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8,396,815 | 1 | 5 |
1. A method for process automation, comprising: monitoring one or more workstations including monitoring screen contents and user actions at the workstations by executing a screen scraper module to obtain a dynamically updated current set of character and graphical information from screens of the workstations that includes user-entered data and retrieved screen data; analyzing the current set to identify monitored functional events; defining focal states as sequences of functional events, wherein the current set comprises time intervals associated with the user actions, respectively and the sequences of functional events of at least a portion of the focal states include the time intervals; generating one or more facilitating scripts associated with respective ones of the focal states, wherein the facilitating scripts each provide one or more automatic actions; matching a sequence of the monitored functional events to the sequence of functional events of one of the focal states; and applying the one or more automatic actions of the facilitating script associated with the one focal state; wherein said steps are implemented in either: computer hardware configured to perform said steps, or computer software embodied in a non-transitory, tangible, computer-readable storage medium.
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1. A method for process automation, comprising: monitoring one or more workstations including monitoring screen contents and user actions at the workstations by executing a screen scraper module to obtain a dynamically updated current set of character and graphical information from screens of the workstations that includes user-entered data and retrieved screen data; analyzing the current set to identify monitored functional events; defining focal states as sequences of functional events, wherein the current set comprises time intervals associated with the user actions, respectively and the sequences of functional events of at least a portion of the focal states include the time intervals; generating one or more facilitating scripts associated with respective ones of the focal states, wherein the facilitating scripts each provide one or more automatic actions; matching a sequence of the monitored functional events to the sequence of functional events of one of the focal states; and applying the one or more automatic actions of the facilitating script associated with the one focal state; wherein said steps are implemented in either: computer hardware configured to perform said steps, or computer software embodied in a non-transitory, tangible, computer-readable storage medium. 5. The method as claimed in claim 1 , wherein providing multiple focal states includes: analyzing monitored screen contents and user actions to determine repeated functional events; and automatically defining a focal state as a sequence of the monitored functional events.
| 0.5 |
8,676,730 | 1 | 4 |
1. A method for at least one processing device to provide at least one sentiment classifier based on a plurality of features, the method comprising: extracting, by the at least one processing device, a plurality of features from training data comprising a plurality of units by: labeling at least one element of each unit of the training data according to a plurality of part-of-speech (PoS) tags to provide PoS features; checking at least one element of each unit of the training data according to a plurality of dictionaries to provide dictionary features; detecting a plurality of negation/inversion elements in the training data to provide a plurality of negation/inversion features based on a negation dictionary and an inversion dictionary of the plurality of dictionaries; labeling at least one element after each of the plurality of negation/inversion features to provide negated/inverted elements features based on at least one of a propagation window or punctuation in the training data; determining, by the at least one processing device, a value for each feature of the plurality of features based on a frequency at which each feature occurs in the training data; labeling, by the at least one processing device, each unit of the training data according to a plurality of sentiment classes to provide labeled training data; and providing, by the at least one processing device, the at least one sentiment classifier based on the value of each feature of the plurality of features and the labeled training data using a supervised classification technique.
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1. A method for at least one processing device to provide at least one sentiment classifier based on a plurality of features, the method comprising: extracting, by the at least one processing device, a plurality of features from training data comprising a plurality of units by: labeling at least one element of each unit of the training data according to a plurality of part-of-speech (PoS) tags to provide PoS features; checking at least one element of each unit of the training data according to a plurality of dictionaries to provide dictionary features; detecting a plurality of negation/inversion elements in the training data to provide a plurality of negation/inversion features based on a negation dictionary and an inversion dictionary of the plurality of dictionaries; labeling at least one element after each of the plurality of negation/inversion features to provide negated/inverted elements features based on at least one of a propagation window or punctuation in the training data; determining, by the at least one processing device, a value for each feature of the plurality of features based on a frequency at which each feature occurs in the training data; labeling, by the at least one processing device, each unit of the training data according to a plurality of sentiment classes to provide labeled training data; and providing, by the at least one processing device, the at least one sentiment classifier based on the value of each feature of the plurality of features and the labeled training data using a supervised classification technique. 4. The method of claim 1 , wherein labeling each unit of the training data comprises: applying an existing sentiment classifier to each unit of the training data to provide a sentiment score for each unit of the training data; and labeling each unit of the training data with one of the plurality of sentiment classes based on the sentiment score.
| 0.672642 |
9,117,125 | 8 | 14 |
8. A method for processing a handwritten document, comprising: displaying on a screen a handwritten document including a plurality of strokes described by handwriting; transmitting to a system a handwritten part corresponding to a select range on the screen; receiving from the system a reshaping result corresponding to the handwritten part; and changing a line kind or a color of a plurality of strokes included in the handwritten part; and displaying the reshaping result in a manner to at least partially overlap with a display area of the handwritten part.
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8. A method for processing a handwritten document, comprising: displaying on a screen a handwritten document including a plurality of strokes described by handwriting; transmitting to a system a handwritten part corresponding to a select range on the screen; receiving from the system a reshaping result corresponding to the handwritten part; and changing a line kind or a color of a plurality of strokes included in the handwritten part; and displaying the reshaping result in a manner to at least partially overlap with a display area of the handwritten part. 14. The method of claim 8 , wherein the transmitting comprises transmitting to the system a plurality of stroke data corresponding to a plurality of strokes included in the handwritten part.
| 0.821429 |
8,452,653 | 25 | 33 |
25. A computer-readable medium having instructions stored therein, which when executed cause a computer to perform a set of operations comprising: recording activity data by a portable user device on a transportable recordable medium (TRM) coupled to the user device, the recording being in response to user participation in an activity; transferring the activity data at the user device to a profiler across a network, the profiler storing a user controlled profile including the activity data; displaying the activity data and user selectable options for the user controlled profile through the user device; and initiating a monetary transaction through the user device based on one of the plurality of recommendations; and receiving a plurality of recommendations by the user device inferred from the activity data by a user selected profiling program.
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25. A computer-readable medium having instructions stored therein, which when executed cause a computer to perform a set of operations comprising: recording activity data by a portable user device on a transportable recordable medium (TRM) coupled to the user device, the recording being in response to user participation in an activity; transferring the activity data at the user device to a profiler across a network, the profiler storing a user controlled profile including the activity data; displaying the activity data and user selectable options for the user controlled profile through the user device; and initiating a monetary transaction through the user device based on one of the plurality of recommendations; and receiving a plurality of recommendations by the user device inferred from the activity data by a user selected profiling program. 33. The computer-readable medium of claim 25 having further instructions stored therein which when executed cause the computer to perform a further set of operations comprising: invoking a monetary transaction; presenting the TRM when a payment is requested; authenticating access rights to the TRM; selecting an account derived from a financial data on the TRM; and consenting to the monetary transaction.
| 0.5 |
8,353,932 | 2 | 3 |
2. The improvement of claim 1 further comprising: a) at least one inner recessed relief surface formed in the compression insert, the recessed relief surface receiving material flow of the longitudinal connecting member.
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2. The improvement of claim 1 further comprising: a) at least one inner recessed relief surface formed in the compression insert, the recessed relief surface receiving material flow of the longitudinal connecting member. 3. The improvement of claim 2 wherein a) the at least one recessed relief surface is formed in one of a pair of straight planar side surfaces disposed adjacent to an arm top surface.
| 0.5 |
7,779,355 | 13 | 14 |
13. The method of claim 1 , wherein the feature comprises an identifier to a location in the recorded information, wherein the information in the recorded information corresponding to the feature is determined using the identifier.
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13. The method of claim 1 , wherein the feature comprises an identifier to a location in the recorded information, wherein the information in the recorded information corresponding to the feature is determined using the identifier. 14. The method of claim 13 , wherein the identifier comprises at least one of a barcode and signature information.
| 0.5 |
9,207,860 | 31 | 38 |
31. A non-transitory computer-readable storage medium having computer-readable code executable by at least one processor of a portable electronic device to perform operations comprising: receiving a swipe gesture from an initial touch location on a touch-sensitive display of an electronic device; determining touch attributes for the swipe gesture, the touch attributes comprising a touch location of the swipe gesture; performing a first lock function when the swipe gesture progresses to a first touch location on the touch-sensitive display that is beyond a first threshold from the initial touch location, wherein the first lock function locks a first set of functionalities of the electronic device; and performing a second lock function when the swipe gesture progresses to a second touch location on the touch-sensitive display that is beyond a second threshold from the initial touch location, wherein the second lock function locks a second, different set of functionalities of the electronic device and the second threshold is beyond the first threshold.
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31. A non-transitory computer-readable storage medium having computer-readable code executable by at least one processor of a portable electronic device to perform operations comprising: receiving a swipe gesture from an initial touch location on a touch-sensitive display of an electronic device; determining touch attributes for the swipe gesture, the touch attributes comprising a touch location of the swipe gesture; performing a first lock function when the swipe gesture progresses to a first touch location on the touch-sensitive display that is beyond a first threshold from the initial touch location, wherein the first lock function locks a first set of functionalities of the electronic device; and performing a second lock function when the swipe gesture progresses to a second touch location on the touch-sensitive display that is beyond a second threshold from the initial touch location, wherein the second lock function locks a second, different set of functionalities of the electronic device and the second threshold is beyond the first threshold. 38. The non-transitory computer-readable storage medium of claim 31 , wherein detecting a release of the swipe gesture activates the first lock function and the second lock function.
| 0.794118 |
8,200,651 | 3 | 4 |
3. The method of claim 2 wherein the network is the World Wide Web.
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3. The method of claim 2 wherein the network is the World Wide Web. 4. The method of claim 3 wherein at least one of the plurality of dictionary databases is an online dictionary.
| 0.5 |
9,792,361 | 16 | 17 |
16. A computer implemented method for presenting social network-provided outputs to a mobile electronic device dependent on a location, in response to the mobile electronic device user's input, comprising: receiving information from the mobile electronic device user defining the user input through a hardware data input port; automatically determining a location of the mobile electronic device by an automated hardware geospatial positioning system; automatically defining a user request dependent on the user input and metadata associated with the received information from the mobile electronic device user with at least one automated hardware processor, comprising at least the automatically determined location of the mobile electronic device; automatically transmitting the user request to an automated social network database comprising a plurality of records having location information, comprising a database of roadway condition records comprising a time and location associated with roadway conditions; automatically receiving location-dependent social network information from the social network database through a hardware interface, selectively dependent on the transmitted user request; and automatically communicating a message dependent on the received location-dependent social network information for creating a new record in the social network database comprising a time and location of a respective roadway condition; automatically ranking the received social network information according to at least one social network ranking factor with at least one automated processor; and presenting the ranked received social network information through an automated hardware user machine interface.
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16. A computer implemented method for presenting social network-provided outputs to a mobile electronic device dependent on a location, in response to the mobile electronic device user's input, comprising: receiving information from the mobile electronic device user defining the user input through a hardware data input port; automatically determining a location of the mobile electronic device by an automated hardware geospatial positioning system; automatically defining a user request dependent on the user input and metadata associated with the received information from the mobile electronic device user with at least one automated hardware processor, comprising at least the automatically determined location of the mobile electronic device; automatically transmitting the user request to an automated social network database comprising a plurality of records having location information, comprising a database of roadway condition records comprising a time and location associated with roadway conditions; automatically receiving location-dependent social network information from the social network database through a hardware interface, selectively dependent on the transmitted user request; and automatically communicating a message dependent on the received location-dependent social network information for creating a new record in the social network database comprising a time and location of a respective roadway condition; automatically ranking the received social network information according to at least one social network ranking factor with at least one automated processor; and presenting the ranked received social network information through an automated hardware user machine interface. 17. The method according to claim 16 , wherein: the mobile electronic device comprises a cell phone; and the received social network information is ranked according to a combination of ranking factors comprising a proximity of a location associated with a respective record and the location of the mobile electronic device.
| 0.5 |
9,646,050 | 8 | 11 |
8. A system for interpreting queries, the system comprising: a storage device for storing entity information, wherein the entity information is derived from metadata associated with a search domain; and a processor that is configured to: receive a search query that includes a plurality of search terms; determine, for at least a portion of the plurality of search terms, whether a search term corresponds to an entity name, wherein the entity name is associated with a plurality of entity types; determine, for the search term and each of the plurality of entity types, an entity score that indicates relatedness of the entity name to a corresponding entity type; remove at least one of the entity names based on the entity score to generate a remaining portion of entity names; and perform a search with the remaining portion of entity names, wherein each entity name in the remaining portion of entity names is searched corresponding to the associated entity type.
