segment of the identified plurality of visible line segments: mapping the line segment, by the computer system, to a selected element of the data structure based on a determination that the line segment intersects a selected rectangular box that maps to the selected element; identifying the location of the field of the form by: determining, by the computer system, a location of a cursor being displayed at a display of the computer system after a user caused the cursor to move; determining, by the computer system, that the location of the cursor intersects a portion of the image that is being displayed at the display, and that maps to a first rectangular box; identifying a nearby line segment of the visible line segments, by the computer system, based on a proximity of a nearby rectangular box, that is mapped to the nearby line segment, to the first rectangular box; determining the location of the field based on a location of the nearby line segment; and determining an extent of the field based on an analysis of other nearby line segments; and determining coordinates of a rectangular box that represents the field based on the location of the field and the extent of the field; and displaying the rectangular box at the display. | 1. A method for predicting a location of a field on a form based on an image of the form, the method comprising: receiving, by a computer system, binary data that represents the image of the form; creating a data structure based on the binary data by: analyzing the binary data, by the computer system, to determine a plurality of grid points, wherein the plurality of grid points define a plurality of rectangular boxes, and wherein each corner of each of the rectangular boxes is coincident with a grid point of the plurality of grid points; creating the data structure, by the computer system, so that each element of the data structure maps to a different one of the rectangular boxes; identifying, by the computer system, a plurality of visible line segments of the image by executing an image analysis algorithm that reads the binary data, that identifies visual data based on the binary data, and that analyzes the visual data to determine which of the visual data represent line segments; and for each line segment of the identified plurality of visible line segments: mapping the line segment, by the computer system, to a selected element of the data structure based on a determination that the line segment intersects a selected rectangular box that maps to the selected element; identifying the location of the field of the form by: determining, by the computer system, a location of a cursor being displayed at a display of the computer system after a user caused the cursor to move; determining, by the computer system, that the location of the cursor intersects a portion of the image that is being displayed at the display, and that maps to a first rectangular box; identifying a nearby line segment of the visible line segments, by the computer system, based on a proximity of a nearby rectangular box, that is mapped to the nearby line segment, to the first rectangular box; determining the location of the field based on a location of the nearby line segment; and determining an extent of the field based on an analysis of other nearby line segments; and determining coordinates of a rectangular box that represents the field based on the location of the field and the extent of the field; and displaying the rectangular box at the display. 2. The method of claim 1 , wherein each of the rectangular boxes is associated with a different pixel of the image, and wherein a rectangular box of the plurality of rectangular boxes is a square. | 0.857558 |
9,349,202 | 17 | 18 | 17. A non-transitory computer-readable medium having instructions encoded thereon which, when executed by a processing device, causes the processing device to perform operations comprising: receiving an image of at least a portion of a physical text source; segmenting the image into a plurality of character blocks, each character block comprising one of a plurality of glyphs; identifying a first font for a first glyph, wherein identifying the first font comprises: analyzing shape and appearance characteristics of two or more of the plurality of glyphs; identifying a candidate font for the first glyph; determining that the candidate font is a most commonly used font for a related set of two or more of the plurality of glyphs; determining that the candidate font that is the most commonly used font is a dominant font for the related set of two or more of the plurality of glyphs; and identifying a final font for glyphs in the related set based at least in part on the dominant font; identifying a second font for a second glyph, the second font different than the first font; and generating a reflowable content file indicating characters, the first font and the second font. | 17. A non-transitory computer-readable medium having instructions encoded thereon which, when executed by a processing device, causes the processing device to perform operations comprising: receiving an image of at least a portion of a physical text source; segmenting the image into a plurality of character blocks, each character block comprising one of a plurality of glyphs; identifying a first font for a first glyph, wherein identifying the first font comprises: analyzing shape and appearance characteristics of two or more of the plurality of glyphs; identifying a candidate font for the first glyph; determining that the candidate font is a most commonly used font for a related set of two or more of the plurality of glyphs; determining that the candidate font that is the most commonly used font is a dominant font for the related set of two or more of the plurality of glyphs; and identifying a final font for glyphs in the related set based at least in part on the dominant font; identifying a second font for a second glyph, the second font different than the first font; and generating a reflowable content file indicating characters, the first font and the second font. 18. The non-transitory computer-readable medium of claim 17 , wherein identifying the first font comprises determining a signature vector comprising values indicative of shape and appearance characteristics for the first glyph and identifying the first font based at least in part on the signature vector for the first glyph. | 0.877173 |
7,984,034 | 27 | 28 | 27. The system of claim 23 , wherein the visual alert comprises a title of the parallel resource and a hyperlink of the parallel resource. | 27. The system of claim 23 , wherein the visual alert comprises a title of the parallel resource and a hyperlink of the parallel resource. 28. The system of claim 27 , wherein the operations further comprise: translating the search query to the second language; searching stored content based on the translated search query, the stored content relating to the parallel resource; and extracting a relevant section of the stored content based on the translated search query, the snippet including the relevant section. | 0.843308 |
9,081,463 | 7 | 9 | 7. A non-transitory computer-readable medium having computer-executable instructions stored thereon, the instructions comprising: instructions for receiving source code of a web page that includes code defining run-time editing capabilities for editing a first region and a second region of an output presentation of the web page; instructions for receiving, during runtime, a first request to edit the first region and a second request to edit the second region; instructions for determining that a client from which the first and second requests are received is authorized to perform a first plurality of run-time editing capabilities in the first region and a second plurality of run-time editing capabilities in the second region, wherein the second plurality of run-time editing capabilities includes at least one run-time editing capability that is not included in the first plurality of run-time editing capabilities that the client is authorized to perform for the first region, wherein the first and second pluralities of run-time editing capabilities are determined based on the code defining the run-time editing capabilities; and instructions for responding to the first and second requests by interpreting the code defining the run-time editing capabilities during run-time of the web page to enable the first and second pluralities of run-time editing capabilities that the client is authorized to perform in the respective first and second regions. | 7. A non-transitory computer-readable medium having computer-executable instructions stored thereon, the instructions comprising: instructions for receiving source code of a web page that includes code defining run-time editing capabilities for editing a first region and a second region of an output presentation of the web page; instructions for receiving, during runtime, a first request to edit the first region and a second request to edit the second region; instructions for determining that a client from which the first and second requests are received is authorized to perform a first plurality of run-time editing capabilities in the first region and a second plurality of run-time editing capabilities in the second region, wherein the second plurality of run-time editing capabilities includes at least one run-time editing capability that is not included in the first plurality of run-time editing capabilities that the client is authorized to perform for the first region, wherein the first and second pluralities of run-time editing capabilities are determined based on the code defining the run-time editing capabilities; and instructions for responding to the first and second requests by interpreting the code defining the run-time editing capabilities during run-time of the web page to enable the first and second pluralities of run-time editing capabilities that the client is authorized to perform in the respective first and second regions. 9. The non-transitory computer-readable medium of claim 7 further comprising: instructions for receiving run-time edits to the first and second regions that revise the output presentation of the web page; and publishing the revised web page for access by clients. | 0.805761 |
8,189,880 | 13 | 14 | 13. A graphical user interface for adding annotations to a photo album, the graphical user interface comprising: a group view area in which a plurality of face groups of the photo album are displayed, each displayed face group being selectable by a user, wherein the displayed face groups are enabled for a drag-and-drop operation to merge two or more face groups; and an annotation input interface via which a user can enter an annotation for a selected face group. | 13. A graphical user interface for adding annotations to a photo album, the graphical user interface comprising: a group view area in which a plurality of face groups of the photo album are displayed, each displayed face group being selectable by a user, wherein the displayed face groups are enabled for a drag-and-drop operation to merge two or more face groups; and an annotation input interface via which a user can enter an annotation for a selected face group. 14. The graphical user interface as recited in claim 13 , further comprising a thumbnail area in which thumbnails of a plurality of photos of a selected face group are displayed. | 0.83964 |
9,183,464 | 17 | 18 | 17. The face annotation system as recited in claim 16 , wherein the FRF module performs the selection of proper multiple MKL classifiers units through a specific layer of constructed database in the pyramid database unit by using a temporal-group context from personal collections of the current owner. | 17. The face annotation system as recited in claim 16 , wherein the FRF module performs the selection of proper multiple MKL classifiers units through a specific layer of constructed database in the pyramid database unit by using a temporal-group context from personal collections of the current owner. 18. The face annotation method as recited in claim 17 , wherein the FRF module performs the fusion of multiple face identification results to merge the returned selected face identification results. | 0.947256 |
9,122,751 | 7 | 10 | 7. An electronic device comprising: a processor component; an input/output (I/O) mechanism that enables manipulation of content within an instant messaging (IM) client executing on the electronic device; a mechanism for transmitting and receiving IM communication from the IM client; and a utility associated with the IM client executing on the processor component and which comprises code that enables completion of the functions of: receiving a selection of content within a transcript of an electronic communication; applying a label to the content selected within the transcript; assigning a unique URL to the content selected from within the transcript; and forwarding at least the content selected to a storage location accessible via the unique URL. | 7. An electronic device comprising: a processor component; an input/output (I/O) mechanism that enables manipulation of content within an instant messaging (IM) client executing on the electronic device; a mechanism for transmitting and receiving IM communication from the IM client; and a utility associated with the IM client executing on the processor component and which comprises code that enables completion of the functions of: receiving a selection of content within a transcript of an electronic communication; applying a label to the content selected within the transcript; assigning a unique URL to the content selected from within the transcript; and forwarding at least the content selected to a storage location accessible via the unique URL. 10. The electronic device of claim 7 , wherein said code for assigning the unique URL further comprises code for: generating prompts for entry of the unique URL, wherein manual entry of the unique URL is available, wherein said prompts are generated at one or more periods from among: before the selection of the content, wherein a received URL serves as a default, automatically utilized URL for content selected, wherein the default URL may be selectably changed after selection of the content; and after selection of the content, wherein a URL is required to be entered after selection of the content; and associating the unique URL with the content and assigning the unique URL to a predefined searchable location. | 0.709312 |
5,559,926 | 19 | 28 | 19. A method for training a speech recognition system to recognize a user's utterance, comprising the steps of: providing an utterance to the speech recognition system while in a training mode; monitoring a bio-signal derived from the user, said bio-signal being related to autonomic activity; and using said bio-signal to identify said utterance for retraining. | 19. A method for training a speech recognition system to recognize a user's utterance, comprising the steps of: providing an utterance to the speech recognition system while in a training mode; monitoring a bio-signal derived from the user, said bio-signal being related to autonomic activity; and using said bio-signal to identify said utterance for retraining. 28. The method of claim 19, wherein said bio-signal is related to the user's emotional state. | 0.732759 |
10,037,137 | 15 | 18 | 15. The electronic device of claim 14 , wherein at least one input field targeting characteristic of the plurality of input field targeting characteristics is weighted. | 15. The electronic device of claim 14 , wherein at least one input field targeting characteristic of the plurality of input field targeting characteristics is weighted. 18. The electronic device of claim 15 , wherein the weighting of the at least one input field targeting characteristic is based on a history of user inputs. | 0.941441 |
9,817,793 | 1 | 12 | 1. A method for formatting bidirectional text, the method comprising: identifying a subject line of an email, the email received by an email client, the email client configured to display text for reading in a particular direction, the subject line containing bidirectional text; identifying subject abbreviations in the subject line; identifying the particular direction the email client is configured to display text for reading; and formatting the subject line such that one or more of the subject abbreviations are moved to be displayed at one side of the subject line per the particular direction the email client is configured to display text for reading. | 1. A method for formatting bidirectional text, the method comprising: identifying a subject line of an email, the email received by an email client, the email client configured to display text for reading in a particular direction, the subject line containing bidirectional text; identifying subject abbreviations in the subject line; identifying the particular direction the email client is configured to display text for reading; and formatting the subject line such that one or more of the subject abbreviations are moved to be displayed at one side of the subject line per the particular direction the email client is configured to display text for reading. 12. The method of claim 1 , wherein formatting the subject line is accomplished by injecting Unicode Control Characters. | 0.882583 |
8,055,635 | 8 | 9 | 8. A computer program product having a plurality of executable instruction codes stored on a non-transitory computer storage medium, for verifying the correctness of query results, comprising: a set of instruction codes for receiving a query at a data repository; a set of instruction codes for executing the received query using a query logic; a set of instruction codes for producing a query output that includes a query result and verification information associated with the query result; a set of instruction codes for transferring the query output to a verification system, wherein the verification system includes verifying indexes migrated to the data repository from a second data repository wherein the indexes were also previously verified in the second data repository; and a set of instruction codes for verifying the integrity and completeness of the query result using the verification information included in the query output, wherein verifying the integrity includes verifying that the query result has not been tampered with, and wherein verifying the completeness of the query result includes verifying that there are no additions or omissions to the query result. | 8. A computer program product having a plurality of executable instruction codes stored on a non-transitory computer storage medium, for verifying the correctness of query results, comprising: a set of instruction codes for receiving a query at a data repository; a set of instruction codes for executing the received query using a query logic; a set of instruction codes for producing a query output that includes a query result and verification information associated with the query result; a set of instruction codes for transferring the query output to a verification system, wherein the verification system includes verifying indexes migrated to the data repository from a second data repository wherein the indexes were also previously verified in the second data repository; and a set of instruction codes for verifying the integrity and completeness of the query result using the verification information included in the query output, wherein verifying the integrity includes verifying that the query result has not been tampered with, and wherein verifying the completeness of the query result includes verifying that there are no additions or omissions to the query result. 9. The computer program product of claim 8 including repeating the verifying of the query result for step N+1 until an end of query execution, wherein the query result includes data records that satisfy the received query, and wherein the verifying the query result includes representing the addresses of metadata pages accessed in a step N as a function of all metadata pages accessed during previous steps. | 0.501222 |
4,433,601 | 18 | 19 | 18. Apparatus, as claimed in claims 3 or 4, wherein the harmony selection means further comprises means for enabling the performer to select one harmony having a defined chord type and a defined root note from a plurality of different harmonies defined by a plurality of different chord types having a plurality of different root notes. | 18. Apparatus, as claimed in claims 3 or 4, wherein the harmony selection means further comprises means for enabling the performer to select one harmony having a defined chord type and a defined root note from a plurality of different harmonies defined by a plurality of different chord types having a plurality of different root notes. 19. Apparatus, as claimed in claim 18, wherein the harmony selection means comprises: a keyboard including a plurality of playing keys operable by the performer, each playing key representing at least one note pitched in at least one octave position; and means for generating a playing key signal identifying each of the different notes represented by the playing keys operated by the performer, whereby the performer can select the defined chord type and defined root note. | 0.915808 |
9,600,169 | 13 | 23 | 13. One or more non-transitory computer-readable storage media storing instructions which, when processed by one or more processors cause: receiving, from a user via a user interface of a first device, mapping input that specifies a particular gesture, one or more first device actions to associate with the particular gesture, and a first device context to associate with the particular gesture and the one or more first device actions, wherein the first device context includes one or more devices, other than the first device, are in the presence of the first device, or the one or more devices have logged in to a particular user account; storing, in a non-transitory storage medium, a first mapping that specifies the particular gesture, the first device context, and the one or more first device actions, wherein the non-transitory storage medium further stores a second mapping that specifies the particular gesture, a second device context, and one or more second device actions; detecting, by the first device, performance of a given gesture; in response to detecting performance of the given gesture: determining that the given gesture matches the particular gesture that is specified in the first mapping and the second mapping; in response to determining that the given gesture matches the particular gesture specified in the first mapping and the second mapping, reading the first mapping to determine the first device context that is specified in the first mapping and reading the second mapping to determine the second device context that is specified in the second mapping; determining whether one or more conditions external to the first device, detected by the first device, match the first device context; responsive to determining that the one or more conditions external to the first device match the first device context, causing the first device to perform the one or more first device actions that are specified in the first mapping; determining whether the one or more conditions external to the first device, detected by the first device, match the second device context; and responsive to determining that the one or more conditions external to the first device match the second device context, causing the first device to perform the one or more second device actions that are specified in the second mapping. | 13. One or more non-transitory computer-readable storage media storing instructions which, when processed by one or more processors cause: receiving, from a user via a user interface of a first device, mapping input that specifies a particular gesture, one or more first device actions to associate with the particular gesture, and a first device context to associate with the particular gesture and the one or more first device actions, wherein the first device context includes one or more devices, other than the first device, are in the presence of the first device, or the one or more devices have logged in to a particular user account; storing, in a non-transitory storage medium, a first mapping that specifies the particular gesture, the first device context, and the one or more first device actions, wherein the non-transitory storage medium further stores a second mapping that specifies the particular gesture, a second device context, and one or more second device actions; detecting, by the first device, performance of a given gesture; in response to detecting performance of the given gesture: determining that the given gesture matches the particular gesture that is specified in the first mapping and the second mapping; in response to determining that the given gesture matches the particular gesture specified in the first mapping and the second mapping, reading the first mapping to determine the first device context that is specified in the first mapping and reading the second mapping to determine the second device context that is specified in the second mapping; determining whether one or more conditions external to the first device, detected by the first device, match the first device context; responsive to determining that the one or more conditions external to the first device match the first device context, causing the first device to perform the one or more first device actions that are specified in the first mapping; determining whether the one or more conditions external to the first device, detected by the first device, match the second device context; and responsive to determining that the one or more conditions external to the first device match the second device context, causing the first device to perform the one or more second device actions that are specified in the second mapping. 23. The one or more non-transitory computer-readable storage media of claim 13 , further comprising additional instructions which, when processed by the one or more processors, cause: detecting, by a second device of the one or more devices other than the first device, performance of the given gesture at the second device; in response to detecting performance of the given gesture by the second device: accessing, by the second device, a device hierarchy establishing a precedence among the first device and the second device of the one or more devices other than the first device; determining, based on the precedence of the second device relative to the first device in the device hierarchy, whether to cause the first device to perform the one or more first device actions. | 0.547674 |
8,140,330 | 3 | 4 | 3. The method of claim 2 wherein the segmenting step further comprises: extracting statistical characteristics of the silence regions using an endpoint detection process; computing a zero-crossing rate threshold for the silence regions computing an energy threshold for the speech segments; determining if the length of a speech segment is less than a defined length threshold; and saving a speech segment as a reference pattern if its length is less than the defined length threshold, or dividing the speech segment into two or more sub-segments if its length is greater than the defined length threshold. | 3. The method of claim 2 wherein the segmenting step further comprises: extracting statistical characteristics of the silence regions using an endpoint detection process; computing a zero-crossing rate threshold for the silence regions computing an energy threshold for the speech segments; determining if the length of a speech segment is less than a defined length threshold; and saving a speech segment as a reference pattern if its length is less than the defined length threshold, or dividing the speech segment into two or more sub-segments if its length is greater than the defined length threshold. 4. The method of claim 3 wherein the defined length threshold is determined by a process comprising one of experimental determination, or theoretical calculation. | 0.929991 |