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8. A system for interpreting queries, the system comprising: a storage device for storing entity information, wherein the entity information is derived from metadata associated with a search domain; and a processor that is configured to: receive a search query that includes a plurality of search terms; determine, for at least a portion of the plurality of search terms, whether a search term corresponds to an entity name, wherein the entity name is associated with a plurality of entity types; determine, for the search term and each of the plurality of entity types, an entity score that indicates relatedness of the entity name to a corresponding entity type; remove at least one of the entity names based on the entity score to generate a remaining portion of entity names; and perform a search with the remaining portion of entity names, wherein each entity name in the remaining portion of entity names is searched corresponding to the associated entity type. 11. The system of claim 8 , wherein the processor is further configured to: retrieve metadata corresponding to the search domain; extract entity names based on the retrieved metadata; determine, for each of the entity names, the entity type and the entity score associated with the entity name; and generate an entity table that includes, for each of the entity names, the entity name and the associated entity type and entity score.
| 0.5 |
8,886,536 | 44 | 45 |
44. A computer-implemented method of providing promotional content related to one or more natural language utterances and/or responses, the method being implemented by a computer system that includes one or more physical processors executing one or more computer program instructions which, when executed, perform the method, the method comprising: receiving, at the one or more physical processors, a first natural language utterance; providing, by the one or more physical processors, a response to the first natural language utterance; receiving, at the one or more physical processors, a second natural language utterance relating to the first natural language utterance; identifying, by the one or more physical processors, requests associated with the second natural language utterance, wherein the requests include a first request to be processed by a first device associated with a user and a second request to be processed by a second device associated with the user; determining, by the one or more physical processors, promotional content that relates to one or more of the first request or the second request; and presenting, by the one or more physical processors, the promotional content to the user.
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44. A computer-implemented method of providing promotional content related to one or more natural language utterances and/or responses, the method being implemented by a computer system that includes one or more physical processors executing one or more computer program instructions which, when executed, perform the method, the method comprising: receiving, at the one or more physical processors, a first natural language utterance; providing, by the one or more physical processors, a response to the first natural language utterance; receiving, at the one or more physical processors, a second natural language utterance relating to the first natural language utterance; identifying, by the one or more physical processors, requests associated with the second natural language utterance, wherein the requests include a first request to be processed by a first device associated with a user and a second request to be processed by a second device associated with the user; determining, by the one or more physical processors, promotional content that relates to one or more of the first request or the second request; and presenting, by the one or more physical processors, the promotional content to the user. 45. The method of claim 44 , wherein a first device type of the first device includes one or more of a mobile phone, a navigation device, or a media player device, and wherein the second device is of a device type different than the first device type.
| 0.677378 |
8,260,737 | 8 | 9 |
8. A computer-implemented method for generating an enhancement of an ontology, the method comprising: identifying a service specification being represented by a class of the ontology, the service specification describing a capability of a service; computing a set of instances that are specified by the service specification and by a requirement specification describing at least one characteristic for selecting the service that is independent of the class of the request ontology; enhancing the ontology with a generated class representing the computed set of instances specified by the service specification and by the requirement specification, wherein the generated class represents a condition part of a selection rule based on the at least one characteristic for selecting the service; and generating an action part of the selection rule using an identification of the service so that a relation between the generated class and the service is represented by the selection rule having the condition part and the action part, wherein the condition part of the selection rule specifies a condition based on the at least one characteristic for selecting the service and the action part of the selection rule specifies an action to be executed when the condition of the condition part is fulfilled.
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8. A computer-implemented method for generating an enhancement of an ontology, the method comprising: identifying a service specification being represented by a class of the ontology, the service specification describing a capability of a service; computing a set of instances that are specified by the service specification and by a requirement specification describing at least one characteristic for selecting the service that is independent of the class of the request ontology; enhancing the ontology with a generated class representing the computed set of instances specified by the service specification and by the requirement specification, wherein the generated class represents a condition part of a selection rule based on the at least one characteristic for selecting the service; and generating an action part of the selection rule using an identification of the service so that a relation between the generated class and the service is represented by the selection rule having the condition part and the action part, wherein the condition part of the selection rule specifies a condition based on the at least one characteristic for selecting the service and the action part of the selection rule specifies an action to be executed when the condition of the condition part is fulfilled. 9. The method of claim 8 , further comprising computing a priority value for the selection rule according to a subset rule, the subset rule including that a first condition part that specifies a first set of instances has a higher priority than a second condition part that specifies a second set of instances in case that the first set of instances is a subset of the second condition part.
| 0.5 |
8,229,737 | 1 | 7 |
1. A method, comprising: accessing a name; dividing, using a computer including a processor, the name into a series of first n-grams; forming multiple concatenated second n-grams by concatenating pairs of the first n-grams; for each of multiple groups, for each of the second n-grams, determining the term frequency-group frequency score using equation:
((0.5+(0.5*(number of times the second n-gram occurs in a group))/(number of times a most common n-gram occurs in the group))*((number of times the second n-gram occurs in the group)/(number of times the second n-gram occurs in the multiple groups)); for each of the multiple groups, summing up the term frequency-group frequency scores for each second n-gram for that group; and determining a likelihood that the name belongs to one group of the multiple groups based on the summed scores, wherein a largest summed score indicates a greater likelihood that the name belongs to the one group.
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1. A method, comprising: accessing a name; dividing, using a computer including a processor, the name into a series of first n-grams; forming multiple concatenated second n-grams by concatenating pairs of the first n-grams; for each of multiple groups, for each of the second n-grams, determining the term frequency-group frequency score using equation:
((0.5+(0.5*(number of times the second n-gram occurs in a group))/(number of times a most common n-gram occurs in the group))*((number of times the second n-gram occurs in the group)/(number of times the second n-gram occurs in the multiple groups)); for each of the multiple groups, summing up the term frequency-group frequency scores for each second n-gram for that group; and determining a likelihood that the name belongs to one group of the multiple groups based on the summed scores, wherein a largest summed score indicates a greater likelihood that the name belongs to the one group. 7. The method of claim 1 , wherein the name is a first name and further comprising: determining that the first name occupies a given name field of a larger name; determining that a second name occupies a second given name field of the larger name, wherein the first name and the second name form a complete given name; accessing the second name; determining a likelihood that the second name belongs to the first group; and determining a likelihood that the complete given name belongs to the first group by averaging the likelihood that the second name belongs to the first group and the likelihood that the complete name belongs to the first group.
| 0.675972 |
9,507,792 | 19 | 20 |
19. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including: detecting a set of assessment content objects for processing, each assessment content object of the set of assessment content objects including content-object data arranged according to a spatial arrangement, each assessment content object of the set of assessment content objects being associated with an identifier corresponding to a student; for each assessment content object of the set of assessment content objects: determining that the assessment content object corresponds to an identifier of a template for a particular assessment, the template identifying a segment-position specification that at least partly defines an area in which an answer for a question is to have been provided; identifying a portion of the content-object data as corresponding to an answer area for the question based on the segment-position specification and the spatial arrangement; extracting the portion of the content-object data; evaluating the portion of the content-object data to identify an answer indicated in the portion of the content-object data; determining an evaluation quality metric reflecting a confidence in the identification of the answer; and determining whether a quality criterion is satisfied based on the evaluation quality metric; identifying multiple portions of content-object data extracted from multiple assessment content objects from the set of assessment content objects, each portion of the multiple portions being associated with a determination that the quality criterion is not satisfied; facilitating a presentation that includes: the multiple portions of content-object data; and a tool that receives external input identifying an answer indicated in one or more portions of the multiple portions; receiving an input corresponding to an identification of an answer indicated in a portion of the one or more portions; and upon receiving the input, storing the answer in association with the identifier of the student associated with the assessment content object from which the portion was extracted.
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19. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including: detecting a set of assessment content objects for processing, each assessment content object of the set of assessment content objects including content-object data arranged according to a spatial arrangement, each assessment content object of the set of assessment content objects being associated with an identifier corresponding to a student; for each assessment content object of the set of assessment content objects: determining that the assessment content object corresponds to an identifier of a template for a particular assessment, the template identifying a segment-position specification that at least partly defines an area in which an answer for a question is to have been provided; identifying a portion of the content-object data as corresponding to an answer area for the question based on the segment-position specification and the spatial arrangement; extracting the portion of the content-object data; evaluating the portion of the content-object data to identify an answer indicated in the portion of the content-object data; determining an evaluation quality metric reflecting a confidence in the identification of the answer; and determining whether a quality criterion is satisfied based on the evaluation quality metric; identifying multiple portions of content-object data extracted from multiple assessment content objects from the set of assessment content objects, each portion of the multiple portions being associated with a determination that the quality criterion is not satisfied; facilitating a presentation that includes: the multiple portions of content-object data; and a tool that receives external input identifying an answer indicated in one or more portions of the multiple portions; receiving an input corresponding to an identification of an answer indicated in a portion of the one or more portions; and upon receiving the input, storing the answer in association with the identifier of the student associated with the assessment content object from which the portion was extracted. 20. The computer-program product as recited in claim 19 , wherein evaluating the portion of the content-object data includes determining a statistic based on a set of pixel intensities and comparing the statistic to a threshold.
| 0.5 |
9,110,874 | 2 | 3 |
2. The document conversion apparatus according to claim 1 , wherein the at least one computer program further comprises program code that, when executed by the computer processor, implements: a processing unit configured to receive a structured document including a first document element and a second document element, the processing unit configured to process a template; a converting unit configured to perform a first binary conversion algorithm on the first document element and to perform a second binary conversion algorithm on the second document element, wherein the second binary conversion algorithm is different from the first binary conversion algorithm; and an output unit configured to output the structured document in the binary format.
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2. The document conversion apparatus according to claim 1 , wherein the at least one computer program further comprises program code that, when executed by the computer processor, implements: a processing unit configured to receive a structured document including a first document element and a second document element, the processing unit configured to process a template; a converting unit configured to perform a first binary conversion algorithm on the first document element and to perform a second binary conversion algorithm on the second document element, wherein the second binary conversion algorithm is different from the first binary conversion algorithm; and an output unit configured to output the structured document in the binary format. 3. The document conversion apparatus of claim 2 , wherein the template includes a processing command for outputting at least one of the document elements as a part of the structured document.
| 0.76125 |
8,289,281 | 2 | 5 |
2. The portable computer according to claim 1 , further comprising: a saving unit to save a table of input key values representing the key array corresponding to the plurality of languages.
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2. The portable computer according to claim 1 , further comprising: a saving unit to save a table of input key values representing the key array corresponding to the plurality of languages. 5. The portable computer according to claim 2 , wherein the control unit generates a message to notify a user of the portable computer that the predetermined language to which the OS operates has a plurality of characters different than characters labeled on the input keys.
| 0.669082 |
10,162,882 | 9 | 14 |
9. A system for automatically linking text to concepts in a knowledge base, the system comprising: a memory having computer readable computer instructions; and one or more processors for executing the computer readable instructions, the computer readable instructions including: receiving a plurality of text strings; building a conceptual index that links the text strings to the knowledge base, the building comprising for each of the text strings: selecting a plurality of data sources that correspond to at least a subset of the concepts in the knowledge base, the selecting based on contents of the text string; calculating, for each of the selected data sources, a probability that the text string is output by a language model built using the selected data source; calculating a probability that the text string is output by a generic language model that is not related to any particular concept in the knowledge base; calculating link confidence scores for each of the at least a subset of the concepts based on a differential analysis of the probabilities; and creating an entry in the conceptual index that includes a link between the text string and one of the concepts in the knowledge base, the creating based at least in part on a link confidence score of the concept being more than a first threshold value away from a prescribed threshold; generating a conceptual inverted index based on entries in the conceptual index, each entry of the conceptual inverted index corresponding to a different one of the concepts in the knowledge base and comprising pointers to at least a subset of text strings of the plurality of text strings linked to the concept in the conceptual index; receiving a query from an agent external to the computer system, the query specifying a concept in the knowledge base; processing the query, the processing comprising searching the conceptual inverted index for the concept specified in the query and returning a pointer to a text string in an entry of the conceptual inverted index corresponding to the concept; and returning a set of documents to the external agent through the use of the conceptual inverted index, based on the received query.