8,930,807 | 9 | 11 | 9. A computer implemented method to render hypertext documents, the computer implemented method comprising: a client sending a hypertext request; receiving a valid web server response, the response comprising hypertext and a first at least one timeliness tag; looking up a timeliness tag rule corresponding to the first at least one timeliness tag; determining that the timeliness tag rule is to display a placeholder in response to an expired tag being associated with a first HyperText Markup Language (HTML) element; responsive to the determination that the timeliness tag rule is to display the placeholder in response to the expired tag, determining whether the hypertext request is made during a valid period, as described by the first at least one timeliness tag, and in response, rendering the placeholder corresponding to the first HTML element having the first at least one timeliness tag and rendering a second HTML element having an unexpired timeliness tag; and parsing a metadata registry reference within the first at least one timeliness tag; and responsive to parsing the metadata registry reference, directing a request to a metadata registry described in the metadata registry reference, wherein the request comprises a unique identifier of content corresponding to the first at least one timeliness tag. | 9. A computer implemented method to render hypertext documents, the computer implemented method comprising: a client sending a hypertext request; receiving a valid web server response, the response comprising hypertext and a first at least one timeliness tag; looking up a timeliness tag rule corresponding to the first at least one timeliness tag; determining that the timeliness tag rule is to display a placeholder in response to an expired tag being associated with a first HyperText Markup Language (HTML) element; responsive to the determination that the timeliness tag rule is to display the placeholder in response to the expired tag, determining whether the hypertext request is made during a valid period, as described by the first at least one timeliness tag, and in response, rendering the placeholder corresponding to the first HTML element having the first at least one timeliness tag and rendering a second HTML element having an unexpired timeliness tag; and parsing a metadata registry reference within the first at least one timeliness tag; and responsive to parsing the metadata registry reference, directing a request to a metadata registry described in the metadata registry reference, wherein the request comprises a unique identifier of content corresponding to the first at least one timeliness tag. 11. The computer implemented method of claim 9 , further comprising: in response to a determination that the hypertext request is made during a valid period, rendering the second HTML element. | 0.867769 |
8,744,847 | 1 | 14 | 1. A method of assessing a key child's expressive language development, comprising: processing an audio recording taken in the key child's language environment to identify segments of the recording that correspond to the key child's vocalizations, wherein a computing device configured to perform the processing is used and the processing includes categorizing a plurality of segments of the audio recording into a plurality of categories, the plurality of categories including categories selected from the group consisting of vocalizations, cries, vegetative sounds, and fixed sounds, and determining which of the plurality of segments characterized as vocalizations are segments of the recording that correspond to the key child's vocalizations by comparing the plurality of segments characterized as vocalizations to a plurality of models; applying an adult automatic speech recognition phone decoder to segments of the key child's vocalizations to identify each occurrence of each of a plurality of phone categories, wherein each of the phone categories corresponds to a pre-defined speech sound; determining a distribution for the phone categories; and using the distribution in an age-based model to assess the key child's expressive language development. | 1. A method of assessing a key child's expressive language development, comprising: processing an audio recording taken in the key child's language environment to identify segments of the recording that correspond to the key child's vocalizations, wherein a computing device configured to perform the processing is used and the processing includes categorizing a plurality of segments of the audio recording into a plurality of categories, the plurality of categories including categories selected from the group consisting of vocalizations, cries, vegetative sounds, and fixed sounds, and determining which of the plurality of segments characterized as vocalizations are segments of the recording that correspond to the key child's vocalizations by comparing the plurality of segments characterized as vocalizations to a plurality of models; applying an adult automatic speech recognition phone decoder to segments of the key child's vocalizations to identify each occurrence of each of a plurality of phone categories, wherein each of the phone categories corresponds to a pre-defined speech sound; determining a distribution for the phone categories; and using the distribution in an age-based model to assess the key child's expressive language development. 14. The method of claim 1 , wherein the vegetative sounds include non-vocal sounds related to respiration and digestions. | 0.910635 |
8,117,242 | 32 | 39 | 32. The computer program product of claim 21 , wherein the computer program product is configured to cooperate with at least one mobile application for accessing at least one of the different online applications utilizing a mobile device, the computer program product further configured to allow the at least one mobile application to provide at least a portion of a functionality of the at least one of the different online applications. | 32. The computer program product of claim 21 , wherein the computer program product is configured to cooperate with at least one mobile application for accessing at least one of the different online applications utilizing a mobile device, the computer program product further configured to allow the at least one mobile application to provide at least a portion of a functionality of the at least one of the different online applications. 39. The computer program product of claim 32 , wherein the computer program product is configured such that the portion of the functionality includes searching functionality. | 0.989814 |
8,572,074 | 4 | 5 | 4. One or more computer-storage media having computer-executable instructions embodied thereon that, when executed, perform a method for automatically organizing search results for a plurality for search queries according to task groups, the method comprising: aggregating a gallery of entities into a query class, wherein the gallery of entities corresponds to queries that share a common categorization; assigning a dictionary to the query class, wherein the dictionary comprises a list of terms that are drawn from one or more sources; identifying the task groups from the list of terms within the dictionary by utilizing a process comprising: (a) analyzing patterns of user search behavior, based on historical user click-through data, to select one or more terms from the list of terms that reflect popular user search intents; (b) ranking the one or more terms based on predetermined parameters to produce an ordering; and (c) based on the ordering, declaring a set of the one or more terms that are highest ranked in the task groups; and storing on the one or more computer-readable media the task groups in association with the query class; receiving a user-issued query comprised of search terms that map to an entity associated with the dictionary; incident to receiving the user-issued query, selecting the dictionary from a plurality of dictionaries and recognizing the task groups associated with the dictionary; gathering search results that are responsive to the query in conjunction with each of the task groups, respectively; and instructing a presentation device to render on the UT display the search results proximate to each of the task groups with which each of the search results corresponds, respectively. | 4. One or more computer-storage media having computer-executable instructions embodied thereon that, when executed, perform a method for automatically organizing search results for a plurality for search queries according to task groups, the method comprising: aggregating a gallery of entities into a query class, wherein the gallery of entities corresponds to queries that share a common categorization; assigning a dictionary to the query class, wherein the dictionary comprises a list of terms that are drawn from one or more sources; identifying the task groups from the list of terms within the dictionary by utilizing a process comprising: (a) analyzing patterns of user search behavior, based on historical user click-through data, to select one or more terms from the list of terms that reflect popular user search intents; (b) ranking the one or more terms based on predetermined parameters to produce an ordering; and (c) based on the ordering, declaring a set of the one or more terms that are highest ranked in the task groups; and storing on the one or more computer-readable media the task groups in association with the query class; receiving a user-issued query comprised of search terms that map to an entity associated with the dictionary; incident to receiving the user-issued query, selecting the dictionary from a plurality of dictionaries and recognizing the task groups associated with the dictionary; gathering search results that are responsive to the query in conjunction with each of the task groups, respectively; and instructing a presentation device to render on the UT display the search results proximate to each of the task groups with which each of the search results corresponds, respectively. 5. The one or more computer-storage media of claim 4 , wherein the process of identifying further comprises: accessing a query log that includes a plurality of user-initiated queries corresponding to the entities designated as part of the query class; analyzing components of each of the plurality of user-initiated queries to identify one or more terms that map to at least one of the entities and to identify modifiers of the one or more terms; compiling a set of the modifiers that most frequently occur as a component within the plurality of user-initiated queries; and establishing the most-frequently-occurring set of modifiers as the task groups. | 0.556386 |
8,281,231 | 1 | 7 | 1. A method of synchronizing text with audio in a multimedia file, wherein the multimedia file includes previously synchronized video and audio, wherein the multimedia file has a start time and a stop time that defines a timeline for the multimedia file, wherein the frames of the video and the corresponding audio are each associated with respective points in time along the timeline, comprising the steps of: receiving the multimedia file and parsing the audio therefrom, but maintaining the timeline synchronization between the video and the audio; receiving closed-captioned data associated with the multimedia file, wherein the closed-captioned data contains closed-captioned text, wherein each word of the closed-captioned text is associated with a corresponding word spoken in the audio, wherein each word of the closed-captioned text has a high degree of accuracy with the corresponding word spoken in the audio but a low correlation with the respective point in time along the timeline at which the corresponding word was spoken in the audio; using automated speech recognition (ASR) software, generating ASR text of the parsed audio, wherein each word of the ASR text is associated approximately with the corresponding words spoken in the audio, wherein each word of the ASR text has a lower degree of accuracy with the corresponding words spoken in the audio than the respective words of the closed-captioned text but a high correlation with the respective point in time along the timeline at which the corresponding word was spoken in the audio; thereafter, using N-gram analysis, comparing each word of the closed-captioned text with a plurality of words of the ASR text until a match is found; for each matched word from the closed-captioned text, associating therewith the respective point in time along the timeline of the matched word from the ASR text corresponding therewith, whereby each closed-captioned word is associated with a respective point on the timeline corresponding to the same point in time on the timeline in which the word is actually spoken in the audio and occurs within the video. | 1. A method of synchronizing text with audio in a multimedia file, wherein the multimedia file includes previously synchronized video and audio, wherein the multimedia file has a start time and a stop time that defines a timeline for the multimedia file, wherein the frames of the video and the corresponding audio are each associated with respective points in time along the timeline, comprising the steps of: receiving the multimedia file and parsing the audio therefrom, but maintaining the timeline synchronization between the video and the audio; receiving closed-captioned data associated with the multimedia file, wherein the closed-captioned data contains closed-captioned text, wherein each word of the closed-captioned text is associated with a corresponding word spoken in the audio, wherein each word of the closed-captioned text has a high degree of accuracy with the corresponding word spoken in the audio but a low correlation with the respective point in time along the timeline at which the corresponding word was spoken in the audio; using automated speech recognition (ASR) software, generating ASR text of the parsed audio, wherein each word of the ASR text is associated approximately with the corresponding words spoken in the audio, wherein each word of the ASR text has a lower degree of accuracy with the corresponding words spoken in the audio than the respective words of the closed-captioned text but a high correlation with the respective point in time along the timeline at which the corresponding word was spoken in the audio; thereafter, using N-gram analysis, comparing each word of the closed-captioned text with a plurality of words of the ASR text until a match is found; for each matched word from the closed-captioned text, associating therewith the respective point in time along the timeline of the matched word from the ASR text corresponding therewith, whereby each closed-captioned word is associated with a respective point on the timeline corresponding to the same point in time on the timeline in which the word is actually spoken in the audio and occurs within the video. 7. The method of claim 1 wherein the step of comparing comprises matching strings of characters between the words of the closed-captioned text with the words of the ASR text to attempt to find approximate matches based on the proximity of the respective points on the timeline of the respective words. | 0.768462 |
8,024,733 | 37 | 43 | 37. The apparatus of claim 26 , wherein the processor further executes the computer executable instructions to: determine a duration of the checkpoint interval for a batch component by the batch container; execute the batch component within the checkpoint interval by the batch container; and store at least one checkpoint cursor upon completion of the checkpoint interval duration by the batch container. | 37. The apparatus of claim 26 , wherein the processor further executes the computer executable instructions to: determine a duration of the checkpoint interval for a batch component by the batch container; execute the batch component within the checkpoint interval by the batch container; and store at least one checkpoint cursor upon completion of the checkpoint interval duration by the batch container. 43. The apparatus of claim 37 , wherein the processor further executes the computer executable instructions to: receive a set of deployment descriptors for the batch component by the batch container; and determine the duration of the checkpoint interval by the batch container using the deployment descriptors and the other processing workloads. | 0.921376 |
9,530,069 | 1 | 5 | 1. A computer-implemented method, comprising: under the control of one or more computer systems configured with executable instructions, receiving an input image that includes at least one image variation; filtering and segmenting the input image; selecting regions within the filtered and segmented input image having connected components; creating a mask corresponding to the regions of connected components, the mask including bounding boxes that at least partially enclose corresponding regions of the connected components; intersecting the filtered and segmented input image with the mask to produce a first output image; separately processing the filtered and segmented input image corresponding to the mask to create a binary output image; separately recognizing text in the first output image and in the binaryoutput image using an optical character recognizer; and combining the separately recognized text from the first output image and from the binary output image to produce a single output. | 1. A computer-implemented method, comprising: under the control of one or more computer systems configured with executable instructions, receiving an input image that includes at least one image variation; filtering and segmenting the input image; selecting regions within the filtered and segmented input image having connected components; creating a mask corresponding to the regions of connected components, the mask including bounding boxes that at least partially enclose corresponding regions of the connected components; intersecting the filtered and segmented input image with the mask to produce a first output image; separately processing the filtered and segmented input image corresponding to the mask to create a binary output image; separately recognizing text in the first output image and in the binaryoutput image using an optical character recognizer; and combining the separately recognized text from the first output image and from the binary output image to produce a single output. 5. The computer-implemented method of claim 1 , wherein selecting regions having the connected components includes identifying regions of connected pixels based on an intensity value of the pixels and a distance between the pixels. | 0.796655 |
9,836,448 | 17 | 21 | 17. A non-transitory computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to at least perform: responsive to user input corresponding to at least one keystroke received at the apparatus during text entry in a text editing program, disambiguate ambiguous keystrokes from the user input at least based on simultaneous use of a common language dictionary, a user dictionary, and a first specific subject matter lexicon selected by a user of the apparatus, wherein the first selected subject matter lexicon is related to a first particular professional area; then, in response to a user input indicating a lexicon swap, display a plurality of specific subject matter lexicons to the user of the apparatus while causing the apparatus to display text previously entered within the text editing program; receive a selection by the user of the apparatus of a second specific subject matter lexicon from the plurality of specific subject matter lexicons displayed to the user, wherein the second specific subject matter lexicon is related to a second particular professional area; disambiguate the ambiguous keystrokes from the user input at least based on simultaneous use of the common language dictionary, the user dictionary, and the second specific subject matter lexicon; and display at least one disambiguation result to the user of the apparatus. | 17. A non-transitory computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to at least perform: responsive to user input corresponding to at least one keystroke received at the apparatus during text entry in a text editing program, disambiguate ambiguous keystrokes from the user input at least based on simultaneous use of a common language dictionary, a user dictionary, and a first specific subject matter lexicon selected by a user of the apparatus, wherein the first selected subject matter lexicon is related to a first particular professional area; then, in response to a user input indicating a lexicon swap, display a plurality of specific subject matter lexicons to the user of the apparatus while causing the apparatus to display text previously entered within the text editing program; receive a selection by the user of the apparatus of a second specific subject matter lexicon from the plurality of specific subject matter lexicons displayed to the user, wherein the second specific subject matter lexicon is related to a second particular professional area; disambiguate the ambiguous keystrokes from the user input at least based on simultaneous use of the common language dictionary, the user dictionary, and the second specific subject matter lexicon; and display at least one disambiguation result to the user of the apparatus. 21. The non-transitory computer readable storage medium of claim 17 , further configured to cause the apparatus to provide a download link pointing to a lexicon server. | 0.835938 |
8,010,938 | 19 | 20 | 19. A computer system as claimed in claim 18 wherein the target domain is a computer programming model; and the pattern class is a meta-class in an object-oriented model. | 19. A computer system as claimed in claim 18 wherein the target domain is a computer programming model; and the pattern class is a meta-class in an object-oriented model. 20. A computer system as claimed in claim 19 wherein the target domain is a Unified Modeling Language 2 structure. | 0.95578 |
7,529,657 | 10 | 11 | 10. The method of claim 1 wherein the grammar components comprise at least two of a rules-based context-free grammar, a statistical model, an n-gram model, and a hidden markov model (HMM). | 10. The method of claim 1 wherein the grammar components comprise at least two of a rules-based context-free grammar, a statistical model, an n-gram model, and a hidden markov model (HMM). 11. The method of claim 10 wherein the grammar configuration parameters map selected grammar components to the parts of the semantic structure. | 0.952961 |
7,788,263 | 1 | 3 | 1. A computer-implemented method implemented using instructions stored on a computer-readable medium and executable by a computing device, the method comprising: initializing event parameters to identify a number of salient events from a corpus of documents, wherein the events comprise occurrences that are described in the corpus of documents and are identified based on a salient score calculated from the distance between peaks on a graph, the peaks on the graph corresponding to each respective one of the events; probabilistically determining, using a generative model, whether documents are associated with a first event to detect representative events of the number of salient events, wherein probabilistically determining comprises: estimating parameters for the generative model using the event parameters; generating event clusters to cluster events reported by the documents using estimated generative model parameters; for each event cluster: increasing or decreasing a number of events to represent a corresponding event; if the number of events is not a minimum or a maximum number of events: (a) again performing operations associated with initializing the event parameters to generate re-initialized event parameters; and (b) using the generative model to probabilistically detect events from salient ones of the documents using the re-initialized event parameters; and if the number of events is a minimum or a maximum number of events, summarizing event(s) associated with the event cluster to assign content of one or more documents to respective events; selecting the first event reported by one or more of the documents; and for each entity associated with the first event: generating a respective news article for the first event; and determining a time for the respective news article. | 1. A computer-implemented method implemented using instructions stored on a computer-readable medium and executable by a computing device, the method comprising: initializing event parameters to identify a number of salient events from a corpus of documents, wherein the events comprise occurrences that are described in the corpus of documents and are identified based on a salient score calculated from the distance between peaks on a graph, the peaks on the graph corresponding to each respective one of the events; probabilistically determining, using a generative model, whether documents are associated with a first event to detect representative events of the number of salient events, wherein probabilistically determining comprises: estimating parameters for the generative model using the event parameters; generating event clusters to cluster events reported by the documents using estimated generative model parameters; for each event cluster: increasing or decreasing a number of events to represent a corresponding event; if the number of events is not a minimum or a maximum number of events: (a) again performing operations associated with initializing the event parameters to generate re-initialized event parameters; and (b) using the generative model to probabilistically detect events from salient ones of the documents using the re-initialized event parameters; and if the number of events is a minimum or a maximum number of events, summarizing event(s) associated with the event cluster to assign content of one or more documents to respective events; selecting the first event reported by one or more of the documents; and for each entity associated with the first event: generating a respective news article for the first event; and determining a time for the respective news article. 3. The method of claim 1 , wherein the generative model models document content and timestamps associated with events with different Bayesian mixture models. | 0.830454 |
7,702,458 | 11 | 20 | 11. An apparatus for entering a street name to determine an address of a destination for a navigation system, comprising: means for displaying a screen for searching a street name, the screen including a street name input field for accepting a user's input of characters either by a full name or a base name of a street; means for distinguishing a non-base name element from a base name of a street name in the input character and for displaying the non-base name element by a selected method on the screen; means for comparing the base name detected from the characters input by the user with entries in a base name database that stores base names of streets; means for retrieving base names from the base name database that match the base name detected from the characters input by the user and displaying a list of the retrieved base names; means for repeating the above steps of comparing and retrieving the base names every time when additional information is supplied by the user; and means for retrieving full names of streets from a full name database that stores full names of streets to determine a correct address of the destination. | 11. An apparatus for entering a street name to determine an address of a destination for a navigation system, comprising: means for displaying a screen for searching a street name, the screen including a street name input field for accepting a user's input of characters either by a full name or a base name of a street; means for distinguishing a non-base name element from a base name of a street name in the input character and for displaying the non-base name element by a selected method on the screen; means for comparing the base name detected from the characters input by the user with entries in a base name database that stores base names of streets; means for retrieving base names from the base name database that match the base name detected from the characters input by the user and displaying a list of the retrieved base names; means for repeating the above steps of comparing and retrieving the base names every time when additional information is supplied by the user; and means for retrieving full names of streets from a full name database that stores full names of streets to determine a correct address of the destination. 20. An apparatus for entering a street name as defined in claim 11 , wherein said non-base element of a street name includes a prefix, road type, and a suffix. | 0.786863 |