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9. A system for automatically linking text to concepts in a knowledge base, the system comprising: a memory having computer readable computer instructions; and one or more processors for executing the computer readable instructions, the computer readable instructions including: receiving a plurality of text strings; building a conceptual index that links the text strings to the knowledge base, the building comprising for each of the text strings: selecting a plurality of data sources that correspond to at least a subset of the concepts in the knowledge base, the selecting based on contents of the text string; calculating, for each of the selected data sources, a probability that the text string is output by a language model built using the selected data source; calculating a probability that the text string is output by a generic language model that is not related to any particular concept in the knowledge base; calculating link confidence scores for each of the at least a subset of the concepts based on a differential analysis of the probabilities; and creating an entry in the conceptual index that includes a link between the text string and one of the concepts in the knowledge base, the creating based at least in part on a link confidence score of the concept being more than a first threshold value away from a prescribed threshold; generating a conceptual inverted index based on entries in the conceptual index, each entry of the conceptual inverted index corresponding to a different one of the concepts in the knowledge base and comprising pointers to at least a subset of text strings of the plurality of text strings linked to the concept in the conceptual index; receiving a query from an agent external to the computer system, the query specifying a concept in the knowledge base; processing the query, the processing comprising searching the conceptual inverted index for the concept specified in the query and returning a pointer to a text string in an entry of the conceptual inverted index corresponding to the concept; and returning a set of documents to the external agent through the use of the conceptual inverted index, based on the received query. 14. The system of claim 9 , wherein each of the text strings have a version number and the method further comprises periodically, by a garbage collection mechanism, deleting links in the conceptual index to text strings having invalid version numbers.
| 0.728942 |
8,694,444 | 18 | 19 |
18. A non-transitory storage medium storing instructions executable by an electronic data processing device to perform a method including (i) learning a set of multi-task decision trees (MT-DT's) for a set of tasks including at least two tasks using different subsets of a training set wherein the learning of each MT-DT includes learning decision rules for nodes of the MT-DT that maximize an aggregate information gain (IG) that aggregates single-task IG values for tasks of the set of tasks and (ii) constructing a multi-task (MT) predictor as a weighted combination of outputs of the learned set of MT-DT's.
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18. A non-transitory storage medium storing instructions executable by an electronic data processing device to perform a method including (i) learning a set of multi-task decision trees (MT-DT's) for a set of tasks including at least two tasks using different subsets of a training set wherein the learning of each MT-DT includes learning decision rules for nodes of the MT-DT that maximize an aggregate information gain (IG) that aggregates single-task IG values for tasks of the set of tasks and (ii) constructing a multi-task (MT) predictor as a weighted combination of outputs of the learned set of MT-DT's. 19. The non-transitory storage medium of claim 18 wherein at least one task of the set of tasks is a multi-class task.
| 0.677596 |
5,495,558 | 4 | 5 |
4. The fuzzy inference development system of claim 1, wherein said specifications input data comprise at least one of the following fields: a capacity of a work area field in a fuzzy inference operations memory; a capacity of an inference engine memory field; a computing speed field required by each of said plurality of inference engine modules; a sequence of processing data field to be used for anticipated input data in a fuzzy inference; and a number of degrees of freedom associated with a shape of a membership function field established in an antecedent of a fuzzy inference rule.
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4. The fuzzy inference development system of claim 1, wherein said specifications input data comprise at least one of the following fields: a capacity of a work area field in a fuzzy inference operations memory; a capacity of an inference engine memory field; a computing speed field required by each of said plurality of inference engine modules; a sequence of processing data field to be used for anticipated input data in a fuzzy inference; and a number of degrees of freedom associated with a shape of a membership function field established in an antecedent of a fuzzy inference rule. 5. The fuzzy inference development system of claim 4, wherein said specifications data further comprises a data field concerning a type of CPU to be used by said development system user and an operating clock frequency.
| 0.68892 |
7,792,826 | 13 | 20 |
13. A method of providing a ranked search result, the method comprising: generating, via a search engine, a set of search results, using an index corpus, wherein the search results comprise one or more matching subject items, and wherein the matching subject items match a given set of one or more search terms; determining, using a taxonomy, one or more parent subject items of the one or more matching subject items, wherein the taxonomy includes at least one of topics and subjects, and wherein the index corpus is related to the at least one of the topics and the subjects of the taxonomy; statistically ranking, by a computer, relevant subject items including the matching subject items and the parent subject items, wherein statistically ranking includes determining a hierarchy score for each of the relevant subject items based on (1) hierarchy relationships between the each of the relevant subject items and other subject items, and (2) a number of references to one of the matching subject items in original data being searched, calculating ranking scores of the relevant subject items based on the hierarchy scores, and ranking the relevant subject items based on the ranking scores; and organizing the relevant subject items based on the ranking of each relevant subject item.
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13. A method of providing a ranked search result, the method comprising: generating, via a search engine, a set of search results, using an index corpus, wherein the search results comprise one or more matching subject items, and wherein the matching subject items match a given set of one or more search terms; determining, using a taxonomy, one or more parent subject items of the one or more matching subject items, wherein the taxonomy includes at least one of topics and subjects, and wherein the index corpus is related to the at least one of the topics and the subjects of the taxonomy; statistically ranking, by a computer, relevant subject items including the matching subject items and the parent subject items, wherein statistically ranking includes determining a hierarchy score for each of the relevant subject items based on (1) hierarchy relationships between the each of the relevant subject items and other subject items, and (2) a number of references to one of the matching subject items in original data being searched, calculating ranking scores of the relevant subject items based on the hierarchy scores, and ranking the relevant subject items based on the ranking scores; and organizing the relevant subject items based on the ranking of each relevant subject item. 20. The method as claimed in claim 13 , wherein the parent determining comprises receiving a search result from a full-text search engine having the taxonomy.
| 0.705224 |
8,738,673 | 17 | 18 |
17. A computer program product comprising a tangible computer readable storage medium including a computer readable program, wherein the computer readable program when executed by a processor on a computer causes the computer to perform: in response to receiving a new document, generating an assigned-doc-ID for the new document; identifying, for the assigned-doc-ID, a virtual-index-epoch from a virtual-index-epoch map that includes virtual-index-epochs that are each assigned a range of assign-doc-IDs; applying a first function to a virtual-index-epoch value of the identified virtual-index-epoch to identify a logical partition; applying a second function to the identified logical partition to identify a physical partition; and placing the new document into the identified physical partition associated with the identified virtual-index-epoch.
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17. A computer program product comprising a tangible computer readable storage medium including a computer readable program, wherein the computer readable program when executed by a processor on a computer causes the computer to perform: in response to receiving a new document, generating an assigned-doc-ID for the new document; identifying, for the assigned-doc-ID, a virtual-index-epoch from a virtual-index-epoch map that includes virtual-index-epochs that are each assigned a range of assign-doc-IDs; applying a first function to a virtual-index-epoch value of the identified virtual-index-epoch to identify a logical partition; applying a second function to the identified logical partition to identify a physical partition; and placing the new document into the identified physical partition associated with the identified virtual-index-epoch. 18. The computer program product of claim 17 , wherein the computer readable program when executed by the processor on the computer causes the computer to perform: maintaining a persistent, transactionally recoverable structure that stores the virtual-index-epoch map.
| 0.578616 |
8,010,890 | 7 | 11 |
7. A system for creating and providing a markup language document, comprising: a receiving computer including at least one processor and computer readable memory; a server computer including at least one processor and computer readable memory; program code for loading said document into said computer readable memory of said server computer, said document containing conventional tags and at least one custom tag, said custom tag associated with machine-executable instructions resident on said receiving computer, wherein said receiving computer is in communication with a network; program code for providing said document from said server computer to said receiving computer over said network; and program code for identifying said custom tag, wherein said identifying includes scanning a document object model (DOM) representation of said document in said receiving computer for the presence of said custom tag, and inserting, by a Web browser executing on said client computer and prior to rendering of said document on a display device, said machine-executable instructions into said document at a location associated with said custom tag, wherein said inserting includes modifying said DOM representation of said document to replace said custom tag with said machine-executable instructions, wherein said executable instructions are for rendering a tree control display.
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7. A system for creating and providing a markup language document, comprising: a receiving computer including at least one processor and computer readable memory; a server computer including at least one processor and computer readable memory; program code for loading said document into said computer readable memory of said server computer, said document containing conventional tags and at least one custom tag, said custom tag associated with machine-executable instructions resident on said receiving computer, wherein said receiving computer is in communication with a network; program code for providing said document from said server computer to said receiving computer over said network; and program code for identifying said custom tag, wherein said identifying includes scanning a document object model (DOM) representation of said document in said receiving computer for the presence of said custom tag, and inserting, by a Web browser executing on said client computer and prior to rendering of said document on a display device, said machine-executable instructions into said document at a location associated with said custom tag, wherein said inserting includes modifying said DOM representation of said document to replace said custom tag with said machine-executable instructions, wherein said executable instructions are for rendering a tree control display. 11. The system of claim 7 wherein said receiving computer renders said document on a display device using said Web browser.
| 0.5 |
9,235,631 | 4 | 5 |
4. The method of claim 1 , wherein the importing the at least a portion of the functional logic further comprises embedding the at least a portion of the functional logic in at least one document.
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4. The method of claim 1 , wherein the importing the at least a portion of the functional logic further comprises embedding the at least a portion of the functional logic in at least one document. 5. The method of claim 4 , wherein the at least one document utilizes a markup language.
| 0.5 |
8,005,847 | 18 | 19 |
18. The system of claim 17 , wherein the file access module is to identify a related file name that corresponds to a related file included in the package and that matches the second pattern string.
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18. The system of claim 17 , wherein the file access module is to identify a related file name that corresponds to a related file included in the package and that matches the second pattern string. 19. The system of claim 18 , wherein the relationship definition records a relationship between the main file and the related file separately from the main file and the related file, and wherein the relationship definition is to facilitate associating information stored in the related file to the main file without altering the main file.
| 0.5 |
8,280,946 | 7 | 9 |
7. A non-transitory computer-readable medium, comprising: a plurality of computer-executable instructions, which, when executed by one or more processors, cause the one or more processors to: receive a document requested by a browser program on a client device; analyze the document to generate a list of domain names associated with a plurality of links, within the document, that are selectable via the browser program, the instructions, which cause the one or more processors to analyze the document further causing the one or more processors to: determine, based on historical data, for which links, of the plurality of links within the document, to include associated domain names in the list of names, the historical data being based on at least one of: a quantity of times that a particular link, of the plurality of links, has been accessed, or an amount of time spent by users accessing documents linked to by the plurality of links the list of domain names being ordered based on a relevance of documents, associated with the domain names, to the document; transmit the list to the client device, the list permitting the client device to perform domain name system (DNS) lookups for the domain names in the list prior to receiving a selection, at the client device, of any of the plurality of links within the document; transmit the document to the client device; and perform, in an order indicated by the list, one or more DNS lookups for one or more of the domain names in the list without receiving a selection of any of the plurality of links within the document.
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7. A non-transitory computer-readable medium, comprising: a plurality of computer-executable instructions, which, when executed by one or more processors, cause the one or more processors to: receive a document requested by a browser program on a client device; analyze the document to generate a list of domain names associated with a plurality of links, within the document, that are selectable via the browser program, the instructions, which cause the one or more processors to analyze the document further causing the one or more processors to: determine, based on historical data, for which links, of the plurality of links within the document, to include associated domain names in the list of names, the historical data being based on at least one of: a quantity of times that a particular link, of the plurality of links, has been accessed, or an amount of time spent by users accessing documents linked to by the plurality of links the list of domain names being ordered based on a relevance of documents, associated with the domain names, to the document; transmit the list to the client device, the list permitting the client device to perform domain name system (DNS) lookups for the domain names in the list prior to receiving a selection, at the client device, of any of the plurality of links within the document; transmit the document to the client device; and perform, in an order indicated by the list, one or more DNS lookups for one or more of the domain names in the list without receiving a selection of any of the plurality of links within the document. 9. The computer-readable medium of claim 7 , where the plurality of computer-executable instructions, which cause the one or more processors to transmit the list of domain names, further cause the one or more processors to: transmit the list of domain names in a hyper-text transfer protocol (HTTP) response header.
| 0.602273 |
10,073,928 | 7 | 8 |
7. A device for analysis of shape optimization, for optimizing a part of a structural body model having a movable portion, by combining a multi-body dynamics analysis and an optimization analysis and using two-dimensional elements or three-dimensional elements, the device comprising a display device and a computer with a central processing unit and memory that: sets, as a design space, a portion to be optimized in the movable portion; generates, in the set design space, an optimization block model formed of three-dimensional elements and is subjected to analysis processing of optimization; connects the generated optimization block model with the structural body model including the movable portion; sets a material property for the optimization block model; sets an optimization analysis condition to determine an optimum shape of the optimization block model; sets a multi-body dynamics analysis condition including a centrifugal force, a reaction force and an inertial force to perform multi-body dynamics analysis on the structural body model including the movable portion with which the optimization block model has been connected; executes, based on the set multi-body dynamics analysis condition, the multi-body dynamics analysis on the structural body model including the movable portion incorporating the optimization block model; executes, based on the set optimization analysis condition, the optimization analysis; and finds the optimum shape of the optimization block model, and utilizes the analysis of shape optimization for configuring optimization of the movable portion of the structural body configured of a thin sheet; wherein the computer generates the optimization block model by: setting nodes in a portion connected with the two-dimensional elements or three-dimensional elements forming the structural body model; and stacking the three-dimensional elements along a plane including the nodes set in the connected portion, and the display device displays the structural body model including the moveable portion based on the optimum shape.
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7. A device for analysis of shape optimization, for optimizing a part of a structural body model having a movable portion, by combining a multi-body dynamics analysis and an optimization analysis and using two-dimensional elements or three-dimensional elements, the device comprising a display device and a computer with a central processing unit and memory that: sets, as a design space, a portion to be optimized in the movable portion; generates, in the set design space, an optimization block model formed of three-dimensional elements and is subjected to analysis processing of optimization; connects the generated optimization block model with the structural body model including the movable portion; sets a material property for the optimization block model; sets an optimization analysis condition to determine an optimum shape of the optimization block model; sets a multi-body dynamics analysis condition including a centrifugal force, a reaction force and an inertial force to perform multi-body dynamics analysis on the structural body model including the movable portion with which the optimization block model has been connected; executes, based on the set multi-body dynamics analysis condition, the multi-body dynamics analysis on the structural body model including the movable portion incorporating the optimization block model; executes, based on the set optimization analysis condition, the optimization analysis; and finds the optimum shape of the optimization block model, and utilizes the analysis of shape optimization for configuring optimization of the movable portion of the structural body configured of a thin sheet; wherein the computer generates the optimization block model by: setting nodes in a portion connected with the two-dimensional elements or three-dimensional elements forming the structural body model; and stacking the three-dimensional elements along a plane including the nodes set in the connected portion, and the display device displays the structural body model including the moveable portion based on the optimum shape. 8. The device for analysis of shape optimization according to claim 7 , wherein the computer sets a load or displacement obtained as a result of performing multi-body dynamics analysis on the structural body model beforehand.
| 0.706266 |
8,229,909 | 8 | 12 |
8. A computer-implemented method, comprising: receiving a search request from a user; determining a multi-dimensional point of a user profile within a user profile context, the user profile context storing information pertaining to the user; determining a multi-dimensional point for a document within a document context, the document context storing a plurality of documents to be searched; generating scores for the plurality of documents to be searched; measuring a distance between the multi-dimensional point of the user profile and the multi-dimensional point for the document; and determining a plurality of search hits based on the measuring and the generated scores.
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8. A computer-implemented method, comprising: receiving a search request from a user; determining a multi-dimensional point of a user profile within a user profile context, the user profile context storing information pertaining to the user; determining a multi-dimensional point for a document within a document context, the document context storing a plurality of documents to be searched; generating scores for the plurality of documents to be searched; measuring a distance between the multi-dimensional point of the user profile and the multi-dimensional point for the document; and determining a plurality of search hits based on the measuring and the generated scores. 12. The computer-implemented method of claim 8 , wherein determining the multi-dimensional point for the document comprises retrieving a stored multi-dimensional point for the document.
| 0.5 |
9,620,086 | 1 | 4 |
1. A method comprising: identifying, by a computer system comprising one or more processors, a font in which text is to be rendered on a display of an electronic device; identifying a first glyph associated with the font; determining that the first glyph comprises a first number of curved portions forming the first glyph; identifying a second glyph associated with the font; determining that the second glyph comprises a second number of curved portions forming the second glyph; determining that the first number of curved portions and the second number of curved portions are within a first range of curved portion values, wherein the first range of curved portion values corresponds to a first glyph category; rendering the first glyph on the display; rendering the second glyph on the display; receiving a request to increase a first contrast of the first glyph; increasing the first contrast by reducing a first grayscale value of the first glyph by an incremental amount, wherein the incremental amount is a numerical value; automatically increasing a second contrast of the second glyph by reducing a second grayscale value of the second glyph by the incremental amount; assigning the reduced first grayscale value to the first glyph in a grayscale mapping table; assigning the reduced second grayscale value to the second glyph in the grayscale mapping table; and generating a font file comprising the first glyph, the second glyph, and the grayscale mapping table.
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1. A method comprising: identifying, by a computer system comprising one or more processors, a font in which text is to be rendered on a display of an electronic device; identifying a first glyph associated with the font; determining that the first glyph comprises a first number of curved portions forming the first glyph; identifying a second glyph associated with the font; determining that the second glyph comprises a second number of curved portions forming the second glyph; determining that the first number of curved portions and the second number of curved portions are within a first range of curved portion values, wherein the first range of curved portion values corresponds to a first glyph category; rendering the first glyph on the display; rendering the second glyph on the display; receiving a request to increase a first contrast of the first glyph; increasing the first contrast by reducing a first grayscale value of the first glyph by an incremental amount, wherein the incremental amount is a numerical value; automatically increasing a second contrast of the second glyph by reducing a second grayscale value of the second glyph by the incremental amount; assigning the reduced first grayscale value to the first glyph in a grayscale mapping table; assigning the reduced second grayscale value to the second glyph in the grayscale mapping table; and generating a font file comprising the first glyph, the second glyph, and the grayscale mapping table. 4. The method of claim 1 , wherein the first glyph and the second glyph are rendered in a font size, further comprising: receiving a render request to render text content on the display of the electronic device; determining that the text content comprises the first glyph and the second glyph in the font size; and rendering the text content with the grayscale mapping table by rendering the first glyph with the reduced first grayscale value and the second glyph with the reduced second grayscale value.
| 0.839898 |
9,256,783 | 10 | 12 |
10. A computer-implemented method for preparing at least a portion of an electronic tax return with a computerized tax preparation application, the computer-implemented method comprising: a computing device receiving an image of at least one document containing tax data therein with an imaging device; the computing device identifying connected pixels within the received image and extracting one or more features from the acquired image of the at least one document based at least in part upon identified connected pixels; the computing device identifying a tax form corresponding to the at least one document from a plurality of different tax forms based at least in part on a confidence level associated with a comparison of the extracted one or more features to a database; and the computing device automatically populating at least one field of an interview screen generated by the computerized tax preparation application with at least a portion of the tax data determined from the acquired image of the at least one document to automatically prepare at least a portion of the electronic tax return.
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10. A computer-implemented method for preparing at least a portion of an electronic tax return with a computerized tax preparation application, the computer-implemented method comprising: a computing device receiving an image of at least one document containing tax data therein with an imaging device; the computing device identifying connected pixels within the received image and extracting one or more features from the acquired image of the at least one document based at least in part upon identified connected pixels; the computing device identifying a tax form corresponding to the at least one document from a plurality of different tax forms based at least in part on a confidence level associated with a comparison of the extracted one or more features to a database; and the computing device automatically populating at least one field of an interview screen generated by the computerized tax preparation application with at least a portion of the tax data determined from the acquired image of the at least one document to automatically prepare at least a portion of the electronic tax return. 12. The method of claim 10 , wherein the extracted one or more features comprise at least one of a title, a separator, a whitespace, a paragraph, an image and associated location information of an extracted feature.
| 0.744048 |
8,745,093 | 11 | 12 |
11. A machine-readable medium having stored thereon instructions which when executed by a processing device, cause the computing device to perform one or more operations comprising: receiving annotated data; parsing, at least partially, the annotated data, wherein parsing includes identifying syntactic structure of sentences within the annotated data; extracting training sets from the parsed annotated data, wherein the training sets are based on a plurality of features, wherein extracting comprises at least one of tagging the annotated data for marking words, and defining and segmenting words based on languages, wherein extracting further comprises extracting entity names and relations between entity names based on the information sets, and wherein extracting further comprises identifying information sets using memory-based Information Gain (IG)-Trees, wherein the IG-Trees are generated based on the plurality of features, wherein the plurality of features comprise one or more of words, phrases, sentences, and objects, and wherein each information set is identified based on a corresponding memory-based IG-Tree including one or more of a person-name IG-Tree, an entity-name IG-Tree, a noun phrase IG-Tree, and a relation IG-Tree.
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11. A machine-readable medium having stored thereon instructions which when executed by a processing device, cause the computing device to perform one or more operations comprising: receiving annotated data; parsing, at least partially, the annotated data, wherein parsing includes identifying syntactic structure of sentences within the annotated data; extracting training sets from the parsed annotated data, wherein the training sets are based on a plurality of features, wherein extracting comprises at least one of tagging the annotated data for marking words, and defining and segmenting words based on languages, wherein extracting further comprises extracting entity names and relations between entity names based on the information sets, and wherein extracting further comprises identifying information sets using memory-based Information Gain (IG)-Trees, wherein the IG-Trees are generated based on the plurality of features, wherein the plurality of features comprise one or more of words, phrases, sentences, and objects, and wherein each information set is identified based on a corresponding memory-based IG-Tree including one or more of a person-name IG-Tree, an entity-name IG-Tree, a noun phrase IG-Tree, and a relation IG-Tree. 12. The machine-readable medium of claim 11 , wherein the plurality of features comprise local context features that are extracted from syntactical relationship between two or more words of a set of words, and wherein the local context features are further extracted from a semantic feature relating to a semantic category of one or more headwords of the set of words, wherein the plurality of features further comprise one or more of global context features, surface linguistic features, and deep linguistic features, wherein the global context features comprise a broader view of each of the set of words in relation with content of an entire document that contains the set of words.
| 0.5 |
8,831,942 | 6 | 9 |
6. The method of claim 1 , further comprising: obtaining male training speech data of a plurality of male speakers and female training speech data of a plurality of female speakers during a training phase; determining a plurality of male pitch values based on the male training speech data and a plurality of female pitch values based on the female training speech data; determining the first pre-determined threshold based on the plurality of female pitch values; and determining the second pre-determined threshold based on the plurality of male pitch values.