9,870,591 | 1 | 16 | 1. A blockchain configured distributed architecture-based system in a communication network, said system comprising: a memory circuit communicatively connected to said communication network that stores a plurality of digital profiles associated with a plurality of crowdsourced experts, and further stores a plurality of segmented digital profiles associated with each of said digital profiles, wherein said segmented digital profiles and digital profiles are created based on a plurality of sources distributed and electronically linked across said communication network; a processor coupled with the memory circuit to execute instructions for evaluating an expert, the instructions comprising: a credentialing engine that allows a plurality of crowdsourced respondents to respond to said segmented digital profiles associated with each of said plurality of experts and credential said plurality of experts and determine crowdsourced credentialed expertise, wherein the credentialing of each of said segmented digital profiles associated with an expert of said plurality of experts contribute to credentialing of a digital profile of said expert upon collation of said credentialed segmented digital profiles, and wherein said segmented digital profiles associated with said experts are credentialed from a plurality of respondents using a computerized crowdsourcing index, wherein said computerized crowdsourcing index is indicative of number of respondents credentialing an expert and dynamically increases with an increase in said number of respondents; an expert scoring module to: determine a set of attributes for said experts, said set of attributes including one or more of said crowdsourced credentialed expertise determined based on said credentialing of said segmented digital profiles of said experts by said respondents, reputation of said experts indicative of a trust of a relevant community on said experts, and officiality indicative of a position or a designation of said experts in a relevant job, wherein each of said attributes are assigned varying computer-calculated weights; and determine an aggregate score of an expert based on said one or more attributes in association with the assigned weights; an electronic document scoring engine to receive and process comments and document ratings for an electronic document by said crowdsourced experts, wherein said crowdsourced experts have an aggregate score greater than a defined threshold, the document scoring engine comprising: a natural language processing-based (NLP-based) analysis engine to process textual information-based reviews and comments generated as part of textual review of said electronic document by said crowdsourced experts; a visual scoring engine for processing visual and non-textual feedback and reviews by the crowdsourced experts, wherein the visual scoring engine comprises: an eye tracks processor controlled by a special purpose microprocessor to receive eye track inputs from respective eye tracking systems associated with computing devices of said crowdsourced experts and process said eye track inputs to associate a review score based on predefined eye track patterns; and a micro expressions processor to receive data indicative of micro facial expressions extracted by respective micro expressions sensors associated with said computing devices of said crowdsourced experts, wherein said micro expressions processor comprises an image processing circuitry and an associated memory to interpret said micro facial expressions and compare them with predefined facial patterns to associate a review score based on said extracted micro facial expressions, wherein, the document scoring engine further configured to: associate an aggregate score to said electronic document based on aggregation of individual textual and visual review scores obtained by processing of said textual and said visual reviews by said crowdsourced experts who review the document; and display on a graphical user interface device, an output indicative of an aggregate score of the document reviewed by said crowdsourced experts along with information about who reviewed and how many times reviewed the document; an expert identity validation device to verify identities of the crowdsourced experts during or prior to review, wherein said expert identity validation device comprises: a device patterns assessment device to receive and process device information extracted by respective agent devices associated with said computing devices of said crowdsourced experts and verify the extracted device information with predefined device information for the respective crowdsourced experts; a network patterns assessment device to receive and process network information extracted by said respective agent devices associated with said computing devices of said crowdsourced experts and verify the extracted network information with predefined network information of the respective crowdsourced experts; a geo-spatial mapping device to perform geo-tagging of the crowdsourced experts and the documents reviewed by said crowdsourced experts and compare the geo-tags with pre-stored geo-spatial information about the experts for processing validation, wherein the geo-tagging is performed based on geo-spatial information received from a global positioning system (GPS)-based device; and a facial expression validation device to receive and process facial expressions received from respective facial expression sensors associated with said computing devices of said crowdsourced experts and verify identity in accordance with respective predefined facial patterns of said crowdsourced experts, wherein the facial expression validation device comprises a digital acquisition unit and multichannel amplifiers for pre-processing and amplification of signals transmitted by said facial expression sensors. | 1. A blockchain configured distributed architecture-based system in a communication network, said system comprising: a memory circuit communicatively connected to said communication network that stores a plurality of digital profiles associated with a plurality of crowdsourced experts, and further stores a plurality of segmented digital profiles associated with each of said digital profiles, wherein said segmented digital profiles and digital profiles are created based on a plurality of sources distributed and electronically linked across said communication network; a processor coupled with the memory circuit to execute instructions for evaluating an expert, the instructions comprising: a credentialing engine that allows a plurality of crowdsourced respondents to respond to said segmented digital profiles associated with each of said plurality of experts and credential said plurality of experts and determine crowdsourced credentialed expertise, wherein the credentialing of each of said segmented digital profiles associated with an expert of said plurality of experts contribute to credentialing of a digital profile of said expert upon collation of said credentialed segmented digital profiles, and wherein said segmented digital profiles associated with said experts are credentialed from a plurality of respondents using a computerized crowdsourcing index, wherein said computerized crowdsourcing index is indicative of number of respondents credentialing an expert and dynamically increases with an increase in said number of respondents; an expert scoring module to: determine a set of attributes for said experts, said set of attributes including one or more of said crowdsourced credentialed expertise determined based on said credentialing of said segmented digital profiles of said experts by said respondents, reputation of said experts indicative of a trust of a relevant community on said experts, and officiality indicative of a position or a designation of said experts in a relevant job, wherein each of said attributes are assigned varying computer-calculated weights; and determine an aggregate score of an expert based on said one or more attributes in association with the assigned weights; an electronic document scoring engine to receive and process comments and document ratings for an electronic document by said crowdsourced experts, wherein said crowdsourced experts have an aggregate score greater than a defined threshold, the document scoring engine comprising: a natural language processing-based (NLP-based) analysis engine to process textual information-based reviews and comments generated as part of textual review of said electronic document by said crowdsourced experts; a visual scoring engine for processing visual and non-textual feedback and reviews by the crowdsourced experts, wherein the visual scoring engine comprises: an eye tracks processor controlled by a special purpose microprocessor to receive eye track inputs from respective eye tracking systems associated with computing devices of said crowdsourced experts and process said eye track inputs to associate a review score based on predefined eye track patterns; and a micro expressions processor to receive data indicative of micro facial expressions extracted by respective micro expressions sensors associated with said computing devices of said crowdsourced experts, wherein said micro expressions processor comprises an image processing circuitry and an associated memory to interpret said micro facial expressions and compare them with predefined facial patterns to associate a review score based on said extracted micro facial expressions, wherein, the document scoring engine further configured to: associate an aggregate score to said electronic document based on aggregation of individual textual and visual review scores obtained by processing of said textual and said visual reviews by said crowdsourced experts who review the document; and display on a graphical user interface device, an output indicative of an aggregate score of the document reviewed by said crowdsourced experts along with information about who reviewed and how many times reviewed the document; an expert identity validation device to verify identities of the crowdsourced experts during or prior to review, wherein said expert identity validation device comprises: a device patterns assessment device to receive and process device information extracted by respective agent devices associated with said computing devices of said crowdsourced experts and verify the extracted device information with predefined device information for the respective crowdsourced experts; a network patterns assessment device to receive and process network information extracted by said respective agent devices associated with said computing devices of said crowdsourced experts and verify the extracted network information with predefined network information of the respective crowdsourced experts; a geo-spatial mapping device to perform geo-tagging of the crowdsourced experts and the documents reviewed by said crowdsourced experts and compare the geo-tags with pre-stored geo-spatial information about the experts for processing validation, wherein the geo-tagging is performed based on geo-spatial information received from a global positioning system (GPS)-based device; and a facial expression validation device to receive and process facial expressions received from respective facial expression sensors associated with said computing devices of said crowdsourced experts and verify identity in accordance with respective predefined facial patterns of said crowdsourced experts, wherein the facial expression validation device comprises a digital acquisition unit and multichannel amplifiers for pre-processing and amplification of signals transmitted by said facial expression sensors. 16. The system of claim 1 , wherein said visual scoring engine further comprising an image processor configured to perform image processing and pre-processing tasks. | 0.864532 |
8,635,201 | 1 | 4 | 1. A method, performed by at least one computer, comprising acts of: (A) receiving a query from a device and location data indicating a location of the device, the location data having a level of specificity; (B) in response to the query being received, identifying at least one first search engine to which to submit a representation of the query and information indicating the location of the device; (C) determining whether the level of specificity of the location data received in (A) is sufficient for the at least one first search engine; (D) when the level of specificity of the location data is sufficient, instructing the device to issue the representation of the query to the at least one first search engine; and (E) when the level of specificity of the location data is not sufficient, instructing the device to send, to the at least one computer, location data at a greater level of specificity, wherein the act (C) comprises determining that the level of specificity of the location data received in (A) is not sufficient for the at least one first search engine, and wherein the method further comprises acts of: (F) receiving location data at the greater level of specificity; and (G) instructing the device to submit a representation of the query and information specifying the location of the device at the greater level of specificity to the at least one first search engine. | 1. A method, performed by at least one computer, comprising acts of: (A) receiving a query from a device and location data indicating a location of the device, the location data having a level of specificity; (B) in response to the query being received, identifying at least one first search engine to which to submit a representation of the query and information indicating the location of the device; (C) determining whether the level of specificity of the location data received in (A) is sufficient for the at least one first search engine; (D) when the level of specificity of the location data is sufficient, instructing the device to issue the representation of the query to the at least one first search engine; and (E) when the level of specificity of the location data is not sufficient, instructing the device to send, to the at least one computer, location data at a greater level of specificity, wherein the act (C) comprises determining that the level of specificity of the location data received in (A) is not sufficient for the at least one first search engine, and wherein the method further comprises acts of: (F) receiving location data at the greater level of specificity; and (G) instructing the device to submit a representation of the query and information specifying the location of the device at the greater level of specificity to the at least one first search engine. 4. The method of claim 1 , wherein the act (C) is performed based at least in part on predefined specificity requirements of the at least one first search engine. | 0.748447 |
9,672,010 | 4 | 9 | 4. A universal modeling language (UML) analysis system, comprising: at least one processor; a memory coupled to the at least one processor, wherein the memory stores program instructions, wherein the program instructions are executable by the at least one processor to: import at least one tool-specific UML model from at least one UML tool, capture a snapshot of the at least one tool-specific UML model, wherein the snapshot comprises text data and at least one diagram, translate the at least one tool-specific UML model into at least one transformed UML model having a universal UML format; at least one transforming mechanism configured to translate the at least one tool-specific UML model into the universal UML format, wherein the transforming mechanism is configured to extract base data and one or more associated extended elements from the tool-specific UML model; and a monitor coupled to the at least one processor, wherein the at least one processor is configured to display a model navigation interface on the monitor, and display particular model elements based on commands input through the model navigation interface. | 4. A universal modeling language (UML) analysis system, comprising: at least one processor; a memory coupled to the at least one processor, wherein the memory stores program instructions, wherein the program instructions are executable by the at least one processor to: import at least one tool-specific UML model from at least one UML tool, capture a snapshot of the at least one tool-specific UML model, wherein the snapshot comprises text data and at least one diagram, translate the at least one tool-specific UML model into at least one transformed UML model having a universal UML format; at least one transforming mechanism configured to translate the at least one tool-specific UML model into the universal UML format, wherein the transforming mechanism is configured to extract base data and one or more associated extended elements from the tool-specific UML model; and a monitor coupled to the at least one processor, wherein the at least one processor is configured to display a model navigation interface on the monitor, and display particular model elements based on commands input through the model navigation interface. 9. The UML analysis system of claim 4 , wherein the program instructions are executable by the at least one processor to import a plurality of tool-specific UML models from a plurality of UML tools, wherein the plurality of tool-specific UML models are in different tool-specific formats, and wherein the program instructions are executable by the at least one processor to translate the plurality of tool-specific UML models into a plurality of transformed UML models, wherein each of the plurality of transformed UML models is in the universal UML format. | 0.500896 |
7,844,629 | 10 | 11 | 10. A system for querying a document containing hierarchical information that includes parent nodes and descendent nodes, comprising: a wrapper unit, implemented in a form of a processor, which locates a first parent node in the document by using a mapping specification, determines if the first parent node satisfies a query and fetches from the document nested descendent nodes relating to the first parent node in response to determining that the first parent node satisfies the query, wherein the wrapper unit parses only parent nodes in the document. | 10. A system for querying a document containing hierarchical information that includes parent nodes and descendent nodes, comprising: a wrapper unit, implemented in a form of a processor, which locates a first parent node in the document by using a mapping specification, determines if the first parent node satisfies a query and fetches from the document nested descendent nodes relating to the first parent node in response to determining that the first parent node satisfies the query, wherein the wrapper unit parses only parent nodes in the document. 11. The system of claim 10 , wherein the wrapper unit stores the first parent node in a first relational storage area and stores the fetched descendent nodes in a second relational storage area. | 0.838063 |
9,262,406 | 2 | 3 | 2. The computer-implemented method of claim 1 , wherein obtaining the model includes training, at the server, the model based on the learned mapping and the learned embeddings. | 2. The computer-implemented method of claim 1 , wherein obtaining the model includes training, at the server, the model based on the learned mapping and the learned embeddings. 3. The computer-implemented method of claim 2 , wherein the labeled training data includes (i) frames for verbs and (ii) possible semantic roles for each frame, and wherein modifier roles in the labeled training data are shared across different frames. | 0.890339 |
8,275,620 | 1 | 11 | 1. A method for providing assistive, context-relevant images, the method comprising: receiving text; receiving a spell check indication; performing a spelling check on the received text in response to the received spell check indication; providing, in response to the performed spelling check, a misspelling indication configured to indicate that at least one word in the received text is misspelled; receiving a selection of the misspelling indication; and displaying on a display device, in response to the received selection of the misspelling indication, a plurality of suggested spellings for the at least one word and an image corresponding to a first one of the plurality of suggested spellings for the at least one word. | 1. A method for providing assistive, context-relevant images, the method comprising: receiving text; receiving a spell check indication; performing a spelling check on the received text in response to the received spell check indication; providing, in response to the performed spelling check, a misspelling indication configured to indicate that at least one word in the received text is misspelled; receiving a selection of the misspelling indication; and displaying on a display device, in response to the received selection of the misspelling indication, a plurality of suggested spellings for the at least one word and an image corresponding to a first one of the plurality of suggested spellings for the at least one word. 11. The method of claim 1 , wherein receiving the spell check indication comprises receiving the spell check indication in response to a user selecting a button on a ribbon displayed on the display device. | 0.845865 |
8,185,372 | 1 | 2 | 1. An example-based translation apparatus comprising: a storage unit that stores source examples of a source language and target examples of a target language in a many-to-many relationship meaning that each of the source examples is associated with one or more of the target examples having the same or similar meaning, and each of the target examples is associated with one or more of the source examples having the same or similar meaning; an input receiving unit that receives an input of a sentence in the source language; a source example search unit that searches the storage unit to identify one or more of the source examples based on the sentence in the source language; a target example search unit that, for each of the first source examples, searches the storage unit to identify one or more of the target examples that have similar meanings as the identified first source example: a determining unit that determines whether there are a plurality of identified target examples; a first acquisition unit that, for each of the identified target examples, acquires from the storage unit one or more second source examples that correspond to the identified target example, when there are the plurality of identified target examples; a second acquisition unit that, for each of the second source examples, acquires from the storage unit one or more target examples that correspond to the second source example; a choice generating unit that chooses one of the second source examples that is associated with the fewest number of the acquired target examples; and an output control unit that outputs the chosen second source example. | 1. An example-based translation apparatus comprising: a storage unit that stores source examples of a source language and target examples of a target language in a many-to-many relationship meaning that each of the source examples is associated with one or more of the target examples having the same or similar meaning, and each of the target examples is associated with one or more of the source examples having the same or similar meaning; an input receiving unit that receives an input of a sentence in the source language; a source example search unit that searches the storage unit to identify one or more of the source examples based on the sentence in the source language; a target example search unit that, for each of the first source examples, searches the storage unit to identify one or more of the target examples that have similar meanings as the identified first source example: a determining unit that determines whether there are a plurality of identified target examples; a first acquisition unit that, for each of the identified target examples, acquires from the storage unit one or more second source examples that correspond to the identified target example, when there are the plurality of identified target examples; a second acquisition unit that, for each of the second source examples, acquires from the storage unit one or more target examples that correspond to the second source example; a choice generating unit that chooses one of the second source examples that is associated with the fewest number of the acquired target examples; and an output control unit that outputs the chosen second source example. 2. The example-based translation apparatus according to claim 1 , wherein the source example search unit searches the storage unit to identify one or more of the first source examples that are coincident with the sentence of the source language. | 0.926559 |
8,397,226 | 1 | 6 | 1. A system for programming a computer for pattern matching; the system comprising: a processor; and a memory coupled to the processor, the memory having stored thereon instructions that when executed by the processor cause the processor to: define a first pattern in an object-oriented programming language, the first pattern being a structured literal that is capable of matching a value contained in an incoming data stream, the structured literal comprising a representation of a list, tree, object, or graph; compare the first pattern to the value in the incoming data stream; determine that one or more variables are defined in the first pattern; infer the type of the one or more variables based on the position of the one or more variables in the first pattern; bound the one or more variables to the value; define a guard expression to be associated with the first pattern; and evaluate the guard expression, wherein if the first pattern matches the value, the guard expression evaluates to true; and if the first pattern does not match the value, the guard expression evaluates to false and a second pattern is compare to the value in the incoming data stream to determine whether the second pattern matches the value. | 1. A system for programming a computer for pattern matching; the system comprising: a processor; and a memory coupled to the processor, the memory having stored thereon instructions that when executed by the processor cause the processor to: define a first pattern in an object-oriented programming language, the first pattern being a structured literal that is capable of matching a value contained in an incoming data stream, the structured literal comprising a representation of a list, tree, object, or graph; compare the first pattern to the value in the incoming data stream; determine that one or more variables are defined in the first pattern; infer the type of the one or more variables based on the position of the one or more variables in the first pattern; bound the one or more variables to the value; define a guard expression to be associated with the first pattern; and evaluate the guard expression, wherein if the first pattern matches the value, the guard expression evaluates to true; and if the first pattern does not match the value, the guard expression evaluates to false and a second pattern is compare to the value in the incoming data stream to determine whether the second pattern matches the value. 6. The system of claim 1 , wherein the first pattern comprises at least one of a wildcard pattern, an irrefutable pattern, and a descendant pattern. | 0.772308 |
9,934,136 | 1 | 7 | 1. A method of generating test cases comprising: receiving, by a processor, a test application in an executable format, the test application including a plurality of forms; simulating the execution of the test application with the processor; iterating through each one of the plurality of forms of the test application; detecting a field in at least one form of the plurality of forms; inspecting a field included in the at least one form for metadata in the test application; generating, based on the metadata, at least one test case corresponding to the field; storing the test case in a first format; and storing the test case in a second format that is different from the first format. | 1. A method of generating test cases comprising: receiving, by a processor, a test application in an executable format, the test application including a plurality of forms; simulating the execution of the test application with the processor; iterating through each one of the plurality of forms of the test application; detecting a field in at least one form of the plurality of forms; inspecting a field included in the at least one form for metadata in the test application; generating, based on the metadata, at least one test case corresponding to the field; storing the test case in a first format; and storing the test case in a second format that is different from the first format. 7. The method of claim 1 , further comprising: receiving a navigation input from a user selection of a form from the plurality of forms; wherein the iterating through each one of the plurality of forms is based at least in part on the navigation input from the user. | 0.722917 |
9,633,002 | 7 | 8 | 7. The method of claim 6 , wherein performing the similarity comparison comprises computing a similarity based on feature vectors corresponding to the first mention and second mention, using the weights. | 7. The method of claim 6 , wherein performing the similarity comparison comprises computing a similarity based on feature vectors corresponding to the first mention and second mention, using the weights. 8. The method of claim 7 , wherein performing the similarity comparison comprises performing a cosine similarity function to determine an amount or degree of similarity between the respective feature vectors corresponding to the first mention and second mention. | 0.899231 |