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6. The method of claim 1 , further comprising: obtaining male training speech data of a plurality of male speakers and female training speech data of a plurality of female speakers during a training phase; determining a plurality of male pitch values based on the male training speech data and a plurality of female pitch values based on the female training speech data; determining the first pre-determined threshold based on the plurality of female pitch values; and determining the second pre-determined threshold based on the plurality of male pitch values. 9. The method of claim 6 , further comprising: training the male GMM using the plurality of male pitch values based on expectation maximization algorithm; training the female GMM using the plurality of female pitch values based on expectation maximization algorithm; and initializing the male GMM and the female GMM by k-mean clustering.
| 0.658907 |
6,088,524 | 17 | 18 |
17. A computer system according to claim 13 wherein said means for inferring one or more new predicates further comprises: means for identifying said aggregation predicates in said f.sub.{R,XA,Y} form which are relevant to said query; and means for inferring one or more new predicates from said relevant aggregation predicates in said f.sub.{R,X,A,Y} form.
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17. A computer system according to claim 13 wherein said means for inferring one or more new predicates further comprises: means for identifying said aggregation predicates in said f.sub.{R,XA,Y} form which are relevant to said query; and means for inferring one or more new predicates from said relevant aggregation predicates in said f.sub.{R,X,A,Y} form. 18. A computer system according to claim 17 wherein said relevant aggregation predicates in f.sub.{R,X,A,Y} form, are those which involve R and a groupby list and aggregation attribute pair, GL.sub.R, where R includes any relation upon which said query does an aggregation, and any relation which defines a view upon which said query does an aggregation, and GL.sub.R includes any groupby list and aggregated attribute pair in R and any projection of a groupby list and aggregated attribute of a relation which defines R.
| 0.5 |
8,423,370 | 23 | 24 |
23. The system of claim 22 , wherein the processing unit is further configured to store in an electronic health record a representation of the received verbal communication classified as having a durable importance.
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23. The system of claim 22 , wherein the processing unit is further configured to store in an electronic health record a representation of the received verbal communication classified as having a durable importance. 24. The system of claim 23 , wherein the representation of the received verbal communication classified as having a durable importance comprises CDA-2 representation.
| 0.5 |
8,286,171 | 1 | 8 |
1. A computer implemented method for preventing unauthorized disclosure of secure information, the computer implemented method comprising: receiving information including a first text, by a computer system having at least a processor for executing instructions, said first text including a plurality of words; normalizing, by said computer system, said first text into a first canonical text expression, said first canonical text expression including a plurality of normalized words; generating, at said computer system, a first word hash list for said first canonical text expression, where said first word hash list is generated at a word level; generating, at said computer system, a first set of fingerprints for said first word hash list; wherein generating said first word hash list includes converting said plurality of normalized words into a plurality of word-value hashes, each specific one of said word-value hashes representing a specific normalized word; and wherein said generating said first set of fingerprints includes: assigning a sliding window of size W, wherein said sliding window is used for reading a W number of said word-value hashes from said first word hash list; using said sliding window to read said W number of said word-level hashes from said first word hash list; designating said word-value hash with a distinct value within said sliding window as an anchor; and generating a fingerprint using a fingerprint hash function, wherein said fingerprint hash function is applied over all said word-value hashes contained within a start of said sliding window to where said anchor resides in said sliding window.
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1. A computer implemented method for preventing unauthorized disclosure of secure information, the computer implemented method comprising: receiving information including a first text, by a computer system having at least a processor for executing instructions, said first text including a plurality of words; normalizing, by said computer system, said first text into a first canonical text expression, said first canonical text expression including a plurality of normalized words; generating, at said computer system, a first word hash list for said first canonical text expression, where said first word hash list is generated at a word level; generating, at said computer system, a first set of fingerprints for said first word hash list; wherein generating said first word hash list includes converting said plurality of normalized words into a plurality of word-value hashes, each specific one of said word-value hashes representing a specific normalized word; and wherein said generating said first set of fingerprints includes: assigning a sliding window of size W, wherein said sliding window is used for reading a W number of said word-value hashes from said first word hash list; using said sliding window to read said W number of said word-level hashes from said first word hash list; designating said word-value hash with a distinct value within said sliding window as an anchor; and generating a fingerprint using a fingerprint hash function, wherein said fingerprint hash function is applied over all said word-value hashes contained within a start of said sliding window to where said anchor resides in said sliding window. 8. A computer implemented method for preventing unauthorized disclosure of secure information as recited in claim 1 , wherein said receiving information includes receiving secure information from a local database.
| 0.865869 |
8,533,206 | 1 | 5 |
1. A method performed by one or more computers, the method comprising: maintaining a collection of uniform resource locator (URL) patterns, wherein each URL pattern is associated with a respective label; receiving a search query that includes a query term and a label of interest from a client device; generating, for the label of interest, by operation of a computer system, a domain filter that satisfies a maximum size threshold and a maximum false positive error rate threshold, wherein generating the domain filter includes: iteratively adjusting a size of the domain filter, wherein in each iteration, the method comprises: identifying a new set of one or more URL patterns as a current set of offsets, wherein each of the one or more URL patterns is associated with a respective label that matches the label of interest; processing the URL patterns in the collection of URL patterns to generate an offset error for the current set of offsets; and determining whether or not the offset error for the current set of offsets is greater than an offset error for a best set of offsets, (i) and if so, performing a next iteration unless no new set of one or more URL patterns is identifiable, (ii) and otherwise, determining whether or not a current size of the domain filter satisfies the maximum size threshold and a current error rate for the domain filter satisfies the maximum false positive error rate threshold, (a) and if so, replacing values of the best set of offsets with values of the current set of offsets and performing the next iteration unless no new set of one or more URL patterns is identifiable, (b) and otherwise, performing the next iteration unless no new set of one or more URL patterns is identifiable; and upon determining that no new set of one or more URL patterns is identifiable, generating the domain filter for the label of interest using values of the best set of offsets; and filtering search results that are relevant to the query term with the domain filter to generate a plurality of filtered search results.
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1. A method performed by one or more computers, the method comprising: maintaining a collection of uniform resource locator (URL) patterns, wherein each URL pattern is associated with a respective label; receiving a search query that includes a query term and a label of interest from a client device; generating, for the label of interest, by operation of a computer system, a domain filter that satisfies a maximum size threshold and a maximum false positive error rate threshold, wherein generating the domain filter includes: iteratively adjusting a size of the domain filter, wherein in each iteration, the method comprises: identifying a new set of one or more URL patterns as a current set of offsets, wherein each of the one or more URL patterns is associated with a respective label that matches the label of interest; processing the URL patterns in the collection of URL patterns to generate an offset error for the current set of offsets; and determining whether or not the offset error for the current set of offsets is greater than an offset error for a best set of offsets, (i) and if so, performing a next iteration unless no new set of one or more URL patterns is identifiable, (ii) and otherwise, determining whether or not a current size of the domain filter satisfies the maximum size threshold and a current error rate for the domain filter satisfies the maximum false positive error rate threshold, (a) and if so, replacing values of the best set of offsets with values of the current set of offsets and performing the next iteration unless no new set of one or more URL patterns is identifiable, (b) and otherwise, performing the next iteration unless no new set of one or more URL patterns is identifiable; and upon determining that no new set of one or more URL patterns is identifiable, generating the domain filter for the label of interest using values of the best set of offsets; and filtering search results that are relevant to the query term with the domain filter to generate a plurality of filtered search results. 5. The method of claim 1 , further comprising: determining a respective URL pattern length of each URL pattern in the collection of URL patterns, wherein the URL pattern length of a particular URL pattern is a value corresponding to a number of alphanumeric characters that appears after a domain name in the particular URL pattern.
| 0.605701 |
4,320,451 | 45 | 46 |
45. The method of communicating between processes in a general purpose computer as recited in claim 44 including the step of detecting on behalf of said second process of said processes the occurrence as specified by the step which specifies the condition which constitutes an event occurrence.
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45. The method of communicating between processes in a general purpose computer as recited in claim 44 including the step of detecting on behalf of said second process of said processes the occurrence as specified by the step which specifies the condition which constitutes an event occurrence. 46. The method of communicating between processes in a general purpose computer as recited in claim 45 including the step for executing the reaction specified by the step which specifies the reaction required on behalf of said second process.
| 0.5 |
9,141,622 | 18 | 19 |
18. The method of claim 17 , wherein determining the updates comprises: selecting the set of feature weights, the selection independent of the feature weights selected in any other iteration.
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18. The method of claim 17 , wherein determining the updates comprises: selecting the set of feature weights, the selection independent of the feature weights selected in any other iteration. 19. The method of claim 18 , wherein selecting the set of feature weights during a first iteration includes: calculating a log-likelihood-ratio association metric (LLR score) for at least some examples in a training set; identifying a maximum number of feature weights to include in the set of feature weights; and including feature weights into the set of feature weights in order of their LLR scores, the set of feature weights limited to include no more than the maximum number of feature weights, and any feature weights included having a non-zero loss function derivative.
| 0.5 |
8,478,753 | 1 | 5 |
1. A method for advanced searching of a service registry for a service description that most closely matches a service name provided by a user, the method comprising: receiving the service name, by a processor of a computer on which a Service Oriented Architecture (SOA) service registry system runs, wherein the SOA service registry system comprises the service registry, a name parser, a dictionary, and a name composer, and wherein the service registry comprises at least one service description searchable by a respectively associated service name; determining that the service name does not have the service description that is an exact match to the received service name in the service registry; generating a ranked alternative service name list by use of the name parser, the dictionary, and the name composer, wherein the ranked alternative service name list comprises at least one alternative service name and a respective rank of each alternative service name of said at least one alternative service name, wherein the respective rank indicates how closely the alternative service name associated with the respective rank resembles the service name provided by the user; ascertaining that the service description matches the highest ranked alternative service name in the alternative service name list by searching the service registry with said at least one alternative service name in a descending order of the respective ranks of said at least one alternative service name; and communicating the service description matching the highest ranked alternative service name to the user.
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1. A method for advanced searching of a service registry for a service description that most closely matches a service name provided by a user, the method comprising: receiving the service name, by a processor of a computer on which a Service Oriented Architecture (SOA) service registry system runs, wherein the SOA service registry system comprises the service registry, a name parser, a dictionary, and a name composer, and wherein the service registry comprises at least one service description searchable by a respectively associated service name; determining that the service name does not have the service description that is an exact match to the received service name in the service registry; generating a ranked alternative service name list by use of the name parser, the dictionary, and the name composer, wherein the ranked alternative service name list comprises at least one alternative service name and a respective rank of each alternative service name of said at least one alternative service name, wherein the respective rank indicates how closely the alternative service name associated with the respective rank resembles the service name provided by the user; ascertaining that the service description matches the highest ranked alternative service name in the alternative service name list by searching the service registry with said at least one alternative service name in a descending order of the respective ranks of said at least one alternative service name; and communicating the service description matching the highest ranked alternative service name to the user. 5. The method of claim 1 , said ascertaining comprising: searching a top rank by locating a greatest value among all ranks in the ranked alternative service name list; and looking up the service registry for the service description with a first alternative service name associated with the top rank from said searching such that the service description is associated with the highest ranked alternative service name.
| 0.864318 |
9,391,902 | 2 | 3 |
2. The method of claim 1 wherein determining the load factor comprises sending one or more test queries to the data source.
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2. The method of claim 1 wherein determining the load factor comprises sending one or more test queries to the data source. 3. The method of claim 2 wherein the test queries include a first query that does not access a data table and a second query that accesses a data table.
| 0.703125 |
9,182,831 | 11 | 18 |
11. A method for implementing sliding input of a text based upon an on-screen soft keyboard on an electronic equipment having a memory device and a processor, characterized in that, said method comprises the following steps: recording user-sliding trajectories; converting the recorded user-sliding trajectories into a user-sliding trajectory feature set; filtering in the memory device and originally choosing the words, wherein each of the originally chosen words has similar ideal sliding trajectory features with the user-sliding trajectory feature set; calculating a similarity between the ideal sliding trajectory features of each originally chosen word and said user-sliding trajectory features set according to key points on said trajectory, comprising the steps of: calculating a rough similarity between the ideal sliding trajectory features of each originally chosen word and said user-sliding trajectory features set, wherein said step of calculating the rough similarity comprises calculating a linear matching distance between the ideal trajectory of each originally chosen word and said user-sliding trajectory feature set; and calculating an accurate similarity between the ideal sliding trajectory features of each word obtained from the rough similarity calculation result and said user-sliding trajectory features set; obtaining candidate words according to the similarity, wherein the ideal sliding trajectory of each candidate word contains at least one of the key points or at least one of the surrounding points of at least one of the key points on said user-sliding trajectory; and displaying the candidate words.
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11. A method for implementing sliding input of a text based upon an on-screen soft keyboard on an electronic equipment having a memory device and a processor, characterized in that, said method comprises the following steps: recording user-sliding trajectories; converting the recorded user-sliding trajectories into a user-sliding trajectory feature set; filtering in the memory device and originally choosing the words, wherein each of the originally chosen words has similar ideal sliding trajectory features with the user-sliding trajectory feature set; calculating a similarity between the ideal sliding trajectory features of each originally chosen word and said user-sliding trajectory features set according to key points on said trajectory, comprising the steps of: calculating a rough similarity between the ideal sliding trajectory features of each originally chosen word and said user-sliding trajectory features set, wherein said step of calculating the rough similarity comprises calculating a linear matching distance between the ideal trajectory of each originally chosen word and said user-sliding trajectory feature set; and calculating an accurate similarity between the ideal sliding trajectory features of each word obtained from the rough similarity calculation result and said user-sliding trajectory features set; obtaining candidate words according to the similarity, wherein the ideal sliding trajectory of each candidate word contains at least one of the key points or at least one of the surrounding points of at least one of the key points on said user-sliding trajectory; and displaying the candidate words. 18. The method for according to claim 11 , characterized in that, said step of filtering in the memory device and originally choosing the words includes a substep of calculating a rough matching degree between words stored in said memory device according to the ideal sliding trajectory features and said user-sliding trajectory features set.
| 0.704152 |
9,386,107 | 28 | 45 |
28. A system, comprising: one or more processors of one or more computing systems; and one or more memories including stored instructions that, when executed by at least one of the one or more processors, cause the at least one processor to: obtain, via electronic interactions over one or more computer networks, information from one or more network-accessible sites including a plurality of textual comments that are supplied by multiple users to the one or more network-accessible sites over multiple prior time periods and that are related to multiple topics; analyze, for each of the multiple prior time periods, multiple textual comments from the plurality that are for the prior time period and identify a subset of the multiple textual comments whose contents are associated with a specified content category; determine, for each of the multiple prior time periods, an actual quantity of the identified textual comments for the prior time period that are associated with an indicated topic from the multiple topics; predict, for each of multiple future time periods, an expected quantity of future textual comments associated with the indicated topic that will be supplied by users during the future time period, the predicting being based at least in part on the determined actual quantities for the multiple prior time periods and by using a first defined template representing a first set of changes in quantity over time; generate, from the determined actual quantities for the multiple prior time periods, a new defined template that includes the determined actual quantities for the multiple prior time periods and that represents a second set of changes in quantity of textual comments over time; provide, via additional electronic interactions over the one or more computer networks, information to one or more recipients that includes, for each of one or more of the multiple future time periods, an indication of the predicted expected quantity of future textual comments for the future time period; and use, at a later time after the generating of the new defined template, the new defined template to provide additional predictions of expected future quantities of textual comments for additional future time periods based on additional actual quantities of textual comments received preceding the additional future time periods.
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28. A system, comprising: one or more processors of one or more computing systems; and one or more memories including stored instructions that, when executed by at least one of the one or more processors, cause the at least one processor to: obtain, via electronic interactions over one or more computer networks, information from one or more network-accessible sites including a plurality of textual comments that are supplied by multiple users to the one or more network-accessible sites over multiple prior time periods and that are related to multiple topics; analyze, for each of the multiple prior time periods, multiple textual comments from the plurality that are for the prior time period and identify a subset of the multiple textual comments whose contents are associated with a specified content category; determine, for each of the multiple prior time periods, an actual quantity of the identified textual comments for the prior time period that are associated with an indicated topic from the multiple topics; predict, for each of multiple future time periods, an expected quantity of future textual comments associated with the indicated topic that will be supplied by users during the future time period, the predicting being based at least in part on the determined actual quantities for the multiple prior time periods and by using a first defined template representing a first set of changes in quantity over time; generate, from the determined actual quantities for the multiple prior time periods, a new defined template that includes the determined actual quantities for the multiple prior time periods and that represents a second set of changes in quantity of textual comments over time; provide, via additional electronic interactions over the one or more computer networks, information to one or more recipients that includes, for each of one or more of the multiple future time periods, an indication of the predicted expected quantity of future textual comments for the future time period; and use, at a later time after the generating of the new defined template, the new defined template to provide additional predictions of expected future quantities of textual comments for additional future time periods based on additional actual quantities of textual comments received preceding the additional future time periods. 45. The system of claim 28 wherein the stored instructions further cause the at least one processor to, before the using of the new defined template at the later time to provide additional predictions of expected future quantities of textual comments: using, for each of multiple further times before the later time and after the generating of the new defined template, the new defined template to predict further expected future quantities of textual comments for the further time; and determining, based at least in part on success from the using of the new defined template for the multiple further times, to promote further use of the new defined template, and wherein the using of the new defined template at the later time is based at least in part on the determining to promote the further use of the new defined template.
| 0.546499 |
6,108,632 | 26 | 28 |
26. A method of operating a transaction support apparatus for use by one or more transaction operators, the support apparatus comprising an electronic speech recognition device, said method comprising: coupling the electronic speech recognition device to receive a speech signal including a confirmatory dialogue from a transaction operator to another party; recognizing values of parameters of the transaction within the speech of the transaction operator utilizing said electronic speech recognizer; and supplying data recording the results of said recognition in electronic form from said electronic speech recognizing unit to an electronic transaction recording computer together with at least a portion of said received speech signal.
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26. A method of operating a transaction support apparatus for use by one or more transaction operators, the support apparatus comprising an electronic speech recognition device, said method comprising: coupling the electronic speech recognition device to receive a speech signal including a confirmatory dialogue from a transaction operator to another party; recognizing values of parameters of the transaction within the speech of the transaction operator utilizing said electronic speech recognizer; and supplying data recording the results of said recognition in electronic form from said electronic speech recognizing unit to an electronic transaction recording computer together with at least a portion of said received speech signal. 28. The method of claim 26 further comprising the step of: generating a confirmatory output indication, recognizable by a said human transaction operator, of values of said parameters thus recognized.
| 0.5 |
8,140,449 | 13 | 15 |
13. A computer-implemented method, comprising: identifying, by a processor, in a document of a plurality of documents, one or more textual sequences; identifying, by the processor, based on the one or more textual sequences, a presence of novel content in the document where the novel content includes content that does not occur in other documents of the plurality of documents; assigning, by the processor, a score to the document based on the identified novel content including each of the one or more textual sequences; and ranking, by the processor, the document among the plurality of documents based on the assigned score.
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13. A computer-implemented method, comprising: identifying, by a processor, in a document of a plurality of documents, one or more textual sequences; identifying, by the processor, based on the one or more textual sequences, a presence of novel content in the document where the novel content includes content that does not occur in other documents of the plurality of documents; assigning, by the processor, a score to the document based on the identified novel content including each of the one or more textual sequences; and ranking, by the processor, the document among the plurality of documents based on the assigned score. 15. The computer-implemented method of claim 13 , where identifying the presence of novel content further comprises: determining an indication of importance of each of the one or more textual sequences to the document.
| 0.763557 |
5,572,668 | 13 | 14 |
13. The apparatus as defined by claim 12, wherein said translator replaces identified ASCII characters in said universal script with predefined characters in said target language.
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13. The apparatus as defined by claim 12, wherein said translator replaces identified ASCII characters in said universal script with predefined characters in said target language. 14. The apparatus as defined by claim 13, wherein said predefined characters represent problem characters in said target language, said predefined characters being selected as having a greater likelihood of detecting errors in said computer program such that said predefined characters provide maximal coverage testing of said computer program.
| 0.5 |
7,849,416 | 1 | 10 |
1. A computer-implemented method for creating a prototype that includes motion control, machine vision, and Data Acquisition (DAQ) functionality, the method comprising: displaying a graphical user interface (GUI) that provides GUI access to a set of operations, wherein the set of operations includes one or more motion control operations, one or more machine vision operations, and one or more DAQ operations; creating a sequence of operations, wherein creating the sequence comprises including a plurality of operations in the sequence in response to user input selecting each operation in the plurality of operations from the GUI, wherein including the plurality of operations in the sequence in response to the user input selecting each operation in the plurality of operations from the GUI comprises including the plurality of operations in the sequence without receiving user input specifying program code for performing the plurality of operations; wherein the plurality of operations included in the sequence includes at least one motion control operation, at least one machine vision operation, and at least one DAQ operation, wherein at least one of the DAQ operations included in the sequence is operable to control a DAQ measurement device to acquire measurement data of a device under test; wherein the method further comprises storing information representing the sequence of operations in a data structure, wherein the sequence of operations comprises the prototype.
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1. A computer-implemented method for creating a prototype that includes motion control, machine vision, and Data Acquisition (DAQ) functionality, the method comprising: displaying a graphical user interface (GUI) that provides GUI access to a set of operations, wherein the set of operations includes one or more motion control operations, one or more machine vision operations, and one or more DAQ operations; creating a sequence of operations, wherein creating the sequence comprises including a plurality of operations in the sequence in response to user input selecting each operation in the plurality of operations from the GUI, wherein including the plurality of operations in the sequence in response to the user input selecting each operation in the plurality of operations from the GUI comprises including the plurality of operations in the sequence without receiving user input specifying program code for performing the plurality of operations; wherein the plurality of operations included in the sequence includes at least one motion control operation, at least one machine vision operation, and at least one DAQ operation, wherein at least one of the DAQ operations included in the sequence is operable to control a DAQ measurement device to acquire measurement data of a device under test; wherein the method further comprises storing information representing the sequence of operations in a data structure, wherein the sequence of operations comprises the prototype. 10. The method of claim 1 , further comprising: creating program instructions executable to perform the sequence of operations; and executing the program instructions.
| 0.907837 |
9,720,962 | 1 | 6 |
1. A method, in a question and answer (QA) system comprising a processor and a memory, for generating an answer to a superlative question, the method comprising: analyzing, by the QA system, the superlative question to extract a superlative term in the superlative question and a focus of the superlative question; identifying, by the QA system, a metric by which to evaluate the superlative term based on one of a clue term in the superlative question or one or more portions of content of a corpus of information comprising the superlative term and focus; executing, by the QA system, a search of the corpus to identify one or more candidate answers to the superlative question based on evidence passages in the corpus, the superlative term, the focus, and the metric; and outputting, by the QA system, a final answer to the superlative question based on the one or more candidate answers, wherein identifying the metric comprises identifying a plurality of metrics, and wherein executing a search of the corpus to identify one or more candidate answers to the superlative question based on evidence passages in the corpus, the superlative term, the focus, and the metric comprises performing a separate search for each metric in the plurality of metrics and generating a separate set of one or more candidate answers for each metric in the plurality of metrics.