8,700,781 | 1 | 8 | 1. A system comprising: a schema module to: provide a first message handling rule based on a first schema, the first schema being defined by a client device, the first message handling rule facilitating translation of a service request from the client device, the service request including incoming information, the incoming information including a plurality of tasks, the first schema including: a base information section for storing at least one of: information associated with a sender of the service request, information associated with a receiver of the service request, or information identifying the first schema, an account information section for storing at least one of: identifier information associated with the client device, or identifier information associated with a user of the client device, an asset information section for storing: data about hardware associated with the service request, or data about software associated with the service request, and an activity information section for storing information identifying activities associated with the service request, and provide a second message handling rule based on a second schema, the second schema being defined by the system, the second message handling rule facilitating making a backend message available to a plurality of backend devices; and a message handler including a processor to: receive the service request, obtain the first schema from the schema module, translate the service request using the first message handling rule, extract the incoming information, publish the incoming information using the second message handling rule, and send, to particular backend devices of the plurality of backend devices, only particular tasks, of the plurality of tasks, of the incoming information that are relevant to the particular backend devices. | 1. A system comprising: a schema module to: provide a first message handling rule based on a first schema, the first schema being defined by a client device, the first message handling rule facilitating translation of a service request from the client device, the service request including incoming information, the incoming information including a plurality of tasks, the first schema including: a base information section for storing at least one of: information associated with a sender of the service request, information associated with a receiver of the service request, or information identifying the first schema, an account information section for storing at least one of: identifier information associated with the client device, or identifier information associated with a user of the client device, an asset information section for storing: data about hardware associated with the service request, or data about software associated with the service request, and an activity information section for storing information identifying activities associated with the service request, and provide a second message handling rule based on a second schema, the second schema being defined by the system, the second message handling rule facilitating making a backend message available to a plurality of backend devices; and a message handler including a processor to: receive the service request, obtain the first schema from the schema module, translate the service request using the first message handling rule, extract the incoming information, publish the incoming information using the second message handling rule, and send, to particular backend devices of the plurality of backend devices, only particular tasks, of the plurality of tasks, of the incoming information that are relevant to the particular backend devices. 8. The system of claim 1 , where the plurality of backend devices are part of a backend system including an application server. | 0.822129 |
10,049,127 | 14 | 15 | 14. The method of claim 13 , wherein the construct defines at least one header word of the object. | 14. The method of claim 13 , wherein the construct defines at least one header word of the object. 15. The method of claim 14 , wherein the header word of the object implements at least one of: meta-locks, relaxed-locks, or thin-locks; and wherein the header word of the object comprises an indication of an access state and transaction information usable in coordinating concurrent transaction-based access to the object. | 0.924427 |
9,418,056 | 21 | 22 | 21. The authoring tool of claim 1 , further comprising a gallery tool for authoring of one or more gallery card(s) among the plurality of cards of the wrap package, each gallery card selectively authored to include a plurality of gallery items that are arranged to be sequentially presented in response to navigable inputs when the gallery card is rendered in the runtime instance of the wrap package. | 21. The authoring tool of claim 1 , further comprising a gallery tool for authoring of one or more gallery card(s) among the plurality of cards of the wrap package, each gallery card selectively authored to include a plurality of gallery items that are arranged to be sequentially presented in response to navigable inputs when the gallery card is rendered in the runtime instance of the wrap package. 22. The authoring tool of claim 21 , wherein the runtime instance of each of the gallery card(s) is characterized by: a different aspect ratio relative to the first aspect ratio of the set of cards, among the plurality of cards, of the wrap package; and the relative position of the content, including the plurality of the gallery items remains fixed, regardless of the size and/or type of display, associated with the consuming device. | 0.880351 |
9,069,818 | 7 | 8 | 7. The method of claim 1 , wherein automatically transforming the original search query into the transformed search query comprises: automatically determining, at the electronic computing system, whether all atomic query elements in the original search query have been processed; when it is determined that one or more of the atomic query elements in the original search query have not been processed, automatically selecting, at the electronic computing system, a selected atomic query element among the one or more of the atomic query elements in the original search query that have not been processed; after selecting the selected atomic query element, automatically determining, at the electronic computing system, whether the selected atomic query element is an atomic numerical comparison query element; when it is determined that the selected atomic query element is the numerical comparison query element, automatically determining, at the electronic computing system, whether the selected atomic query element is an equality numerical comparison query element; when it is determined that the selected atomic query element is the equality numerical comparison query element, automatically performing, at the electronic computing system, an equality transformation on the selected atomic query element; when it is determined that the selected atomic query element is the numerical comparison query element, automatically determining, at the electronic computing system, whether the selected atomic query element is greater than or equal to the numerical comparison query element; when it is determined that the selected atomic query element is greater than or equal to the numerical comparison query element, automatically performing, at the electronic computing system, a greater than or equals transformation on the selected atomic query element; when it is determined that the selected atomic query element is the numerical comparison query element, automatically determining, at the electronic computing system, whether the selected atomic query element is greater than the numerical comparison query element; when it is determined that the selected atomic query element is greater than the numerical comparison query element, automatically performing, at the electronic computing system, a greater than transformation on the selected atomic query element; when it is determined that the selected atomic query element is the numerical comparison query element, automatically determining, at the electronic computing system, whether the selected atomic query element is not equal to the numerical comparison query element; when it is determined that the selected atomic query element is not equal to the numerical comparison query element, automatically performing, at the electronic computing system, a not equals transformation on the selected atomic query element; when it is determined that the selected atomic query element is the numerical comparison query element, automatically determining, at the electronic computing system, whether the selected atomic query element is less than the numerical comparison query element; when it is determined that the selected atomic query element is less than the numerical comparison query element, automatically performing, at the electronic computing system, a less than transformation on the selected atomic query element; when it is determined that the selected atomic query element is the numerical comparison query element, automatically determining, at the electronic computing system, whether the selected atomic query element is less than or equal to the numerical comparison query element; and when it is determined that the selected atomic query element is less than or equal to the numerical comparison query element, automatically performing, at the electronic computing system, a less than or equal transformation on the selected atomic query element. | 7. The method of claim 1 , wherein automatically transforming the original search query into the transformed search query comprises: automatically determining, at the electronic computing system, whether all atomic query elements in the original search query have been processed; when it is determined that one or more of the atomic query elements in the original search query have not been processed, automatically selecting, at the electronic computing system, a selected atomic query element among the one or more of the atomic query elements in the original search query that have not been processed; after selecting the selected atomic query element, automatically determining, at the electronic computing system, whether the selected atomic query element is an atomic numerical comparison query element; when it is determined that the selected atomic query element is the numerical comparison query element, automatically determining, at the electronic computing system, whether the selected atomic query element is an equality numerical comparison query element; when it is determined that the selected atomic query element is the equality numerical comparison query element, automatically performing, at the electronic computing system, an equality transformation on the selected atomic query element; when it is determined that the selected atomic query element is the numerical comparison query element, automatically determining, at the electronic computing system, whether the selected atomic query element is greater than or equal to the numerical comparison query element; when it is determined that the selected atomic query element is greater than or equal to the numerical comparison query element, automatically performing, at the electronic computing system, a greater than or equals transformation on the selected atomic query element; when it is determined that the selected atomic query element is the numerical comparison query element, automatically determining, at the electronic computing system, whether the selected atomic query element is greater than the numerical comparison query element; when it is determined that the selected atomic query element is greater than the numerical comparison query element, automatically performing, at the electronic computing system, a greater than transformation on the selected atomic query element; when it is determined that the selected atomic query element is the numerical comparison query element, automatically determining, at the electronic computing system, whether the selected atomic query element is not equal to the numerical comparison query element; when it is determined that the selected atomic query element is not equal to the numerical comparison query element, automatically performing, at the electronic computing system, a not equals transformation on the selected atomic query element; when it is determined that the selected atomic query element is the numerical comparison query element, automatically determining, at the electronic computing system, whether the selected atomic query element is less than the numerical comparison query element; when it is determined that the selected atomic query element is less than the numerical comparison query element, automatically performing, at the electronic computing system, a less than transformation on the selected atomic query element; when it is determined that the selected atomic query element is the numerical comparison query element, automatically determining, at the electronic computing system, whether the selected atomic query element is less than or equal to the numerical comparison query element; and when it is determined that the selected atomic query element is less than or equal to the numerical comparison query element, automatically performing, at the electronic computing system, a less than or equal transformation on the selected atomic query element. 8. The method of claim 7 , wherein automatically performing the equality transformation comprises: receiving, at the electronic computing system, an invocation of the equality transformation that specifies a set of unprocessed blocks of a property specified by the selected atomic query element and a comparison value specified by the selected atomic query element; automatically determining, at the electronic computing system, whether the set of unprocessed blocks includes at least one block; when it is determined that the set of unprocessed blocks includes the at least one block, selecting, at the electronic computing system, a selected block from among the set of unprocessed blocks; automatically generating, at the electronic computing system, an “and” operation query element; automatically generating, at the electronic computing system, an comparison string that comprises a base comparison string that has a length proportional to a number represented by the selected block; automatically generating, at the electronic computing system, an exact match text comparison query element that specifies a meta-property associated with the selected block and that specifies the comparison string; automatically adding, at the electronic computing system, the exact match text comparison query element as a first operand of the “and” operation query element; automatically removing, at the electronic computing system, the selected block from the set of unprocessed blocks; after removing the selected block from the set of unprocessed blocks, automatically performing, at the electronic computing system, the equality transformation on the set of unprocessed blocks; after performing the equality transformation on the set of unprocessed blocks, adding, at the electronic computing system, a textual comparison query element generated by performing the equality transformation on the set of unprocessed blocks as a second operand of the “and” operation query element; after adding the textual comparison query element, returning, at the electronic computing system, the “and” operation query element; and when it is determined that the set of unprocessed blocks does not include the at least one block, returning, at the electronic computing system, a query element that denotes all of the data objects. | 0.641527 |
9,710,502 | 2 | 3 | 2. A system for managing document version access control which allows for at least two non-overlapping sets of users to each maintain privacy as to specific document versions within a single shared document chain, the system comprising: a hardware processor; and computer-readable storage medium containing instructions executable by the hardware processor to cause the system to: establish a document chain based on information received from a user of the system associated with a first non-overlapping set of users, and where the document chain consists of a collection of document versions within the system, where each document version includes some content that is same as content in at least one other document version in the document chain and where the document chain allows for document versions that include some content that is different from content in other document versions in the document chain; store and update information about the document chain; allow different users of the system to access different document versions within the document chain, where access to each of the document versions in the document chain is controlled by version specific access control settings; store one or more document versions received from users associated with the first non-overlapping set of users and at least a second non-overlapping set of users that has access to a document version associated with the document chain; and set separate access control settings for each document version, where the system allows for a specified document version to be associated with the document chain and also to remain private and exclusive to the second non-overlapping set of users, such that the specified document version is private from all users that are not associated with the second non-overlapping set of users, including private from all users associated with the first non-overlapping set of users, and further where the system allows for a different specified document version to be associated with the document chain and to remain private and exclusive to a user that did not first establish the document chain but that has access to the different specified document version associated with the document chain, such that the different specified document version is private from all other users including a user that first established the document chain in the system. | 2. A system for managing document version access control which allows for at least two non-overlapping sets of users to each maintain privacy as to specific document versions within a single shared document chain, the system comprising: a hardware processor; and computer-readable storage medium containing instructions executable by the hardware processor to cause the system to: establish a document chain based on information received from a user of the system associated with a first non-overlapping set of users, and where the document chain consists of a collection of document versions within the system, where each document version includes some content that is same as content in at least one other document version in the document chain and where the document chain allows for document versions that include some content that is different from content in other document versions in the document chain; store and update information about the document chain; allow different users of the system to access different document versions within the document chain, where access to each of the document versions in the document chain is controlled by version specific access control settings; store one or more document versions received from users associated with the first non-overlapping set of users and at least a second non-overlapping set of users that has access to a document version associated with the document chain; and set separate access control settings for each document version, where the system allows for a specified document version to be associated with the document chain and also to remain private and exclusive to the second non-overlapping set of users, such that the specified document version is private from all users that are not associated with the second non-overlapping set of users, including private from all users associated with the first non-overlapping set of users, and further where the system allows for a different specified document version to be associated with the document chain and to remain private and exclusive to a user that did not first establish the document chain but that has access to the different specified document version associated with the document chain, such that the different specified document version is private from all other users including a user that first established the document chain in the system. 3. The system of claim 2 further comprising storage for storing the document versions of the document chain. | 0.826923 |
9,588,741 | 1 | 7 | 1. A method comprising: generating a build of an application in a C Object-Oriented Programming Language; generating a unity file including a plurality of source files comprising references to a plurality of header files, at least two of the source files comprise references to a same header file, wherein generating the unity file further comprises excluding the source files from being separately compiled one at a time; compiling, via a processor, the unity file comprising the plurality of source files to obtain a single object file; linking the single object file to generate an executable of the application; and generating another build of the application based in part on determining that one or more new source files are added to the unity file in response to receipt of an indication of a selection to generate a full unity build. | 1. A method comprising: generating a build of an application in a C Object-Oriented Programming Language; generating a unity file including a plurality of source files comprising references to a plurality of header files, at least two of the source files comprise references to a same header file, wherein generating the unity file further comprises excluding the source files from being separately compiled one at a time; compiling, via a processor, the unity file comprising the plurality of source files to obtain a single object file; linking the single object file to generate an executable of the application; and generating another build of the application based in part on determining that one or more new source files are added to the unity file in response to receipt of an indication of a selection to generate a full unity build. 7. The method of claim 1 , wherein the object file comprises references to each of the plurality of source files that comprise references to the header files. | 0.883994 |
7,890,471 | 6 | 10 | 6. A machine-readable medium having instructions stored thereon for execution by a processor to perform a method of generating a virtual suffix tree (ViST) structure for searching XML documents, comprising the steps of: receiving one or more XML documents; converting the one or more XML documents into respective structure-encoded sequences; generating the ViST structure comprising: generating a D-Ancestor index of node pairs in the respective structure-encoded sequences; generating an S-Ancestor index of labels in one or more suffix trees corresponding to respective ones of the structure-encoded sequences; and generating a doc-ID index encoding the D-Ancestor index and the S-Ancestor index for each node of the structure-encoded sequences, wherein the encoding of the doc-ID index contains an answer to a query matching a non-contiguous subsequence in the doc-ID index; updating the ViST structure, the updating comprising: receiving a new XML document; transforming the new XML document into a respective structure-encoded sequence; inserting each element of the sequence into the D-Ancestor index to update relationships among nodes of the D-Ancestor index wherein the insertion of at least one of the elements results in the creation of a new node; assigning a new label to the new node based on an estimated number of different elements following the element corresponding to the new node and an occurrence probability of each of the elements following the element corresponding to the new node; and inserting the new label into the S-Ancestor index. | 6. A machine-readable medium having instructions stored thereon for execution by a processor to perform a method of generating a virtual suffix tree (ViST) structure for searching XML documents, comprising the steps of: receiving one or more XML documents; converting the one or more XML documents into respective structure-encoded sequences; generating the ViST structure comprising: generating a D-Ancestor index of node pairs in the respective structure-encoded sequences; generating an S-Ancestor index of labels in one or more suffix trees corresponding to respective ones of the structure-encoded sequences; and generating a doc-ID index encoding the D-Ancestor index and the S-Ancestor index for each node of the structure-encoded sequences, wherein the encoding of the doc-ID index contains an answer to a query matching a non-contiguous subsequence in the doc-ID index; updating the ViST structure, the updating comprising: receiving a new XML document; transforming the new XML document into a respective structure-encoded sequence; inserting each element of the sequence into the D-Ancestor index to update relationships among nodes of the D-Ancestor index wherein the insertion of at least one of the elements results in the creation of a new node; assigning a new label to the new node based on an estimated number of different elements following the element corresponding to the new node and an occurrence probability of each of the elements following the element corresponding to the new node; and inserting the new label into the S-Ancestor index. 10. The machine-readable medium of claim 6 , wherein generating the doc-ID index comprises generating a doc-ID B+Tree, wherein the doc-ID B+Tree indexes one or more (key,data) pairs and wherein the key element is a node ID, and the data element is a list of XML document IDs. | 0.618056 |
8,332,231 | 18 | 23 | 18. A system for processing an interaction with a person, comprising a processor, two or more analyst user interface devices in communication with the processor, and a memory in communication with the processor, the memory storing programming instructions executable by the processor to: receive data representing a multi-utterance transaction with the person, the data having multiple elements, a subset of the elements including sensitive customer data; portion the multi-utterance transaction into discrete, logical utterance units; automatically present the utterance units in perceptible form through the analyst interface devices to a number of intent analysts, the utterance units being distributed so that no intent analyst is ever exposed to more than one of said subset of the elements including sensitive customer data; accept intent input from each intent analyst through the respective analyst user interface device, where the intent input characterizes the intent analyst's interpretation of the person's intent expressed in the utterance, and where the intent input is prevented from providing information to be communicated to the person; and automatically communicate a message to the person, in perceptible form and in substantially real time relative to the receiving function, the message being automatically selected from among a predetermined set of possible messages as a function of the intent input accepted from the two or more intent analysts. | 18. A system for processing an interaction with a person, comprising a processor, two or more analyst user interface devices in communication with the processor, and a memory in communication with the processor, the memory storing programming instructions executable by the processor to: receive data representing a multi-utterance transaction with the person, the data having multiple elements, a subset of the elements including sensitive customer data; portion the multi-utterance transaction into discrete, logical utterance units; automatically present the utterance units in perceptible form through the analyst interface devices to a number of intent analysts, the utterance units being distributed so that no intent analyst is ever exposed to more than one of said subset of the elements including sensitive customer data; accept intent input from each intent analyst through the respective analyst user interface device, where the intent input characterizes the intent analyst's interpretation of the person's intent expressed in the utterance, and where the intent input is prevented from providing information to be communicated to the person; and automatically communicate a message to the person, in perceptible form and in substantially real time relative to the receiving function, the message being automatically selected from among a predetermined set of possible messages as a function of the intent input accepted from the two or more intent analysts. 23. The system of claim 18 , wherein the programming instructions are further executable by the processor to automatically change the number of intent analysts to whom one of the utterance units is presented depending on a load factor, the utterance units still being distributed so that no intent analyst is ever exposed to more than one of said subset of the elements including sensitive customer data. | 0.501235 |