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1. A method, in a question and answer (QA) system comprising a processor and a memory, for generating an answer to a superlative question, the method comprising: analyzing, by the QA system, the superlative question to extract a superlative term in the superlative question and a focus of the superlative question; identifying, by the QA system, a metric by which to evaluate the superlative term based on one of a clue term in the superlative question or one or more portions of content of a corpus of information comprising the superlative term and focus; executing, by the QA system, a search of the corpus to identify one or more candidate answers to the superlative question based on evidence passages in the corpus, the superlative term, the focus, and the metric; and outputting, by the QA system, a final answer to the superlative question based on the one or more candidate answers, wherein identifying the metric comprises identifying a plurality of metrics, and wherein executing a search of the corpus to identify one or more candidate answers to the superlative question based on evidence passages in the corpus, the superlative term, the focus, and the metric comprises performing a separate search for each metric in the plurality of metrics and generating a separate set of one or more candidate answers for each metric in the plurality of metrics. 6. The method of claim 1 , wherein outputting the final answer to the superlative question based on the one or more candidate answers comprises outputting a final answer for each metric in the plurality of metrics as alternative answers to the superlative question.
| 0.683014 |
9,747,349 | 2 | 4 |
2. The method of claim 1 , wherein the process of determining the optimal set of grouping patterns continues until no additional combinations of grouping patterns and combined cubes allows for a larger percentage of the historical queries to be answered.
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2. The method of claim 1 , wherein the process of determining the optimal set of grouping patterns continues until no additional combinations of grouping patterns and combined cubes allows for a larger percentage of the historical queries to be answered. 4. The method of claim 2 , wherein other of the grouping patterns and combined cubes may be further combined until all combined cubes have reached the predefined maximum number of dimensions.
| 0.5 |
8,526,739 | 30 | 39 |
30. A method, comprising: receiving an image of a document; performing optical character recognition (OCR) on the image of the document; extracting an address of a sender of the document from the image based on the OCR; comparing the extracted address with content in a first database; identifying complementary textual information in a second database based on the address; and at least one of: extracting additional content from the image of the document; correcting OCR errors in the document using the complementary textual information, and normalizing data from the document prior to determining a validity of the document using at least one of the complementary textual information and predefined business rules.
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30. A method, comprising: receiving an image of a document; performing optical character recognition (OCR) on the image of the document; extracting an address of a sender of the document from the image based on the OCR; comparing the extracted address with content in a first database; identifying complementary textual information in a second database based on the address; and at least one of: extracting additional content from the image of the document; correcting OCR errors in the document using the complementary textual information, and normalizing data from the document prior to determining a validity of the document using at least one of the complementary textual information and predefined business rules. 39. The method as recited in claim 30 , wherein extracting the address of the sender comprises scanning a barcode.
| 0.64375 |
4,351,032 | 23 | 29 |
23. A frequency sensing circuit for sensing the presence of a frequency component F.sub.0 in an applied signal, comprising: means for sampling said applied signal at a rate of NF.sub.0, where N is any integer of two or more; an adder having first and second inputs and an output, said first input of said adder coupled to said means for sampling; a feedback circuit coupling said output to said second input of said adder, said feedback circuit including delay means and a multiplier coupled in series, said delay means providing a time delay of an integral number of periods 1/2F.sub.0 corresponding to the frequency to be sensed and said multiplier multiplying signals in said feedback circuit by a number less than one and greater than zero; and means coupled to said frequency sensing circuit for deriving an output signal therefrom having a magnitude indicative of the frequency sensed.
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23. A frequency sensing circuit for sensing the presence of a frequency component F.sub.0 in an applied signal, comprising: means for sampling said applied signal at a rate of NF.sub.0, where N is any integer of two or more; an adder having first and second inputs and an output, said first input of said adder coupled to said means for sampling; a feedback circuit coupling said output to said second input of said adder, said feedback circuit including delay means and a multiplier coupled in series, said delay means providing a time delay of an integral number of periods 1/2F.sub.0 corresponding to the frequency to be sensed and said multiplier multiplying signals in said feedback circuit by a number less than one and greater than zero; and means coupled to said frequency sensing circuit for deriving an output signal therefrom having a magnitude indicative of the frequency sensed. 29. A frequency sensing circuit according to claim 23 wherein said output deriving means is coupled to said adder.
| 0.813725 |
8,856,642 | 10 | 11 |
10. A system, comprising: a processor; logic encoded in one or more tangible media for execution by the processor, the logic when executed by the processor causing the system to perform operations comprising: receiving annotated documents comprising annotated fields; analyzing the annotated documents to determine contextual information for each of the annotated fields; determining discriminative sequences using the contextual information by: aligning pairs of strings having possible contextual matches; normalizing the pairs of strings by extracting matching segments having a given length; aggregating the normalized pairs of strings; and applying a greedy contiguity heuristic to the aggregated normalized pairs of strings, wherein the greedy contiguity heuristic is used by the system to evaluate any of a number of matching segments, a number of gaps between segments, and variances between segment lengths; determining longest contiguous common subsequences between aligned pairs of strings of the annotated documents; determining a frequency of occurrence of similar longest contiguous common subsequences; and generating a proposed rule or a feature set from longest contiguous common subsequences having a desired frequency of occurrence; and an extractor system that trains a classifier model using the feature set.
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10. A system, comprising: a processor; logic encoded in one or more tangible media for execution by the processor, the logic when executed by the processor causing the system to perform operations comprising: receiving annotated documents comprising annotated fields; analyzing the annotated documents to determine contextual information for each of the annotated fields; determining discriminative sequences using the contextual information by: aligning pairs of strings having possible contextual matches; normalizing the pairs of strings by extracting matching segments having a given length; aggregating the normalized pairs of strings; and applying a greedy contiguity heuristic to the aggregated normalized pairs of strings, wherein the greedy contiguity heuristic is used by the system to evaluate any of a number of matching segments, a number of gaps between segments, and variances between segment lengths; determining longest contiguous common subsequences between aligned pairs of strings of the annotated documents; determining a frequency of occurrence of similar longest contiguous common subsequences; and generating a proposed rule or a feature set from longest contiguous common subsequences having a desired frequency of occurrence; and an extractor system that trains a classifier model using the feature set. 11. The system according to claim 10 , wherein the processor further executes the logic to perform operations of: executing a base annotation of original documents to create documents with base annotations, the base annotations comprising basic categories of words or groups of characters; and providing the documents with base annotations to a document annotator via a user interface.
| 0.5 |
9,256,680 | 1 | 3 |
1. A system, comprising: a relevance component associated with each result of a results page, the relevance component having an interactive positive relevance as a “more” link configured to enable positive feedback as to each result and an interactive negative relevance as a “none” link configured to enable negative feedback as to each result, the results page related to an original query; an analysis component configured to automatically analyze metadata associated with each result and automatically select a topical term from each result; a query formulation component configured to automatically reformulate for each result of the relevance component a new query associated with the “more” link and a new query associated with the “none” link; a query processing component configured to automatically process the new query associated with selection of the “more” link or the new query associated with selection of the “none” link for each result of the results page, and return new results for the new query, such that selection of the “more” link includes the topical term in the processing of the new search results, or selection of the “none” link indicates negation of the topical term from the processing of the new search results to ensure the new search results do not contain the topical term; and a microprocessor configured to execute computer-executable instructions in a memory, the execution of the instructions enables at least one of the relevance component, analysis component, query formulation component, or query processing component.
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1. A system, comprising: a relevance component associated with each result of a results page, the relevance component having an interactive positive relevance as a “more” link configured to enable positive feedback as to each result and an interactive negative relevance as a “none” link configured to enable negative feedback as to each result, the results page related to an original query; an analysis component configured to automatically analyze metadata associated with each result and automatically select a topical term from each result; a query formulation component configured to automatically reformulate for each result of the relevance component a new query associated with the “more” link and a new query associated with the “none” link; a query processing component configured to automatically process the new query associated with selection of the “more” link or the new query associated with selection of the “none” link for each result of the results page, and return new results for the new query, such that selection of the “more” link includes the topical term in the processing of the new search results, or selection of the “none” link indicates negation of the topical term from the processing of the new search results to ensure the new search results do not contain the topical term; and a microprocessor configured to execute computer-executable instructions in a memory, the execution of the instructions enables at least one of the relevance component, analysis component, query formulation component, or query processing component. 3. The system of claim 1 , wherein the relevance component is dynamically associated with each result on the results page when the results page is rendered.
| 0.694118 |
9,135,396 | 16 | 18 |
16. A system, comprising: a memory comprising program instructions; and one or more processors coupled to said memory, wherein the program instructions are executable by at least one of said one or more processors to: for each particular item of a plurality of items: determine one or more other items of the plurality of items that are each distinct from but similar to the particular item, wherein said determining is based on accessing data that includes, for each item of the plurality of items, a textual description of the item that describes the item but is not itself an item in the plurality of items; for each given item of the determined one or more other items, identify an item data pair with one member comprising a sequence of text strings from the textual description of the particular item, and the other member comprising another sequence of text strings from the textual description of the given item; subsequent to said identifying, align each identified item data pair, wherein to align the identified item data pair the program instructions are configured to align text in the sequence of text strings from the textual description of the particular item with text in the other sequence of text strings from the textual description of the given item; and for each aligned item data pair, determine one or more misalignments of the aligned item data pair, and assign a similarity score to the aligned item data pair dependent on the one or more misalignments, wherein the similarity score indicates a degree of confidence that the given item and the particular item are distinct variants of each other; and based on a plurality of the aligned item data pairs and similarity scores assigned to each of those aligned item data pairs, determine a variant set comprising multiple ones of the plurality of items, wherein each item of the variant set is determined to be a variant of each other item of the variant set; wherein at least one of the aligned item data pairs comprises multiple misalignments; for each misalignment of the multiple misalignments, determine a respective subscore based on that misalignment; wherein to assign the similarity score to said at least one aligned item data pair, the program instructions are configured to assign a result of a combination of each of said subscores to said at least one aligned item data pair.
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16. A system, comprising: a memory comprising program instructions; and one or more processors coupled to said memory, wherein the program instructions are executable by at least one of said one or more processors to: for each particular item of a plurality of items: determine one or more other items of the plurality of items that are each distinct from but similar to the particular item, wherein said determining is based on accessing data that includes, for each item of the plurality of items, a textual description of the item that describes the item but is not itself an item in the plurality of items; for each given item of the determined one or more other items, identify an item data pair with one member comprising a sequence of text strings from the textual description of the particular item, and the other member comprising another sequence of text strings from the textual description of the given item; subsequent to said identifying, align each identified item data pair, wherein to align the identified item data pair the program instructions are configured to align text in the sequence of text strings from the textual description of the particular item with text in the other sequence of text strings from the textual description of the given item; and for each aligned item data pair, determine one or more misalignments of the aligned item data pair, and assign a similarity score to the aligned item data pair dependent on the one or more misalignments, wherein the similarity score indicates a degree of confidence that the given item and the particular item are distinct variants of each other; and based on a plurality of the aligned item data pairs and similarity scores assigned to each of those aligned item data pairs, determine a variant set comprising multiple ones of the plurality of items, wherein each item of the variant set is determined to be a variant of each other item of the variant set; wherein at least one of the aligned item data pairs comprises multiple misalignments; for each misalignment of the multiple misalignments, determine a respective subscore based on that misalignment; wherein to assign the similarity score to said at least one aligned item data pair, the program instructions are configured to assign a result of a combination of each of said subscores to said at least one aligned item data pair. 18. The system of claim 16 , wherein the sequence of text strings from the textual description of the particular item, and the other sequence of text strings from the textual description of the given item, respectively comprise one or more of: an item title, item description, and item specification for the particular item and the other item.
| 0.857202 |
9,082,406 | 25 | 26 |
25. A dialog system that uses a dialog move tree to manage a conversation between the dialog system and a user, comprising: a dialog manager to associate the conversation with a complex activity, the dialog manager being configured to interpret semantically incoming user requests and utterances, and perform an appropriate action on a device controlled by the dialog system; and a plan engine to execute a plan script in connection with the complex activity, the plan script including a set of atomic dialog activities and logic to control a data and sequence flow of the atomic dialog activities, the set of atomic dialog activities being sub-activities of the complex activity; wherein: the dialog move tree forms a structured history of dialog moves performed by the dialog system and the user in the conversation; the dialog manager dynamically adds to the dialog move tree a node for each occurrence of an atomic dialog activity of the set of atomic dialog activities; the complex activity is specified via a declarative activity specification language that connects the atomic dialog activities with a process related to at least one of a web service and a business process; and the plan engine is configured to interact with a process engine that executes a process script in connection with the process, the process script including a set of atomic process activities and corresponding order constraints of the atomic process activities, the set of atomic process activities being sub-activities of the process, the process script conforming to BPEL4WS (Business Process Execution Language for Web Services).