9,659,045 | 4 | 5 | 4. The method of claim 3 , wherein: said first index comprises first index entries; and wherein for each particular token of said key word tokens and said at least a portion of said tag name tokens, an index entry of said first index entries maps said each particular token to one or more token locations within one or more respective hierarchical data objects. | 4. The method of claim 3 , wherein: said first index comprises first index entries; and wherein for each particular token of said key word tokens and said at least a portion of said tag name tokens, an index entry of said first index entries maps said each particular token to one or more token locations within one or more respective hierarchical data objects. 5. The method of claim 4 , wherein for each particular token of said key word tokens and said at least a portion of said tag name tokens, an index entry of said first index entries contains a posting listing comprising object references, each object reference of said object references identifying a certain hierarchical data object of said one or more respective hierarchical data objects and specifying certain one or more token locations within said certain hierarchical data object. | 0.867935 |
8,620,836 | 13 | 18 | 13. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions which, when executed by at least one processor, cause the at least one processor to: receive a document; determine a plurality of topics associated with the document; each of the plurality of topics being associated with text, determine one or more desired topics of the plurality of topics; filter a first portion of text from the document without filtering a second portion of text from the document, the second portion of text being associated with the one or more desired topics, the first portion of text not being associated with the one or more desired topics, the first portion of text being removed from the document, and the second portion of text being different than the first portion of text; split the second portion of text into a plurality of segments; cluster each of the plurality of segments into one or more clusters of a plurality of clusters, each cluster, of the plurality of clusters, including at least one of the plurality of segments, and each cluster, of the plurality of clusters, being associated with the one or more desired topics; identify at least one segment, of the plurality of segments, having low relevance to a cluster, of the plurality of clusters, that includes the at least one segment; and remove the at least one segment from the cluster. | 13. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions which, when executed by at least one processor, cause the at least one processor to: receive a document; determine a plurality of topics associated with the document; each of the plurality of topics being associated with text, determine one or more desired topics of the plurality of topics; filter a first portion of text from the document without filtering a second portion of text from the document, the second portion of text being associated with the one or more desired topics, the first portion of text not being associated with the one or more desired topics, the first portion of text being removed from the document, and the second portion of text being different than the first portion of text; split the second portion of text into a plurality of segments; cluster each of the plurality of segments into one or more clusters of a plurality of clusters, each cluster, of the plurality of clusters, including at least one of the plurality of segments, and each cluster, of the plurality of clusters, being associated with the one or more desired topics; identify at least one segment, of the plurality of segments, having low relevance to a cluster, of the plurality of clusters, that includes the at least one segment; and remove the at least one segment from the cluster. 18. The computer-readable medium of claim 13 , where the instructions further comprise: one or more instructions to identify one or more titles in the text; one or more instructions to identify at least one segment of the plurality of segments that does not include at least one word from the identified one or more titles; and one or more instructions to remove the identified at least one segment from the text. | 0.501208 |
7,681,147 | 1 | 4 | 1. A computer-implemented method for determining probable meanings of inputted search query terms, the method comprising: receiving an input of at least one search query word by a networked server; determining a probable meaning of the at least one word by a processor of the networked server in accordance with a prior probability and a context frequency probability of probable meanings of the word, wherein the context frequency probability consists of the meaning of the word in a context of a plurality of terms immediately preceding or immediately following the word, wherein the prior probability comprises a probability that the word refers to a predetermined meaning irregardless of a query context in which the word is used, which prior probability is derived from previous analysis of documents containing the word, wherein determining the probable meaning comprises: estimating an expected final probability for the at least one word given the prior probability of the at least one word; deriving an inverse combine function that uses the prior probability and the expected final probability to determine the context frequency probability of the at least one word; using a combine mathematical function to combine the prior probability and the context frequency probability to produce a final probability of the probable meaning of the at least one search query word; and using the final probability of the probable meaning of the at least one word by the networked server to influence search results returned in response to the inputted at least one word. | 1. A computer-implemented method for determining probable meanings of inputted search query terms, the method comprising: receiving an input of at least one search query word by a networked server; determining a probable meaning of the at least one word by a processor of the networked server in accordance with a prior probability and a context frequency probability of probable meanings of the word, wherein the context frequency probability consists of the meaning of the word in a context of a plurality of terms immediately preceding or immediately following the word, wherein the prior probability comprises a probability that the word refers to a predetermined meaning irregardless of a query context in which the word is used, which prior probability is derived from previous analysis of documents containing the word, wherein determining the probable meaning comprises: estimating an expected final probability for the at least one word given the prior probability of the at least one word; deriving an inverse combine function that uses the prior probability and the expected final probability to determine the context frequency probability of the at least one word; using a combine mathematical function to combine the prior probability and the context frequency probability to produce a final probability of the probable meaning of the at least one search query word; and using the final probability of the probable meaning of the at least one word by the networked server to influence search results returned in response to the inputted at least one word. 4. The method of claim 1 , further comprising displaying an advertisement in accordance with the probable meanings of at least one inputted word. | 0.852041 |
9,244,985 | 19 | 25 | 19. One or more non-transitory machine-readable media storing instructions that are executable to perform operations comprising: receiving a search query; identifying content responsive to the search query; identifying one or more members of a social networking service that have an association with identified content; and outputting, to a computing device, search results based on the content and data corresponding to the one or more members, the data corresponding to the one or more members comprising data for generating individual display areas for each of the one or more members for display on a display screen of the computing device and on a same page as, but separate from, the search results, each of the display areas for enabling access to information about a corresponding member of the social networking service who is represented by the corresponding display area and about content associated with the member of the social networking service, wherein the data corresponding to the one or more members is determined based on a number of interactions of the one or more members with content relating to a topic of the identified content. | 19. One or more non-transitory machine-readable media storing instructions that are executable to perform operations comprising: receiving a search query; identifying content responsive to the search query; identifying one or more members of a social networking service that have an association with identified content; and outputting, to a computing device, search results based on the content and data corresponding to the one or more members, the data corresponding to the one or more members comprising data for generating individual display areas for each of the one or more members for display on a display screen of the computing device and on a same page as, but separate from, the search results, each of the display areas for enabling access to information about a corresponding member of the social networking service who is represented by the corresponding display area and about content associated with the member of the social networking service, wherein the data corresponding to the one or more members is determined based on a number of interactions of the one or more members with content relating to a topic of the identified content. 25. The one or more non-transitory machine-readable media of claim 19 , wherein determining the data corresponding to the one or more members based on the number of interactions comprises determining if the number of interactions exceeds a threshold. | 0.825905 |
9,229,692 | 8 | 12 | 8. A system for processing proposed program code libraries in a networked computing environment, comprising: a memory medium comprising instructions; a bus coupled to the memory medium; and a processor coupled to the bus that when executing the instructions causes the system to: receive a set of annotations associated with a set of program code files in an integrated development environment (IDE), wherein the set of annotations include one or more conditions for replacing a program code library; receive a proposed program code library in the IDE; determine whether the proposed program code library is an excluded program code library based on the set of annotations; compute whether the proposed program code library meets one or more thresholds for replacing an existing program code library, the computing being based on at least one of: an attribute comparison and a micro-benchmarking analysis; and provide, responsive to the proposed program code library meeting the one or more thresholds, the proposed program code library to a computer device hosting the IDE. | 8. A system for processing proposed program code libraries in a networked computing environment, comprising: a memory medium comprising instructions; a bus coupled to the memory medium; and a processor coupled to the bus that when executing the instructions causes the system to: receive a set of annotations associated with a set of program code files in an integrated development environment (IDE), wherein the set of annotations include one or more conditions for replacing a program code library; receive a proposed program code library in the IDE; determine whether the proposed program code library is an excluded program code library based on the set of annotations; compute whether the proposed program code library meets one or more thresholds for replacing an existing program code library, the computing being based on at least one of: an attribute comparison and a micro-benchmarking analysis; and provide, responsive to the proposed program code library meeting the one or more thresholds, the proposed program code library to a computer device hosting the IDE. 12. The system of claim 8 , the micro-benchmarking analysis comprising a comparison of a set of micro-benchmarking times and a set of innovation stamps associated with the proposed program code library and the existing program code library. | 0.631902 |
6,151,576 | 13 | 19 | 13. A computer program, residing on a computer-readable medium, comprising instructions for causing a computer to: convert a stream of digitized speech samples to a stream of text and associated reliability measures, the reliability measures indicating a level of confidence in the correctness of the speech to text conversion of the associated portions of the stream of text; and create a mixed-media data stream comprising the stream of text as a text component and selected portions of the digitized stream of speech as a speech component, each selected portion corresponding to a portion of the stream of text having a reliability measure below a threshold. | 13. A computer program, residing on a computer-readable medium, comprising instructions for causing a computer to: convert a stream of digitized speech samples to a stream of text and associated reliability measures, the reliability measures indicating a level of confidence in the correctness of the speech to text conversion of the associated portions of the stream of text; and create a mixed-media data stream comprising the stream of text as a text component and selected portions of the digitized stream of speech as a speech component, each selected portion corresponding to a portion of the stream of text having a reliability measure below a threshold. 19. The method of claim 13, further comprising instructions to: find in the text component a segment of text matching a text search request; and speaking the segment of text. | 0.845745 |
7,644,377 | 2 | 3 | 2. The method of claim 1 , wherein providing the models further comprises specifying an association between at least two of the models. | 2. The method of claim 1 , wherein providing the models further comprises specifying an association between at least two of the models. 3. The method of claim 2 , wherein specifying the association between the at least two models comprises specifying one of a many-to-many association, many-to-one association, and one-to-many association. | 0.907643 |
9,646,605 | 1 | 6 | 1. A computerized method for reducing false alarms in a speech recognition system, the method comprising: receiving a plurality of training examples; generating a model of a left internal context based at least in part on the plurality of training examples, wherein the generation of the model includes compact representation of the left internal context in the form of spectral, cepstral or sinusoidal descriptions; generating a model of a right internal context based at least in part on the plurality of training examples, wherein the generation of the model includes compact representation of the right internal context in the form of spectral, cepstral or sinusoidal descriptions; generating a model of a left external context based at least in part on the plurality of training examples, wherein the generation of the model includes compact representation of the left external context in the form of spectral, cepstral or sinusoidal descriptions; generating a model of a right external context based at least in part on the plurality of training examples, wherein the generation of the model includes compact representation of the right external context in the form of spectral, cepstral or sinusoidal descriptions; receiving at least one test word, the at least one test word comprising an external context; comparing the external context of the at least one test word against a threshold associated with each of the model of the left internal context, the model of the right internal context, the model of the left external context, and the model of the right external context; and rejecting the at least one test word if it is not within the thresholds. | 1. A computerized method for reducing false alarms in a speech recognition system, the method comprising: receiving a plurality of training examples; generating a model of a left internal context based at least in part on the plurality of training examples, wherein the generation of the model includes compact representation of the left internal context in the form of spectral, cepstral or sinusoidal descriptions; generating a model of a right internal context based at least in part on the plurality of training examples, wherein the generation of the model includes compact representation of the right internal context in the form of spectral, cepstral or sinusoidal descriptions; generating a model of a left external context based at least in part on the plurality of training examples, wherein the generation of the model includes compact representation of the left external context in the form of spectral, cepstral or sinusoidal descriptions; generating a model of a right external context based at least in part on the plurality of training examples, wherein the generation of the model includes compact representation of the right external context in the form of spectral, cepstral or sinusoidal descriptions; receiving at least one test word, the at least one test word comprising an external context; comparing the external context of the at least one test word against a threshold associated with each of the model of the left internal context, the model of the right internal context, the model of the left external context, and the model of the right external context; and rejecting the at least one test word if it is not within the thresholds. 6. The method of claim 1 , wherein the comparing step comprises the additional step of evaluating the at least one word with a perplexity test. | 0.813316 |
9,905,222 | 1 | 5 | 1. A method comprising: mapping, via one or more processors, call-types between a first spoken dialog system and a second spoken dialog system using a set of labeled data, to yield mapped call-types; training, via the one or more processors, a model using information based on the mapped call-types; and routing incoming calls based on the model. | 1. A method comprising: mapping, via one or more processors, call-types between a first spoken dialog system and a second spoken dialog system using a set of labeled data, to yield mapped call-types; training, via the one or more processors, a model using information based on the mapped call-types; and routing incoming calls based on the model. 5. The method of claim 1 , further comprising labeling, as a new call-type, a call-type of the first spoken dialog system when the call-type has more than a specified ratio among the call-types. | 0.7575 |
7,921,105 | 2 | 4 | 2. The bio-item searching apparatus according to claim 1 , wherein the storage device further includes: a bio-item relation database that stores two of the bio-items arbitrarily and a co-occurrence correlation score between the two bio-items in association with each other, the processor further includes: a related bio-item extracting unit that extracts a bio-item having correlation with the candidate bio-item as a related bio-item based on the co-occurrence correlation score stored in the bio-item relation database; and an integrated correlation score calculating unit that calculates an integrated correlation score between the related bio-item and the keyword by integrating the correlation score of the candidate bio-item with the co-occurrence correlation score, and the output unit outputs the related bio-item to the output device based on the integrated correlation score calculated by the integrated correlation score calculating unit. | 2. The bio-item searching apparatus according to claim 1 , wherein the storage device further includes: a bio-item relation database that stores two of the bio-items arbitrarily and a co-occurrence correlation score between the two bio-items in association with each other, the processor further includes: a related bio-item extracting unit that extracts a bio-item having correlation with the candidate bio-item as a related bio-item based on the co-occurrence correlation score stored in the bio-item relation database; and an integrated correlation score calculating unit that calculates an integrated correlation score between the related bio-item and the keyword by integrating the correlation score of the candidate bio-item with the co-occurrence correlation score, and the output unit outputs the related bio-item to the output device based on the integrated correlation score calculated by the integrated correlation score calculating unit. 4. The bio-item searching apparatus according to claim 2 , wherein the integrated correlation score calculating unit integrates the integrated correlation score based on the following numerical expression 1, 1-1 or 1-2,
P= 1−(1 −P 1)(1 −P 2) (Numerical Expression 1),
P=P 1 +P 2 (Numerical Expression 1-1),
Log( P )=Max{Log( P 1),Log( P 2)} (Numerical Expression 1-2), where P is the integrated correlation score, P1 is the correlation score of the candidate bio-item, P2 is the co-occurrence correlation score, and Max {A,B} is a function of selecting a not smaller one of A and B. | 0.856274 |
8,527,489 | 1 | 4 | 1. A method implemented by data processing apparatus, the method comprising: determining, at a user device, that one or more first search queries received in a user interface during a search session are provided to a first search engine; determining, at the user device, that for each of the one or more first search queries, a plurality of respective search results determined by the first search engine as satisfying the respective first search queries are displayed in the user interface; determining, at the user device, that the respective search results determined by the first search engine as satisfying the respective first search queries do not satisfy an informational need of a user that input the one or more first search queries at the user device; and in response to determining that the respective search results determined by the first search engine do not satisfy an informational need: displaying, in the user interface, a second search query and a third search query that is based on the first search queries, wherein the third search query is different from the second search query; and displaying, in the user interface, a message informing the user of an ability to submit either the second search query or the third search query to a second search engine. | 1. A method implemented by data processing apparatus, the method comprising: determining, at a user device, that one or more first search queries received in a user interface during a search session are provided to a first search engine; determining, at the user device, that for each of the one or more first search queries, a plurality of respective search results determined by the first search engine as satisfying the respective first search queries are displayed in the user interface; determining, at the user device, that the respective search results determined by the first search engine as satisfying the respective first search queries do not satisfy an informational need of a user that input the one or more first search queries at the user device; and in response to determining that the respective search results determined by the first search engine do not satisfy an informational need: displaying, in the user interface, a second search query and a third search query that is based on the first search queries, wherein the third search query is different from the second search query; and displaying, in the user interface, a message informing the user of an ability to submit either the second search query or the third search query to a second search engine. 4. The method of claim 1 , wherein displaying a suggestion to provide the second search query to the second search engine comprises: displaying the second search query in the user interface; and displaying a message in the user interface that states that selection of the second search query will result in the second search query being provided to the second search engine. | 0.67193 |
7,783,614 | 1 | 2 | 1. A method of linking elements in a computer-generated document to corresponding data in a database, comprising: attaching a schema file associated with at least one intended use of the document to a document defining rules associated with a markup language to be applied to the document, wherein the markup language is XML and wherein the rules associated with the markup language to be applied to the document comprise names of elements of the markup language and data types associated with the names of the elements of the markup language; applying the elements of the markup language to the document; establishing data fields within the database for linking to corresponding markup language elements in the document; writing a unique document identifier number to the document for linking the data fields in the database to the document, wherein linking the data fields in the database to the document comprises: determining if a table associated with the document exists within a document library; if no table is associated with the document, creating a table containing user-defined elements associated with the document; selecting a table within a document library, the document library being maintained in the database where the table is associated with the document, and linking at least one markup language element in the document to corresponding data in the database; when data is entered into the database associated with the given markup language element in the document, automatically writing the data to the document in a location in the document associated with the given markup language element; when the given markup language element in the document is modified, automatically updating the corresponding data in the database; providing at least one suggested document element according to the schema file associated with the at least one intended use of the document, wherein the at least one suggested document element comprises an element structure linked to at least one corresponding data field in the database; and enforcing at least one element constraint according to the schema file, wherein the element constraint comprises at least one piece of required data for at least one document element. | 1. A method of linking elements in a computer-generated document to corresponding data in a database, comprising: attaching a schema file associated with at least one intended use of the document to a document defining rules associated with a markup language to be applied to the document, wherein the markup language is XML and wherein the rules associated with the markup language to be applied to the document comprise names of elements of the markup language and data types associated with the names of the elements of the markup language; applying the elements of the markup language to the document; establishing data fields within the database for linking to corresponding markup language elements in the document; writing a unique document identifier number to the document for linking the data fields in the database to the document, wherein linking the data fields in the database to the document comprises: determining if a table associated with the document exists within a document library; if no table is associated with the document, creating a table containing user-defined elements associated with the document; selecting a table within a document library, the document library being maintained in the database where the table is associated with the document, and linking at least one markup language element in the document to corresponding data in the database; when data is entered into the database associated with the given markup language element in the document, automatically writing the data to the document in a location in the document associated with the given markup language element; when the given markup language element in the document is modified, automatically updating the corresponding data in the database; providing at least one suggested document element according to the schema file associated with the at least one intended use of the document, wherein the at least one suggested document element comprises an element structure linked to at least one corresponding data field in the database; and enforcing at least one element constraint according to the schema file, wherein the element constraint comprises at least one piece of required data for at least one document element. 2. The method of claim 1 , further comprising: writing a database query to the database for assembling data from at least one data field within the database; and writing the results of the database query into the document in a location in the document associated with the database query. | 0.86068 |
8,024,334 | 1 | 5 | 1. A method, relating to creating and maintaining, in at least one database available to a population of users, on a database server computer, information about a plurality of database of subjects, comprising the steps of: a) associating with each database subject of such plurality of database subjects at least one plurality of natural-language tags potentially descriptive of such each database subject according to an involved subset of such population of database users; said at least one plurality of natural language tags comprising other user chosen and/or other user provided natural language terms potentially descriptive of a subject or subjects of said database; b) assessing at least one measure of descriptive relevance of each of such at least one plurality of natural-language tags to such each database subject according to each particular database user of such involved subset of such population of database users; c) associatively indexing, in such at least one database, such respective particular database users, such respective natural-language tags, such respective measures of relevance, and such respective database subjects; and d) accumulating and storing such respective measures of relevance. | 1. A method, relating to creating and maintaining, in at least one database available to a population of users, on a database server computer, information about a plurality of database of subjects, comprising the steps of: a) associating with each database subject of such plurality of database subjects at least one plurality of natural-language tags potentially descriptive of such each database subject according to an involved subset of such population of database users; said at least one plurality of natural language tags comprising other user chosen and/or other user provided natural language terms potentially descriptive of a subject or subjects of said database; b) assessing at least one measure of descriptive relevance of each of such at least one plurality of natural-language tags to such each database subject according to each particular database user of such involved subset of such population of database users; c) associatively indexing, in such at least one database, such respective particular database users, such respective natural-language tags, such respective measures of relevance, and such respective database subjects; and d) accumulating and storing such respective measures of relevance. 5. The method according to claim 1 in which such natural-language tags are essentially evaluative. | 0.93726 |
9,934,289 | 16 | 17 | 16. A non-transitory computer readable medium including instructions that when executed cause one or more processors to perform: identifying paths of a next valid character tree including a plurality of nodes including a root node and at least one leaf node for at least one document, wherein one or more paths from the root node to the at least one leaf node corresponds to at least one of (a) at least one first token for a first search term or (b) at least one second token for a second search term; computing a plurality of document error lists for one or more paths that reaches a leaf node in the next valid character tree, the plurality of document error lists comprising a first document error list and a second document error list, wherein (a) the first error list comprises one or more instances of first error list information, each instance of first error list information comprising a first document identifier and a first error distance, (b) the second error list comprises one or more instances of second error list information, each instance of second error list information comprising a second document identifier and a second error distance, (c) the first document identifier is associated with a document that comprises at least one instance of the at least one first token, and (d) the second document identifier is associated with a document that comprises at least one instance of the at least one second token; and comparing the plurality of document error lists to identify an instance of first error list information and an instance of second error list information, wherein (a) the first document identifier and the second document identifier are a common document identifier and (b) at least one of (i) the first error distance satisfies an error distance threshold and the second error distance satisfies the error distance threshold or (ii) the sum of the first error distance and the second error distance satisfies the error distance threshold. | 16. A non-transitory computer readable medium including instructions that when executed cause one or more processors to perform: identifying paths of a next valid character tree including a plurality of nodes including a root node and at least one leaf node for at least one document, wherein one or more paths from the root node to the at least one leaf node corresponds to at least one of (a) at least one first token for a first search term or (b) at least one second token for a second search term; computing a plurality of document error lists for one or more paths that reaches a leaf node in the next valid character tree, the plurality of document error lists comprising a first document error list and a second document error list, wherein (a) the first error list comprises one or more instances of first error list information, each instance of first error list information comprising a first document identifier and a first error distance, (b) the second error list comprises one or more instances of second error list information, each instance of second error list information comprising a second document identifier and a second error distance, (c) the first document identifier is associated with a document that comprises at least one instance of the at least one first token, and (d) the second document identifier is associated with a document that comprises at least one instance of the at least one second token; and comparing the plurality of document error lists to identify an instance of first error list information and an instance of second error list information, wherein (a) the first document identifier and the second document identifier are a common document identifier and (b) at least one of (i) the first error distance satisfies an error distance threshold and the second error distance satisfies the error distance threshold or (ii) the sum of the first error distance and the second error distance satisfies the error distance threshold. 17. The non-transitory computer readable medium of claim 16 , the instructions further configured to cause the processor to perform: adding the common document identifiers to a result set; and providing the result set of document identifiers. | 0.778388 |
8,676,800 | 1 | 35 | 1. A method of generating information from a plurality of data items, the method comprising the steps of: (a) populating an aggregate data item with at least one of the plurality of data items; wherein each individual data item comprises original information including an attribute and a value, wherein the attribute of the individual data item is an identifier for the individual data item, wherein the aggregate data item is a form of derived attribute, wherein the derived attribute represents a transformation of a collection of individual data items into a single data item with a value, wherein said value of the derived attribute is an aggregate value comprising a map of attribute to value for each said individual data item within said collection of individual data items such that a derived attribute forms a single data item suitable for inferencing by a knowledge base, said single data item retaining the original information relating to each of the plurality of individual data items yet queried by the knowledge base as a whole to extract information regarding said individual data items; and (b) generating the information using the aggregate data item, wherein the method of generating information is performed by a decision support system, and wherein the information so generated falls into one or more of the following groups: i. textual information; ii. a machine instruction. | 1. A method of generating information from a plurality of data items, the method comprising the steps of: (a) populating an aggregate data item with at least one of the plurality of data items; wherein each individual data item comprises original information including an attribute and a value, wherein the attribute of the individual data item is an identifier for the individual data item, wherein the aggregate data item is a form of derived attribute, wherein the derived attribute represents a transformation of a collection of individual data items into a single data item with a value, wherein said value of the derived attribute is an aggregate value comprising a map of attribute to value for each said individual data item within said collection of individual data items such that a derived attribute forms a single data item suitable for inferencing by a knowledge base, said single data item retaining the original information relating to each of the plurality of individual data items yet queried by the knowledge base as a whole to extract information regarding said individual data items; and (b) generating the information using the aggregate data item, wherein the method of generating information is performed by a decision support system, and wherein the information so generated falls into one or more of the following groups: i. textual information; ii. a machine instruction. 35. A method defined by claim 1 including using a computer program comprising instructions for controlling a computer to implement a method in accordance with the method defined by claim 1 . | 0.876463 |
9,349,299 | 1 | 2 | 1. A method comprising: availing a plurality of educational standards to a plurality of students, wherein said standards correspond to a plurality of lessons to be at least partially taught via a plurality of teachers to said students, wherein said availing comprises receiving, by an evaluation server, an educational standard and a lesson plan from a teacher client over a network, storing, by said evaluation server, said educational standard and said lesson plan in a database coupled to said evaluation server, receiving, by said evaluation server, an educational standard selection and a lesson plan selection from said teacher client over said network, wherein said educational standard selection selects said educational standard stored in said database, wherein said lesson plan selection selects said lesson plan stored in said database, assigning, by said evaluation server, said educational standard to said lesson plan in said database based on said educational standard selection and said lesson plan selection, receiving, by said evaluation server, an input from a student client over said network, wherein said input is indicative of at least one of a teacher, a class, or a location of a class, presenting, by said evaluation server, said educational standard and said lesson plan on said student client over said network based on said input and said assigning; receiving a plurality of ratings from said students, wherein said ratings rating said teachers based at least in part on said students perceptions of said teachers at least partially teaching said lessons according to said standards, wherein said receiving is based on presenting, by said evaluation server, a binary rating scale on said student client over said network based on said presenting said educational standard and said lesson plan on said student client, receiving, by said evaluation server, a rating from said student client over said network based on said binary rating scale, storing, by said evaluation server, said rating in said database such that said rating is associated with said educational standard and said lesson plan, wherein said rating is anonymous to said teacher client based on said evaluation server not revealing a student identity associated with said student client to said teacher client, wherein said rating is not anonymous to an administrator client based on said evaluation server revealing said student identity to said administrator client; extracting a meaning from said ratings, wherein the extracting comprises correlating, by said evaluation server, a test score with said rating associated with said educational standard and said lesson plan, wherein said test score is stored in said database, providing, by said evaluation server, a notice to said administrator client over said network based on said correlating. | 1. A method comprising: availing a plurality of educational standards to a plurality of students, wherein said standards correspond to a plurality of lessons to be at least partially taught via a plurality of teachers to said students, wherein said availing comprises receiving, by an evaluation server, an educational standard and a lesson plan from a teacher client over a network, storing, by said evaluation server, said educational standard and said lesson plan in a database coupled to said evaluation server, receiving, by said evaluation server, an educational standard selection and a lesson plan selection from said teacher client over said network, wherein said educational standard selection selects said educational standard stored in said database, wherein said lesson plan selection selects said lesson plan stored in said database, assigning, by said evaluation server, said educational standard to said lesson plan in said database based on said educational standard selection and said lesson plan selection, receiving, by said evaluation server, an input from a student client over said network, wherein said input is indicative of at least one of a teacher, a class, or a location of a class, presenting, by said evaluation server, said educational standard and said lesson plan on said student client over said network based on said input and said assigning; receiving a plurality of ratings from said students, wherein said ratings rating said teachers based at least in part on said students perceptions of said teachers at least partially teaching said lessons according to said standards, wherein said receiving is based on presenting, by said evaluation server, a binary rating scale on said student client over said network based on said presenting said educational standard and said lesson plan on said student client, receiving, by said evaluation server, a rating from said student client over said network based on said binary rating scale, storing, by said evaluation server, said rating in said database such that said rating is associated with said educational standard and said lesson plan, wherein said rating is anonymous to said teacher client based on said evaluation server not revealing a student identity associated with said student client to said teacher client, wherein said rating is not anonymous to an administrator client based on said evaluation server revealing said student identity to said administrator client; extracting a meaning from said ratings, wherein the extracting comprises correlating, by said evaluation server, a test score with said rating associated with said educational standard and said lesson plan, wherein said test score is stored in said database, providing, by said evaluation server, a notice to said administrator client over said network based on said correlating. 2. The method of claim 1 , further comprising: revealing, by said evaluation server, said student identity to said teacher client based on said rating being negative. | 0.75 |
10,129,367 | 13 | 19 | 13. A method comprising: receiving user information about a target user of an online system, the user information describing interactions performed by the target user on the online system; receiving third party information from a third party outside of the online system, the third party information describing an item associated with the third party that the target user is interested in acquiring; training a machine learning model using feature vectors identified based on actions performed by a plurality of users of the online system and based on the information about the plurality of users, the actions describing items that at least one of the plurality of users previously acquired, the trained machine learning model configured to determine a likelihood that a particular user will acquire a particular item; receiving item information indicating a change in a status of the item associated with the third party; providing as inputs to the trained machine learning model, the user information, the third party information, and the item information; determining, by the trained machine learning model based on the inputs, a likelihood that the target user will acquire the item based on the change in status of the item; sending a content item associated with the item for display on a client device of the target user in response to the likelihood that the target user will acquire the item exceeding a threshold value. | 13. A method comprising: receiving user information about a target user of an online system, the user information describing interactions performed by the target user on the online system; receiving third party information from a third party outside of the online system, the third party information describing an item associated with the third party that the target user is interested in acquiring; training a machine learning model using feature vectors identified based on actions performed by a plurality of users of the online system and based on the information about the plurality of users, the actions describing items that at least one of the plurality of users previously acquired, the trained machine learning model configured to determine a likelihood that a particular user will acquire a particular item; receiving item information indicating a change in a status of the item associated with the third party; providing as inputs to the trained machine learning model, the user information, the third party information, and the item information; determining, by the trained machine learning model based on the inputs, a likelihood that the target user will acquire the item based on the change in status of the item; sending a content item associated with the item for display on a client device of the target user in response to the likelihood that the target user will acquire the item exceeding a threshold value. 19. The method of claim 13 , wherein the feature vectors are each associated with a weight, a first vector of the feature vectors identified based on actions performed by users connected to the target user on the online system, a second vector of the feature vectors identified based on actions performed by users not connected to the target user on the online system, the first vector weighted higher than the second vector. | 0.647595 |
8,762,130 | 1 | 4 | 1. A system comprising at least one computer configured to form: a linguistic knowledgebase (LKB) for a natural language, the LKB comprising a set of computer-readable lexicon declarations, a set of computer-readable inflected form declarations, and a set of computer-readable syntax rule declarations; a computer-implemented word retriever connected to the LKB and configured to: receive a first word, perform a lookup of an inflected form declaration of the first word in the LKB, in response to performing the lookup of the inflected form declaration, perform a lookup of a lexicon declaration of the first word in the LKB, determine a first word interpretation of the first word according to the lexicon declaration and the inflected form declaration, the first word interpretation comprising a lemma of the first word and an inflection indicator of the first word; a computer-implemented form generator connected to the word retriever and configured to: receive a second word not necessarily distinct from the first word, produce a first set of words, each word of the first set of words having a predetermined spelling similarity to the second word, and for each word of the first set of words, receive from the word retriever a second word interpretation of said each word of the first set of words; a computer-implemented synthetic annotator connected to the word retriever and configured to: receive a word sequence, for each word of the word sequence, receive from the word retriever a third word interpretation of said each word of the word sequence, and determine a synthetic annotation of the word sequence, the synthetic annotation comprising the third word interpretation of said each word of the word sequence; and a computer-implemented syntax checker connected to the synthetic annotator and configured to: receive the synthetic annotation from the synthetic annotator, perform a lookup of a syntax rule declaration of the word sequence in the LKB according to the synthetic annotation, and perform a syntactic analysis of the word sequence according to the syntax rule declaration, to determine a synthetic dependency tree of the word sequence. | 1. A system comprising at least one computer configured to form: a linguistic knowledgebase (LKB) for a natural language, the LKB comprising a set of computer-readable lexicon declarations, a set of computer-readable inflected form declarations, and a set of computer-readable syntax rule declarations; a computer-implemented word retriever connected to the LKB and configured to: receive a first word, perform a lookup of an inflected form declaration of the first word in the LKB, in response to performing the lookup of the inflected form declaration, perform a lookup of a lexicon declaration of the first word in the LKB, determine a first word interpretation of the first word according to the lexicon declaration and the inflected form declaration, the first word interpretation comprising a lemma of the first word and an inflection indicator of the first word; a computer-implemented form generator connected to the word retriever and configured to: receive a second word not necessarily distinct from the first word, produce a first set of words, each word of the first set of words having a predetermined spelling similarity to the second word, and for each word of the first set of words, receive from the word retriever a second word interpretation of said each word of the first set of words; a computer-implemented synthetic annotator connected to the word retriever and configured to: receive a word sequence, for each word of the word sequence, receive from the word retriever a third word interpretation of said each word of the word sequence, and determine a synthetic annotation of the word sequence, the synthetic annotation comprising the third word interpretation of said each word of the word sequence; and a computer-implemented syntax checker connected to the synthetic annotator and configured to: receive the synthetic annotation from the synthetic annotator, perform a lookup of a syntax rule declaration of the word sequence in the LKB according to the synthetic annotation, and perform a syntactic analysis of the word sequence according to the syntax rule declaration, to determine a synthetic dependency tree of the word sequence. 4. The system of claim 1 , further comprising a computer-implemented morphological analyzer connected to the synthetic annotator and syntax checker and configured to receive the synthetic annotation from the synthetic annotator; receive the synthetic dependency tree from the syntax checker; determine an analytic annotation of the word sequence according to the synthetic annotation and the synthetic dependency tree, the analytic annotation comprising an analytic dependency tree of the word sequence, wherein a selected node of the analytic dependency tree comprises a plurality of nodes of the synthetic dependency tree; and display to a user the analytic annotation or the analytic dependency tree. | 0.628042 |
8,086,047 | 16 | 19 | 16. The system according to claim 14 , wherein said user interface provides capability for analysis type selection. | 16. The system according to claim 14 , wherein said user interface provides capability for analysis type selection. 19. The system according to claim 16 , wherein said analysis type includes either cluster generation or heat map generation. | 0.969077 |
8,408,914 | 1 | 3 | 1. A method for learning scripts of Chinese-character-based languages using a computer-based key-symbol system, the method comprising: storing a set of pages in an electronic key-symbol dictionary, each of the pages: having a bridge entry respectively associated with a single Chinese radical within a set of Chinese radicals in a Chinese-character-based language; having the single Chinese radical as a symbol; having a single keyword corresponding to a given meaning of the single Chinese radical, the single keyword: being in a user's language and having letters in the user's alphabet; and including therein a bridge comprised of at least one of the letters; and having a plurality of different memory joggers, each of the different memory joggers being a word in the user's language containing therein the bridge; creating a user-recognized radical dictionary by identifying a list of user-recognized Chinese radicals and electronically collating the pages associated with each of the user-recognized Chinese radicals; and creating a user-recognized complex-character dictionary by: identifying a complex Chinese multi-character to be learned; electronically transcribing a set of at least two of the user-recognized Chinese radicals within the complex Chinese multi-character to be learned; and electronically transcribing a mnemonic in the language of the user for recalling the written form of the complex Chinese multi-character, the mnemonic being based upon the keywords, memory joggers, and bridges associated with the recognized set of the at least two user-recognized Chinese radicals within the complex Chinese multi-character. | 1. A method for learning scripts of Chinese-character-based languages using a computer-based key-symbol system, the method comprising: storing a set of pages in an electronic key-symbol dictionary, each of the pages: having a bridge entry respectively associated with a single Chinese radical within a set of Chinese radicals in a Chinese-character-based language; having the single Chinese radical as a symbol; having a single keyword corresponding to a given meaning of the single Chinese radical, the single keyword: being in a user's language and having letters in the user's alphabet; and including therein a bridge comprised of at least one of the letters; and having a plurality of different memory joggers, each of the different memory joggers being a word in the user's language containing therein the bridge; creating a user-recognized radical dictionary by identifying a list of user-recognized Chinese radicals and electronically collating the pages associated with each of the user-recognized Chinese radicals; and creating a user-recognized complex-character dictionary by: identifying a complex Chinese multi-character to be learned; electronically transcribing a set of at least two of the user-recognized Chinese radicals within the complex Chinese multi-character to be learned; and electronically transcribing a mnemonic in the language of the user for recalling the written form of the complex Chinese multi-character, the mnemonic being based upon the keywords, memory joggers, and bridges associated with the recognized set of the at least two user-recognized Chinese radicals within the complex Chinese multi-character. 3. The method according to claim 1 , which further comprises carrying out the list-identifying step by electronically adding a new page of the electronic key-symbol dictionary to the user-recognized radical dictionary for each new user-recognized Chinese radical. | 0.787561 |
7,495,662 | 6 | 7 | 6. The system of claim 1 , wherein a terminal alphabet of the context free grammar comprises at least a solid, union, intersection and subtraction. | 6. The system of claim 1 , wherein a terminal alphabet of the context free grammar comprises at least a solid, union, intersection and subtraction. 7. The system of claim 6 , wherein the terminal alphabet further comprises juxtaposition. | 0.973715 |
9,465,795 | 1 | 3 | 1. A method, comprising: receiving enterprise network traffic associated with a particular user; identifying irrelevant documents in the received network traffic using a document filter; developing a personal vocabulary for the particular user based on the enterprise network traffic, wherein the irrelevant documents are not evaluated to develop the personal vocabulary, wherein the personal vocabulary is developed independent of additional users; determining an expertise associated with the particular user based, at least in part, on the personal vocabulary and activity of the additional users; determining a category associated with the particular user, wherein the category is at least partially based on applications used by the particular user; determining areas of interest for the particular user based on the personal vocabulary, the category, and inter-category terms, wherein the inter-category terms are used to link similar categories; determining associations for the particular user in relation to the additional users; and generating a feed based on a portion of the enterprise network traffic and areas of interest for the particular user, wherein the feed is automatically delivered to a subset of the additional users. | 1. A method, comprising: receiving enterprise network traffic associated with a particular user; identifying irrelevant documents in the received network traffic using a document filter; developing a personal vocabulary for the particular user based on the enterprise network traffic, wherein the irrelevant documents are not evaluated to develop the personal vocabulary, wherein the personal vocabulary is developed independent of additional users; determining an expertise associated with the particular user based, at least in part, on the personal vocabulary and activity of the additional users; determining a category associated with the particular user, wherein the category is at least partially based on applications used by the particular user; determining areas of interest for the particular user based on the personal vocabulary, the category, and inter-category terms, wherein the inter-category terms are used to link similar categories; determining associations for the particular user in relation to the additional users; and generating a feed based on a portion of the enterprise network traffic and areas of interest for the particular user, wherein the feed is automatically delivered to a subset of the additional users. 3. The method of claim 1 , wherein a profile is developed for the particular user, and wherein the profile can be manually changed by the particular user adding tags to be included in the personal vocabulary. | 0.762557 |
10,025,848 | 17 | 18 | 17. The non-transitory machine-readable storage medium of claim 11 , wherein the information is received as an electronic mail message. | 17. The non-transitory machine-readable storage medium of claim 11 , wherein the information is received as an electronic mail message. 18. The non-transitory machine-readable storage medium of claim 17 , wherein the electronic mail message includes an attachment of an audio file of the voicemail message corresponding to the portion of the information. | 0.917299 |
7,805,428 | 13 | 16 | 13. A method for search engine optimization, comprising: using log files for key-wordless rank checking by a first smart tool; using log files for hyperlink analysis by a second smart tool; identifying web authorities for competitive analysis by a third smart tool; providing live relevancy metrics in on-page optimization editor by a fourth smart tool; authorization code to prevent usage of a software license in more than one computer by a fifth smart tool ; extracting incoming links for a user website and querying search engines directly; processing website log files to identify every internal page on the user website, and external pages that are linking to each internal page; presenting the website log files in a referrer log files; and identifying link rich pages and link poor pages from extracted hyperlink information where the link poor pages are less likely to be included in search engines index than the link rich pages. | 13. A method for search engine optimization, comprising: using log files for key-wordless rank checking by a first smart tool; using log files for hyperlink analysis by a second smart tool; identifying web authorities for competitive analysis by a third smart tool; providing live relevancy metrics in on-page optimization editor by a fourth smart tool; authorization code to prevent usage of a software license in more than one computer by a fifth smart tool ; extracting incoming links for a user website and querying search engines directly; processing website log files to identify every internal page on the user website, and external pages that are linking to each internal page; presenting the website log files in a referrer log files; and identifying link rich pages and link poor pages from extracted hyperlink information where the link poor pages are less likely to be included in search engines index than the link rich pages. 16. The method for search engine optimization of claim 13 , wherein the first smart tool using log files for key-wordless rank checking in an SEO tool identifies an exact position on the search engine by querying the search engine directly on a specific page and a small window of one or two pages from an identified page. | 0.882909 |
8,200,834 | 13 | 15 | 13. A computer program product on a non-transitory computer-readable medium embodying computer program code for controlling access to protected resources within a distributed data processing system, the computer program code comprising computer executable instructions configured for: using a processor configured to perform data processing operations; receiving at a first server from a client a first single-use domain token associated with said client and a request to access a protected resource; validating said single-use domain token; generating a client authorization credential request; sending to a second server said client authorization credential request, said first single-use domain token, and a second single-use domain token associated with said first server, wherein said first server and said second server are operated within a common domain; receiving a single-use service token from said second server, said single-use service token associated with said client; processing said single-use service token to generate a response to said request; refreshing said single-use service token; and providing said response and said refreshed single-use service token to said client. | 13. A computer program product on a non-transitory computer-readable medium embodying computer program code for controlling access to protected resources within a distributed data processing system, the computer program code comprising computer executable instructions configured for: using a processor configured to perform data processing operations; receiving at a first server from a client a first single-use domain token associated with said client and a request to access a protected resource; validating said single-use domain token; generating a client authorization credential request; sending to a second server said client authorization credential request, said first single-use domain token, and a second single-use domain token associated with said first server, wherein said first server and said second server are operated within a common domain; receiving a single-use service token from said second server, said single-use service token associated with said client; processing said single-use service token to generate a response to said request; refreshing said single-use service token; and providing said response and said refreshed single-use service token to said client. 15. The computer-readable medium of claim 13 , wherein said first single-use domain token comprises session information for performing session management with respect to said client. | 0.829588 |
6,076,059 | 1 | 8 | 1. A computerized method for aligning text segments of a text file with audio segments of an audio file, comprising the steps of: generating a vocabulary and language model from the text file, generation of said model involving determination of relative probabilities of all one, two, and three word sequences in all unaligned text segments of the text file based upon frequencies of occurrences of said sequences in said unaligned text segments, all of said text segments being initially classified as unaligned text segments; recognizing a word list from the audio segments using the vocabulary and language model but without considering the text file; aligning the word list with the text segments based upon respective scores for all possible alignments of words in the word list with the text segments, each respective score being weighted to increase each respective score by a relatively greater amount if a respective alignment associated with the respective score involves relatively longer sequences of correctly aligned words; choosing corresponding anchors in the word list and text segments in accordance with the respective scores; partitioning the text and the audio segments into unaligned and aligned text and audio segments according to the anchors; and repeating the generating, recognizing, aligning, choosing, and partitioning steps with the unaligned text and audio segments until a termination condition is reached. | 1. A computerized method for aligning text segments of a text file with audio segments of an audio file, comprising the steps of: generating a vocabulary and language model from the text file, generation of said model involving determination of relative probabilities of all one, two, and three word sequences in all unaligned text segments of the text file based upon frequencies of occurrences of said sequences in said unaligned text segments, all of said text segments being initially classified as unaligned text segments; recognizing a word list from the audio segments using the vocabulary and language model but without considering the text file; aligning the word list with the text segments based upon respective scores for all possible alignments of words in the word list with the text segments, each respective score being weighted to increase each respective score by a relatively greater amount if a respective alignment associated with the respective score involves relatively longer sequences of correctly aligned words; choosing corresponding anchors in the word list and text segments in accordance with the respective scores; partitioning the text and the audio segments into unaligned and aligned text and audio segments according to the anchors; and repeating the generating, recognizing, aligning, choosing, and partitioning steps with the unaligned text and audio segments until a termination condition is reached. 8. The method of claim 1 further including the steps of determining a plurality of possible alignments, scoring each possible alignment, and selecting a best alignment using dynamic programming. | 0.502564 |
9,502,024 | 7 | 8 | 7. A system for controlling operation of an automatic speech recognition (ASR) system, the ASR system comprising at least one model having a plurality of representations of phones, the system comprising at least one computer programmed by executable code to: receive an audio signal comprising a first user input; analyze the audio signal to identify at least one first phone as having a selected recognition performance characteristic, wherein the at least one computer is programmed to analyze the audio signal at least in part by comparing one or more sounds in the audio signal with a representation of the at least one first phone in the at least one model; subsequent to receiving and analyzing the audio signal comprising the first user input, select a user prompt to be presented to a user of a speech-responsive application to elicit a second user input, wherein the speech-responsive application is programmed to perform at least one action based on the second user input, and wherein the user prompt is selected based on a determination of whether the user is expected to speak, in response to the user prompt, the at least one first phone which is identified as having the selected recognition performance characteristic; cause the user prompt to be presented to the user of the speech-responsive application, wherein: the at least one first phone is associated with a first confidence score, the first confidence score being lower than a selected confidence threshold, and the user prompt is selected to invite the user to speak an input phrase that combines a first word with one or more second words, wherein the first word comprises the at least one first phone associated with the first confidence score that is lower than the selected confidence threshold, and the one or more second words comprise at least one second phone that is associated with a second confidence score, the second confidence score being higher than the selected confidence threshold; and perform, by the speech-responsive application, the at least one action based on the second user input. | 7. A system for controlling operation of an automatic speech recognition (ASR) system, the ASR system comprising at least one model having a plurality of representations of phones, the system comprising at least one computer programmed by executable code to: receive an audio signal comprising a first user input; analyze the audio signal to identify at least one first phone as having a selected recognition performance characteristic, wherein the at least one computer is programmed to analyze the audio signal at least in part by comparing one or more sounds in the audio signal with a representation of the at least one first phone in the at least one model; subsequent to receiving and analyzing the audio signal comprising the first user input, select a user prompt to be presented to a user of a speech-responsive application to elicit a second user input, wherein the speech-responsive application is programmed to perform at least one action based on the second user input, and wherein the user prompt is selected based on a determination of whether the user is expected to speak, in response to the user prompt, the at least one first phone which is identified as having the selected recognition performance characteristic; cause the user prompt to be presented to the user of the speech-responsive application, wherein: the at least one first phone is associated with a first confidence score, the first confidence score being lower than a selected confidence threshold, and the user prompt is selected to invite the user to speak an input phrase that combines a first word with one or more second words, wherein the first word comprises the at least one first phone associated with the first confidence score that is lower than the selected confidence threshold, and the one or more second words comprise at least one second phone that is associated with a second confidence score, the second confidence score being higher than the selected confidence threshold; and perform, by the speech-responsive application, the at least one action based on the second user input. 8. The system of claim 7 , wherein the at least one computer is further programmed to: receive speech from the user in response to the user prompt; and use the speech to train the at least one model. | 0.764218 |
8,571,047 | 2 | 3 | 2. The method according to claim 1 , wherein the maintaining, by the MG, the packet filter rule context according to the processing message comprises one of: creating a new packet filter rule context according to a message for creating the packet filter rule context sent by the MGC, or modifying a created packet filter rule context according to a message for modifying the packet filter rule context sent by the MGC, or deleting a created packet filter rule context according to a message for deleting the packet filter rule context sent by the MGC; and returning a reply message to the MGC after completion of the creation, modification or deletion. | 2. The method according to claim 1 , wherein the maintaining, by the MG, the packet filter rule context according to the processing message comprises one of: creating a new packet filter rule context according to a message for creating the packet filter rule context sent by the MGC, or modifying a created packet filter rule context according to a message for modifying the packet filter rule context sent by the MGC, or deleting a created packet filter rule context according to a message for deleting the packet filter rule context sent by the MGC; and returning a reply message to the MGC after completion of the creation, modification or deletion. 3. The method according to claim 2 , wherein the modifying the created packet filter rule context according to the message for modifying the packet filter rule context sent by the MGC comprises one of: adding, modifying or deleting a filter rule carried in the packet filter rule context according to the message for modifying the packet filter rule context sent by the MGC. | 0.872789 |
10,102,771 | 12 | 13 | 12. The computer readable recording medium according to claim 11 , wherein the step of determining whether the second phoneme sequence is the same as the standard phoneme sequence corresponding to the learning sentence comprises: comparing the second phoneme sequence with the standard phoneme sequence by using a dynamic time warping (DTW) algorithm; and determining whether the second phoneme sequence is the same as the standard phoneme sequence according to a comparison result of the DTW algorithm. | 12. The computer readable recording medium according to claim 11 , wherein the step of determining whether the second phoneme sequence is the same as the standard phoneme sequence corresponding to the learning sentence comprises: comparing the second phoneme sequence with the standard phoneme sequence by using a dynamic time warping (DTW) algorithm; and determining whether the second phoneme sequence is the same as the standard phoneme sequence according to a comparison result of the DTW algorithm. 13. The computer readable recording medium according to claim 12 , wherein the step of generating the ancillary information comprising the at least one error word in the input sentence that is different from the learning sentence comprises: obtaining an error information between the second phoneme sequence and the standard phoneme sequence according to the comparison result of the DTW algorithm; obtaining the at least one error word from the input sentence and at least one standard word corresponding to the at least one error word from the learning sentence according to the error information; and generating the ancillary information comprising the at least one error word and the at least one standard word. | 0.887579 |
5,497,320 | 1 | 2 | 1. A two-level document processing method preparing a downstream document on the basis of an upstream document, said upstream document being a document associated with a product and placed near a user, and said downstream document being a document associated with the product and placed near a maker, said method comprising the steps of: comparing the upstream document with an upstream deletion document including information which is particular to the upstream document and is unnecessary for the downstream document; selecting, from the upstream document and based on results of the step of comparing, a deletion candidate to be deleted from the upstream document until no more deletion candidate is found; and preparing the downstream document by performing deletion or non-deletion of the deletion candidate from the upstream document in accordance with results of the step of selecting. | 1. A two-level document processing method preparing a downstream document on the basis of an upstream document, said upstream document being a document associated with a product and placed near a user, and said downstream document being a document associated with the product and placed near a maker, said method comprising the steps of: comparing the upstream document with an upstream deletion document including information which is particular to the upstream document and is unnecessary for the downstream document; selecting, from the upstream document and based on results of the step of comparing, a deletion candidate to be deleted from the upstream document until no more deletion candidate is found; and preparing the downstream document by performing deletion or non-deletion of the deletion candidate from the upstream document in accordance with results of the step of selecting. 2. The two-level document processing method as claimed in claim 1, wherein said step of preparing comprises the steps of: searching said upstream deletion document for corresponding information corresponding to the deletion candidate; displaying, if the corresponding information is present in the upstream deletion document, the deletion candidate and the corresponding information to have approval of deletion; deleting the deletion candidate from the upstream document if the deletion of the deletion candidate is approved; and storing the deletion candidate and the corresponding information into a deletion file. | 0.978988 |
9,588,596 | 6 | 8 | 6. A handheld electronic device comprising: at least one processor; a memory device that stores a set of instructions that, when executed by the at least one processor, causes the at least one processor to: detect an ambiguous input including one or more selections of one or more input keys; generate one or more prefix objects corresponding with the ambiguous input; generate an output set including at least some of the one or more prefix objects, wherein each of the at least some of the one or more prefix objects is associated with an identified corresponding word object; determine a quantity of prefix objects in the output set is fewer than a predetermined quantity, and, based on the determination, adding as an orphan prefix object to the output set an additional prefix object of the one or more of prefix objects for which a corresponding word object was not identified; output the output set; detect an additional selection of one or more input keys; determine that a selection input was not detected between the detection of the ambiguous input and the detection of the additional selection; and generate one or more additional prefix objects corresponding with the ambiguous input plus the additional selection without generating an additional prefix object corresponding with the orphan prefix object. | 6. A handheld electronic device comprising: at least one processor; a memory device that stores a set of instructions that, when executed by the at least one processor, causes the at least one processor to: detect an ambiguous input including one or more selections of one or more input keys; generate one or more prefix objects corresponding with the ambiguous input; generate an output set including at least some of the one or more prefix objects, wherein each of the at least some of the one or more prefix objects is associated with an identified corresponding word object; determine a quantity of prefix objects in the output set is fewer than a predetermined quantity, and, based on the determination, adding as an orphan prefix object to the output set an additional prefix object of the one or more of prefix objects for which a corresponding word object was not identified; output the output set; detect an additional selection of one or more input keys; determine that a selection input was not detected between the detection of the ambiguous input and the detection of the additional selection; and generate one or more additional prefix objects corresponding with the ambiguous input plus the additional selection without generating an additional prefix object corresponding with the orphan prefix object. 8. The handheld electronic device of claim 6 , wherein the set of instructions, when executed by the at least one processor, further causes the at least one processor to: determine that a particular prefix object of the one or more prefix objects is of a length having a first quantity of characters; determine that a particular identified word object corresponding with the particular prefix object is of a length having a second quantity of characters; determine that the first quantity is equal to the second quantity; and position the particular prefix object in the output set at a position corresponding with a relatively high frequency. | 0.500776 |
10,073,926 | 14 | 15 | 14. A computer program product for team analytics context graph generation and augmentation, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory medium per se, the program instructions being executable by a device to cause the device to perform a method comprising: extracting a set of relevant features from a received message; predicting a context graph corresponding to the received message being sparse based on the extracted relevant features, a context of the received message being indeterminable from the context graph in response to the context graph being sparse, wherein the context graph is a sparse context graph in response to a number of nodes and associated edges of the context graph corresponding to the received message being below a preset threshold number of nodes and associated edges; generating an augmented context graph in response to the context graph being predicted to be sparse, wherein the augmented context graph is generated by adding a quantity of nodes and associated edges to the sparse context graph such that the context of the received message is determinable from the augmented context graph, wherein generating the augmented context graph comprises: extracting data from the received message, the data comprising subjects and terms in the received message; automatically generating a set of queries using the subjects and terms extracted from the received message to generate a list of selected users with a similar expertise or content corresponding to the subjects and terms; generating the list of selected users based on the set of queries; determining connections between each selected user of the list of selected users, wherein determining connections comprises searching a cache comprising previously generated context graphs and sub-graphs; translating the selected users and connections between the selected users into nodes and edges of the augmented context graph; building the augmented context graph in a data structure of parent-child relationships from translating the selected users and connections into the nodes and edges of the augmented context graph, the context of the received message being determinable from the augmented context graph; and presenting the augmented context graph. | 14. A computer program product for team analytics context graph generation and augmentation, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory medium per se, the program instructions being executable by a device to cause the device to perform a method comprising: extracting a set of relevant features from a received message; predicting a context graph corresponding to the received message being sparse based on the extracted relevant features, a context of the received message being indeterminable from the context graph in response to the context graph being sparse, wherein the context graph is a sparse context graph in response to a number of nodes and associated edges of the context graph corresponding to the received message being below a preset threshold number of nodes and associated edges; generating an augmented context graph in response to the context graph being predicted to be sparse, wherein the augmented context graph is generated by adding a quantity of nodes and associated edges to the sparse context graph such that the context of the received message is determinable from the augmented context graph, wherein generating the augmented context graph comprises: extracting data from the received message, the data comprising subjects and terms in the received message; automatically generating a set of queries using the subjects and terms extracted from the received message to generate a list of selected users with a similar expertise or content corresponding to the subjects and terms; generating the list of selected users based on the set of queries; determining connections between each selected user of the list of selected users, wherein determining connections comprises searching a cache comprising previously generated context graphs and sub-graphs; translating the selected users and connections between the selected users into nodes and edges of the augmented context graph; building the augmented context graph in a data structure of parent-child relationships from translating the selected users and connections into the nodes and edges of the augmented context graph, the context of the received message being determinable from the augmented context graph; and presenting the augmented context graph. 15. The computer program product of claim 14 , wherein the method further comprises: caching the augmented context graph; continuously monitoring new messages being received; automatically evaluating the relevant features of each new message; and updating the relevant features and associated information based on the evaluation for presenting a new context graph or augmented contextual graph. | 0.501266 |
5,542,090 | 3 | 6 | 3. The method of claim 1, wherein said step of generating the index term signature for each index term in the document corpus, comprises: generating a term vector based on each adjacent term within the predetermined number of adjacent terms; and combining the generated term vectors to form the index term signature. | 3. The method of claim 1, wherein said step of generating the index term signature for each index term in the document corpus, comprises: generating a term vector based on each adjacent term within the predetermined number of adjacent terms; and combining the generated term vectors to form the index term signature. 6. The method of claim 3, wherein said predetermined number of adjacent terms succeed the index term. | 0.967356 |
7,519,201 | 21 | 22 | 21. A computer program product, comprising a computer readable medium storing computer executable code for human detection and a pose classification, the computer executable code performing steps of: receiving a probabilistic model derived from a set of training images in at least one of an unsupervised training stage or a semi-supervised training stage; generating a set of test image descriptors representing the test image; determining a likelihood that the test image contains a human based on parameters of the probabilistic model and the test image descriptors; and classifying a body pose of a detected human in the test image based on the test image descriptors and the parameters of the probabilistic model. | 21. A computer program product, comprising a computer readable medium storing computer executable code for human detection and a pose classification, the computer executable code performing steps of: receiving a probabilistic model derived from a set of training images in at least one of an unsupervised training stage or a semi-supervised training stage; generating a set of test image descriptors representing the test image; determining a likelihood that the test image contains a human based on parameters of the probabilistic model and the test image descriptors; and classifying a body pose of a detected human in the test image based on the test image descriptors and the parameters of the probabilistic model. 22. The computer program product of claim 21 wherein the training stage comprises an unsupervised training stage wherein human poses in the set of training images are unlabeled. | 0.882 |
7,693,951 | 2 | 7 | 2. The data processing system of claim 1 further comprising: displaying means displaying the contact list; selecting means, responsive to a user input for selecting the particular contact on the contact list; and displaying means for displaying instant messages stored in the folder associated with the particular contact. | 2. The data processing system of claim 1 further comprising: displaying means displaying the contact list; selecting means, responsive to a user input for selecting the particular contact on the contact list; and displaying means for displaying instant messages stored in the folder associated with the particular contact. 7. The data processing system of claim 2 further comprising: adding means for adding a new folder to the set of folders when a listing of a new contact is added to the contact list. | 0.914461 |
7,565,607 | 12 | 15 | 12. A content generation system for generating content describing the progressive steps to be taken by a user to perform a task on a user interface, comprising: a recording system configured to: receive an indication that the user has taken a step by manipulating a control record an image of the control; record separately a contextual image, larger than the image of the control, at least partially showing a context of the control with respect to the user interface; record separately an image of a parent window on the user interface that is parent to the control; and an editor component configured to: display the image of the control, the contextual image, and the image of the parent window; automatically receive and edit pre-associated text with a description of steps in the performance of the task for each image; repeat the above recording system steps and above editor component steps for user manipulation of at least another control; generate the content describing the progressive steps to be taken to perform a task on the user interface, with the image of the control, the contextual image, and the image of the parent window separately embedded in the corresponding pre-associated text. | 12. A content generation system for generating content describing the progressive steps to be taken by a user to perform a task on a user interface, comprising: a recording system configured to: receive an indication that the user has taken a step by manipulating a control record an image of the control; record separately a contextual image, larger than the image of the control, at least partially showing a context of the control with respect to the user interface; record separately an image of a parent window on the user interface that is parent to the control; and an editor component configured to: display the image of the control, the contextual image, and the image of the parent window; automatically receive and edit pre-associated text with a description of steps in the performance of the task for each image; repeat the above recording system steps and above editor component steps for user manipulation of at least another control; generate the content describing the progressive steps to be taken to perform a task on the user interface, with the image of the control, the contextual image, and the image of the parent window separately embedded in the corresponding pre-associated text. 15. The content generation system of claim 12 wherein the recording system comprises: a component configured to identify a position on the user interface, and a size, of the control manipulated by the user. | 0.667742 |
9,265,441 | 1 | 3 | 1. A computer-implemented method for assessing a traumatic brain injury (TBI) in a patient, the method comprising: obtaining diffusion magnetic resonance imaging (dMRI) data from the patient; obtaining resting-state functional magnetic resonance imaging (rs-fMRI) data from the patient; extracting connectivity features between different regions of a brain of the patient, the connectivity features extracted from the rs-fMRI data obtained from the patient and/or from the diffusion magnetic resonance imaging (dMRI) data obtained from the patient; extracting network features from the connectivity features; and determining a TBI diagnosis for the patient based on at least the network features and the connectivity features using a trained classifier. | 1. A computer-implemented method for assessing a traumatic brain injury (TBI) in a patient, the method comprising: obtaining diffusion magnetic resonance imaging (dMRI) data from the patient; obtaining resting-state functional magnetic resonance imaging (rs-fMRI) data from the patient; extracting connectivity features between different regions of a brain of the patient, the connectivity features extracted from the rs-fMRI data obtained from the patient and/or from the diffusion magnetic resonance imaging (dMRI) data obtained from the patient; extracting network features from the connectivity features; and determining a TBI diagnosis for the patient based on at least the network features and the connectivity features using a trained classifier. 3. The computer-implemented method of claim 1 , further comprising extracting functional features from the rs-fMRI data, wherein the trained classifier uses the functional features. | 0.92588 |
7,707,160 | 20 | 21 | 20. The computer system of claim 19 further comprising at least one web browser operable to display the factual knowledge and the new knowledge. | 20. The computer system of claim 19 further comprising at least one web browser operable to display the factual knowledge and the new knowledge. 21. The computer system of claim 20 wherein the web browser is operable to display a summary information screen providing general information on a named object using a portion of the factual knowledge retrieved from the knowledge base. | 0.919905 |
8,537,401 | 19 | 20 | 19. The system of claim 17 , wherein receiving input identifying a printing option to be performed in response to locating the first text item in the electronic file further comprises receiving input identifying to select an exception page in connection with a subset of documents. | 19. The system of claim 17 , wherein receiving input identifying a printing option to be performed in response to locating the first text item in the electronic file further comprises receiving input identifying to select an exception page in connection with a subset of documents. 20. The system of claim 19 , wherein receiving input identifying a printing option to be performed in response to locating the first text item in the electronic file further comprises receiving input identifying for the exception page in connection with a subset of documents to perform one or more of the following: override media type; perform simplex printing; perform duplex printing; print on a and particular media color. | 0.873669 |
9,274,698 | 1 | 7 | 1. A method comprising: displaying text on a touch screen in an original configuration; detecting a touch associated with a first area of the touch screen; displaying an enlarged view of the first area comprising at least two characters of the text; detecting selection of a first character from the at least two characters; resuming the original configuration after detecting the selection of the first character, wherein the first character is shown as designated text when the original configuration is resumed. | 1. A method comprising: displaying text on a touch screen in an original configuration; detecting a touch associated with a first area of the touch screen; displaying an enlarged view of the first area comprising at least two characters of the text; detecting selection of a first character from the at least two characters; resuming the original configuration after detecting the selection of the first character, wherein the first character is shown as designated text when the original configuration is resumed. 7. The method of claim 1 , wherein more than three lines of text are shown when in the original configuration. | 0.921875 |
9,053,423 | 1 | 19 | 1. A method for proposing candidate solutions for updating a knowledge base, comprising: for each of a set of knowledge base solutions, each knowledge base solution comprising a sequence of main steps, expressed in a natural language, to be performed on a class of device, processing the knowledge base solution to generate a first action sequence of atomic steps, each of the atomic steps including a verb and an object, the object comprising a noun which is in a syntactic dependency with the verb; receiving a recorded solution, expressed in a natural language, comprising actions performed on a device in the device class; processing the recorded solution to generate a second action sequence of atomic steps, each of the atomic steps including a verb and an object, the object comprising a noun which is in a syntactic dependency with the verb; with a processor, comparing the second action sequence with each of the first action sequences to determine whether the recorded solution corresponds to one of the knowledge base solutions; and based on the comparison, providing for proposing an update to the knowledge base, based on the recorded solution. | 1. A method for proposing candidate solutions for updating a knowledge base, comprising: for each of a set of knowledge base solutions, each knowledge base solution comprising a sequence of main steps, expressed in a natural language, to be performed on a class of device, processing the knowledge base solution to generate a first action sequence of atomic steps, each of the atomic steps including a verb and an object, the object comprising a noun which is in a syntactic dependency with the verb; receiving a recorded solution, expressed in a natural language, comprising actions performed on a device in the device class; processing the recorded solution to generate a second action sequence of atomic steps, each of the atomic steps including a verb and an object, the object comprising a noun which is in a syntactic dependency with the verb; with a processor, comparing the second action sequence with each of the first action sequences to determine whether the recorded solution corresponds to one of the knowledge base solutions; and based on the comparison, providing for proposing an update to the knowledge base, based on the recorded solution. 19. A computer program product comprising a non-transitory recording medium storing instructions, which when executed on a computer, causes the computer to perform the method of claim 1 . | 0.792683 |
8,681,098 | 48 | 52 | 48. The system of claim 42 , comprising: generating at least one offset; and forming the data capsule to include the at least one offset. | 48. The system of claim 42 , comprising: generating at least one offset; and forming the data capsule to include the at least one offset. 52. The system of claim 48 , wherein at least one of the first offset and the second offset include metadata, the metadata comprising context-specific metadata. | 0.942071 |
8,572,024 | 13 | 18 | 13. An apparatus for implementing a data extraction process, the apparatus comprising a workstation storage device, a workstation processor connected to the workstation storage device, the workstation storage device storing a workstation program for controlling the workstation processor, and the workstation processor operative with the workstation program to: receive one or more web sites selected by a user for data extraction; collect a plurality of co-occurring different HTML structured documents for each of the selected web sites; form a plurality of clusters comprising different subsets of a group of co-occurring Hyper Text Mark-up Language (HTML) structured documents for each of the selected web sites, wherein: each cluster comprises a different HTML structured document of the group of co-occurring HTML structured documents as a centroid document and other HTML structured documents of the group of co-occurring HTML structured documents that achieve a threshold of similarity with respect to the centroid document, the clusters are formed by comparing each co-occurring HTML structured document to each centroid document of each cluster based on relative structural similarity of HTML data structure of each co-occurring HTML structured document with respect to HTML data structure of each centroid document of each cluster, and an alignment algorithm is used to determine the co-occurring HTML structured documents that achieve the threshold of similarity with respect to each centroid document by comparing structured locations of data fields for storing data elements within each centroid document and structured locations of corresponding data fields for storing data elements within each of the co-occurring HTML structured documents, the co-occurring HTML structured documents are compared to each centroid document based on similarity of structured locations of corresponding data fields within the HTML data structures, display the centroid document of a particular cluster selected from a list of clusters; mark a data element on the centroid document of the particular cluster; and provide a user interface displaying content of data elements identified from the other HTML structured documents of the particular cluster on a computer display. | 13. An apparatus for implementing a data extraction process, the apparatus comprising a workstation storage device, a workstation processor connected to the workstation storage device, the workstation storage device storing a workstation program for controlling the workstation processor, and the workstation processor operative with the workstation program to: receive one or more web sites selected by a user for data extraction; collect a plurality of co-occurring different HTML structured documents for each of the selected web sites; form a plurality of clusters comprising different subsets of a group of co-occurring Hyper Text Mark-up Language (HTML) structured documents for each of the selected web sites, wherein: each cluster comprises a different HTML structured document of the group of co-occurring HTML structured documents as a centroid document and other HTML structured documents of the group of co-occurring HTML structured documents that achieve a threshold of similarity with respect to the centroid document, the clusters are formed by comparing each co-occurring HTML structured document to each centroid document of each cluster based on relative structural similarity of HTML data structure of each co-occurring HTML structured document with respect to HTML data structure of each centroid document of each cluster, and an alignment algorithm is used to determine the co-occurring HTML structured documents that achieve the threshold of similarity with respect to each centroid document by comparing structured locations of data fields for storing data elements within each centroid document and structured locations of corresponding data fields for storing data elements within each of the co-occurring HTML structured documents, the co-occurring HTML structured documents are compared to each centroid document based on similarity of structured locations of corresponding data fields within the HTML data structures, display the centroid document of a particular cluster selected from a list of clusters; mark a data element on the centroid document of the particular cluster; and provide a user interface displaying content of data elements identified from the other HTML structured documents of the particular cluster on a computer display. 18. The apparatus of claim 13 , further configured to identify a data element on each of the other HTML structured documents of the particular cluster that is stored within a data field having a structured location that corresponds to the structured location of the data field storing the marked data element within the centroid document of the particular cluster. | 0.833028 |
9,058,393 | 8 | 9 | 8. The system of claim 3 , wherein: i) said plurality of aftermarket sales information further comprises: a) a domain aftermarket data identifying said sold domain name as being sold in said domain name aftermarket; b) a highest bid offered, in said domain name aftermarket, for said sold domain name; and c) a discount domain data identifying said sold domain name as having been sold at a discount in said domain name aftermarket; and ii) said keyword record generating module is further configured to write, to said database: said domain aftermarket data; said highest bid offered; and said discount domain data. | 8. The system of claim 3 , wherein: i) said plurality of aftermarket sales information further comprises: a) a domain aftermarket data identifying said sold domain name as being sold in said domain name aftermarket; b) a highest bid offered, in said domain name aftermarket, for said sold domain name; and c) a discount domain data identifying said sold domain name as having been sold at a discount in said domain name aftermarket; and ii) said keyword record generating module is further configured to write, to said database: said domain aftermarket data; said highest bid offered; and said discount domain data. 9. The system of claim 8 , wherein said keyword record generating module is configured to generate said monetary value by calculating a quotient calculated by dividing said highest bid offered by said quantity of said one or more keywords. | 0.943712 |
5,412,566 | 1 | 8 | 1. A variable replacement apparatus which replaces variable names in a text with corresponding variable values, each variable name formed by at least one character and each character in the variable name having a corresponding format, the apparatus comprising: variable name extracting means for extracting a variable name from the text; variable value acquisition means for obtaining a variable value corresponding to the variable name extracted by said variable name extracting means; variable name analyzing means for analyzing the corresponding format of each character forming the variable name; variable value converting means for converting the variable value obtained by said variable value acquisition means so that the variable value has a format which is determined in accordance with the corresponding format of each character forming the variable name; and variable replacing means for replacing the variable name in the text by the converted variable value. | 1. A variable replacement apparatus which replaces variable names in a text with corresponding variable values, each variable name formed by at least one character and each character in the variable name having a corresponding format, the apparatus comprising: variable name extracting means for extracting a variable name from the text; variable value acquisition means for obtaining a variable value corresponding to the variable name extracted by said variable name extracting means; variable name analyzing means for analyzing the corresponding format of each character forming the variable name; variable value converting means for converting the variable value obtained by said variable value acquisition means so that the variable value has a format which is determined in accordance with the corresponding format of each character forming the variable name; and variable replacing means for replacing the variable name in the text by the converted variable value. 8. The variable replacement apparatus as claimed in claim 1, wherein said variable name analyzing means analyzes the corresponding formats of respective characters forming the variable name, the corresponding formats including different colors. | 0.757455 |
8,494,834 | 22 | 31 | 22. A system for local, computer-aided translation using remotely-generated translation predictions comprising: a remote translation server comprising: a memory means for providing access to a stored translation, a remote processor means for determining that the translation stored in the remote translation memory is useful in translating a first portion of a document, and a transmitter means for transmitting the stored translation; and a local machine comprising: a receiver means for receiving the stored translation, and a local processor means for: determining, prior to receiving a communication from the remote translation memory indicating that the remote translation memory stores an updated version of the stored translation, and prior to receiving a request from a translator for the translation of the first portion of the document, that the means for providing access to the stored translation stores an updated version of the stored translation, identifying the updated version of the stored translation as useful in translating a second portion of the document, wherein the identification occurs before a translation of the second portion of the local document is generated, and using the updated version of the stored translation to generate a translation of the second portion of the document, the generation of the translation being in response to the identification of the utility of the updated version of the stored translation in translating the second portion of the document. | 22. A system for local, computer-aided translation using remotely-generated translation predictions comprising: a remote translation server comprising: a memory means for providing access to a stored translation, a remote processor means for determining that the translation stored in the remote translation memory is useful in translating a first portion of a document, and a transmitter means for transmitting the stored translation; and a local machine comprising: a receiver means for receiving the stored translation, and a local processor means for: determining, prior to receiving a communication from the remote translation memory indicating that the remote translation memory stores an updated version of the stored translation, and prior to receiving a request from a translator for the translation of the first portion of the document, that the means for providing access to the stored translation stores an updated version of the stored translation, identifying the updated version of the stored translation as useful in translating a second portion of the document, wherein the identification occurs before a translation of the second portion of the local document is generated, and using the updated version of the stored translation to generate a translation of the second portion of the document, the generation of the translation being in response to the identification of the utility of the updated version of the stored translation in translating the second portion of the document. 31. The system of claim 22 , wherein the receiver further comprises a means for receiving an updated version of the stored translation replacing the received translation. | 0.729299 |
9,116,865 | 15 | 16 | 15. The non-transitory computer-readable medium of claim 13 , wherein the educational electronic document is in a first language, wherein the related content document is a dictionary in a second language, and wherein the computer program instructions further comprise instructions for: translating the identified term into the second language; and identifying a definition associated with the translated term in the second language dictionary; wherein extracting the content associated with the identified term comprises extracting the definition associated with the translated term. | 15. The non-transitory computer-readable medium of claim 13 , wherein the educational electronic document is in a first language, wherein the related content document is a dictionary in a second language, and wherein the computer program instructions further comprise instructions for: translating the identified term into the second language; and identifying a definition associated with the translated term in the second language dictionary; wherein extracting the content associated with the identified term comprises extracting the definition associated with the translated term. 16. The non-transitory computer-readable medium of claim 15 , the computer program instructions further comprising instructions for: extracting from the second language dictionary, an audio file including a pronunciation of the identified term in the second language. | 0.904095 |
8,276,101 | 1 | 3 | 1. A method comprising: receiving, using a presence-sensitive display coupled to a computing device, a first user input comprising a first drawing gesture associated with a first area for user input defined at the presence-sensitive display, wherein the first user input specifies one or more characters to be displayed at the presence-sensitive display, and wherein the first drawing gesture includes a drawn representation of the one or more characters; receiving, using the presence-sensitive display, a second user input comprising a second drawing gesture, wherein the second drawing gesture spans only the first area and a second area for user input defined at the presence-sensitive display, and wherein the second user input specifies a first editing operation associated with the one or more characters; applying, by the computing device, the first editing operation to the one or more characters in response to receiving the second user input; receiving, using the presence-sensitive display, a third user input comprising a third drawing gesture, wherein the third drawing gesture spans the first area, the second area, and a third area for user input defined at the presence-sensitive display, and wherein the third user input specifies a second editing operation associated with the one or more characters; and applying, by the computing device, the second editing operation to the one or more characters in response to receiving the third user input. | 1. A method comprising: receiving, using a presence-sensitive display coupled to a computing device, a first user input comprising a first drawing gesture associated with a first area for user input defined at the presence-sensitive display, wherein the first user input specifies one or more characters to be displayed at the presence-sensitive display, and wherein the first drawing gesture includes a drawn representation of the one or more characters; receiving, using the presence-sensitive display, a second user input comprising a second drawing gesture, wherein the second drawing gesture spans only the first area and a second area for user input defined at the presence-sensitive display, and wherein the second user input specifies a first editing operation associated with the one or more characters; applying, by the computing device, the first editing operation to the one or more characters in response to receiving the second user input; receiving, using the presence-sensitive display, a third user input comprising a third drawing gesture, wherein the third drawing gesture spans the first area, the second area, and a third area for user input defined at the presence-sensitive display, and wherein the third user input specifies a second editing operation associated with the one or more characters; and applying, by the computing device, the second editing operation to the one or more characters in response to receiving the third user input. 3. The method of claim 1 , wherein the second area comprises a subset of the first area, and wherein the second area is defined by a first graphical boundary that partitions the first area. | 0.763158 |
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