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25. A dialog system that uses a dialog move tree to manage a conversation between the dialog system and a user, comprising: a dialog manager to associate the conversation with a complex activity, the dialog manager being configured to interpret semantically incoming user requests and utterances, and perform an appropriate action on a device controlled by the dialog system; and a plan engine to execute a plan script in connection with the complex activity, the plan script including a set of atomic dialog activities and logic to control a data and sequence flow of the atomic dialog activities, the set of atomic dialog activities being sub-activities of the complex activity; wherein: the dialog move tree forms a structured history of dialog moves performed by the dialog system and the user in the conversation; the dialog manager dynamically adds to the dialog move tree a node for each occurrence of an atomic dialog activity of the set of atomic dialog activities; the complex activity is specified via a declarative activity specification language that connects the atomic dialog activities with a process related to at least one of a web service and a business process; and the plan engine is configured to interact with a process engine that executes a process script in connection with the process, the process script including a set of atomic process activities and corresponding order constraints of the atomic process activities, the set of atomic process activities being sub-activities of the process, the process script conforming to BPEL4WS (Business Process Execution Language for Web Services). 26. The dialog system of claim 25 , wherein the dialog move tree represents a dynamic state of interactions between the user and the dialog system.
| 0.5 |
8,392,173 | 1 | 6 |
1. A method for translating text messages, comprising: receiving a text message in a source language; accessing a storage device on a client processing device to obtain language preferences of a plurality of users; determining whether the source language is similar to a preferred destination language; translating the text message into the preferred destination language when the source language is not similar to the preferred destination language; outputting the text message in the preferred destination language for display in a transcript window; sending a reply text message in the preferred destination language; determining whether the reply text message is in the same language as a preferred language of a reply text message recipient; translating the reply text message into the preferred language of the reply text message recipient when language of the reply text message is not the same as the preferred language of the reply text message recipient; displaying the translated reply text message on a display device of the reply text message recipient; and receiving and providing authorization information for accessing an instant message account of one of the plurality of users, wherein all messages in the transcript window are displayed in the preferred destination language.
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1. A method for translating text messages, comprising: receiving a text message in a source language; accessing a storage device on a client processing device to obtain language preferences of a plurality of users; determining whether the source language is similar to a preferred destination language; translating the text message into the preferred destination language when the source language is not similar to the preferred destination language; outputting the text message in the preferred destination language for display in a transcript window; sending a reply text message in the preferred destination language; determining whether the reply text message is in the same language as a preferred language of a reply text message recipient; translating the reply text message into the preferred language of the reply text message recipient when language of the reply text message is not the same as the preferred language of the reply text message recipient; displaying the translated reply text message on a display device of the reply text message recipient; and receiving and providing authorization information for accessing an instant message account of one of the plurality of users, wherein all messages in the transcript window are displayed in the preferred destination language. 6. The method of claim 1 , further comprising broadcasting the text message to a plurality of users in a chat session.
| 0.703518 |
9,501,580 | 1 | 9 |
1. A computer implemented method, comprising: selecting a content item from a plurality of content that is candidate for presentation to first time visitors of a website; identifying all topics present in the selected content item; scoring each identified topic; associating each scored topic with at least one subject matter category; calculating a first score associated with the selected content item based on an intercategory topic density determined for the selected content item; calculating a second score associated with the selected content item based on a topic density determined for the selected content item; calculating a third score associated with the selected content item based on a presentability of the selected content item; calculating a weighted average of the first, second and third score to compute an interestingness score for the selected content item; and presenting the selected content item to the first time visitors of the website when it is determined that the interestingness score calculated for the selected content item is greater than a threshold.
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1. A computer implemented method, comprising: selecting a content item from a plurality of content that is candidate for presentation to first time visitors of a website; identifying all topics present in the selected content item; scoring each identified topic; associating each scored topic with at least one subject matter category; calculating a first score associated with the selected content item based on an intercategory topic density determined for the selected content item; calculating a second score associated with the selected content item based on a topic density determined for the selected content item; calculating a third score associated with the selected content item based on a presentability of the selected content item; calculating a weighted average of the first, second and third score to compute an interestingness score for the selected content item; and presenting the selected content item to the first time visitors of the website when it is determined that the interestingness score calculated for the selected content item is greater than a threshold. 9. The method of claim 1 , wherein calculating the first score includes identifying a number of topics present within the selected content item that are each associated with a plurality of subject matter categories.
| 0.782389 |
9,323,720 | 1 | 12 |
1. A method for unifying a fragmented document comprising: identifying structural information elements of a root document, wherein the structural information elements comprise at least one reference to a discrete document other than the root document; presenting to a user, the identified structural information elements within a rapid selection interface for selective acquisition of content from the discrete document; receiving at the rapid selection interface, a user initiated unification command including a user selection of one or more of the presented structural information elements; responsive to said unification command, acquiring content represented by the at least one reference from the discrete document without presenting the discrete document within a user interface window; and adding the acquired content to the root document.
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1. A method for unifying a fragmented document comprising: identifying structural information elements of a root document, wherein the structural information elements comprise at least one reference to a discrete document other than the root document; presenting to a user, the identified structural information elements within a rapid selection interface for selective acquisition of content from the discrete document; receiving at the rapid selection interface, a user initiated unification command including a user selection of one or more of the presented structural information elements; responsive to said unification command, acquiring content represented by the at least one reference from the discrete document without presenting the discrete document within a user interface window; and adding the acquired content to the root document. 12. The method of claim 1 , further comprising: executing a client-side program, that receives the unification command, acquires the content, and adds the acquired content to the root document.
| 0.906037 |
9,594,824 | 1 | 10 |
1. A method, comprising: receiving, by a processor implemented recommender manager, a query identifying a source entity, the source entity being of a first entity-type; generating, by the recommender manager, a plurality of candidate entities from an analysis of an entity-relationship graph in response to the query based on the source entity; computing, by the recommender manager, feature values for each candidate entity of the plurality of candidate entities by passing the source entity and the plurality of candidate entities to a type-specific entity recommender particular to the first entity-type; computing, by the recommender manager, an aggregated score for each candidate entity by combining all of the feature values for each candidate entity; generating, by the recommender manager, a plurality of ranked candidate entities by ranking each candidate entity in accordance with the computed aggregate score corresponding to that candidate entity to represent complex interactions between the plurality of candidate entities and leverage the complex interactions in the ranking; and identifying, by the recommender manager, entity and relationship events that alter the entity-relationship graph by monitoring the entity-relationship graph.
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1. A method, comprising: receiving, by a processor implemented recommender manager, a query identifying a source entity, the source entity being of a first entity-type; generating, by the recommender manager, a plurality of candidate entities from an analysis of an entity-relationship graph in response to the query based on the source entity; computing, by the recommender manager, feature values for each candidate entity of the plurality of candidate entities by passing the source entity and the plurality of candidate entities to a type-specific entity recommender particular to the first entity-type; computing, by the recommender manager, an aggregated score for each candidate entity by combining all of the feature values for each candidate entity; generating, by the recommender manager, a plurality of ranked candidate entities by ranking each candidate entity in accordance with the computed aggregate score corresponding to that candidate entity to represent complex interactions between the plurality of candidate entities and leverage the complex interactions in the ranking; and identifying, by the recommender manager, entity and relationship events that alter the entity-relationship graph by monitoring the entity-relationship graph. 10. The method of claim 1 , wherein the complex interactions include a plurality of entity types being involved in a plurality of interactions that together represent collaborative, semantic, and schematic metadata.
| 0.618794 |
8,682,671 | 17 | 18 |
17. The method of claim 15 , wherein the first and second text strings represent different numerical fields within a larger text string.
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17. The method of claim 15 , wherein the first and second text strings represent different numerical fields within a larger text string. 18. The method of claim 17 , wherein the numerical fields are selected from the group consisting of: currency fields, date fields, digit sequence fields, number fields, fractional number fields, ordinal number fields, telephone number fields, flight number fields, street number fields, time fields, and zipcode fields.
| 0.5 |
8,311,737 | 1 | 2 |
1. A computer-implemented method for automatically generating a map, the method comprising: determining, by a computer, a frequency with which each of a plurality of city names occurs in a corpus of documents; associating, by the computer, each city name with one of a plurality of city categories according to the determined frequency of the city name in the corpus; and generating, by the computer a digital map including the city names, wherein an appearance of each city name on the map is determined by the category with which it is associated.
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1. A computer-implemented method for automatically generating a map, the method comprising: determining, by a computer, a frequency with which each of a plurality of city names occurs in a corpus of documents; associating, by the computer, each city name with one of a plurality of city categories according to the determined frequency of the city name in the corpus; and generating, by the computer a digital map including the city names, wherein an appearance of each city name on the map is determined by the category with which it is associated. 2. The computer-implemented method of claim 1 wherein the appearance on the map of city names associated with a first category is larger than the appearance on the map of city names associated with a second category.
| 0.706522 |
8,626,784 | 9 | 10 |
9. A system for performing a method for model-based searching to provide semantically relevant search results, the system comprising: a processor coupled to a memory that retains computer-executable instructions, wherein the processor executes a method comprising: providing a model that describes a search framework that is useable with each of a plurality of predefined search topics, the search framework including a plurality of model-based search queries that are executable by a computing device via one or more search engines, the plurality of model-based search queries being predefined and including one or more fields in which to insert one or more search terms and one or more search-term attributes, the one or more search terms being included in a list of search terms that comprise at least one search-term attribute of the one or more search-term attributes for a search topic, and the at least one search-term attribute of the one or more search-term attributes being descriptive of the search topic; compiling the plurality of model-based search queries for the search topic by inserting the one or more search terms and the one or more search-term attributes into the one or more fields in each of the plurality of model-based search queries to generate a search string for each of the plurality of model-based search queries; executing the plurality of model-based search queries using the search strings to obtain a plurality of search results; caching at least a portion of the plurality of search results in a computer memory; receiving from a user at a computing device, a search query containing the one or more search terms; retrieving the portion of the plurality of search results that are cached in the computer memory; receiving an additional search-term attribute that describes the search topic from a third party; compiling one or more additional model-based search queries by inserting the additional search-term attribute into one or more of the fields of one or more model-based search queries of the plurality of model-based search queries; and executing the one or more additional model-based search queries to obtain an additional search result; organizing the portion of the plurality of search results and the additional search result based on the model, the model being useable with a plurality of categories, wherein the plurality of categories are based on the model; and presenting the portion of the plurality of search results and the additional search result that are organized based on the model to the user.
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9. A system for performing a method for model-based searching to provide semantically relevant search results, the system comprising: a processor coupled to a memory that retains computer-executable instructions, wherein the processor executes a method comprising: providing a model that describes a search framework that is useable with each of a plurality of predefined search topics, the search framework including a plurality of model-based search queries that are executable by a computing device via one or more search engines, the plurality of model-based search queries being predefined and including one or more fields in which to insert one or more search terms and one or more search-term attributes, the one or more search terms being included in a list of search terms that comprise at least one search-term attribute of the one or more search-term attributes for a search topic, and the at least one search-term attribute of the one or more search-term attributes being descriptive of the search topic; compiling the plurality of model-based search queries for the search topic by inserting the one or more search terms and the one or more search-term attributes into the one or more fields in each of the plurality of model-based search queries to generate a search string for each of the plurality of model-based search queries; executing the plurality of model-based search queries using the search strings to obtain a plurality of search results; caching at least a portion of the plurality of search results in a computer memory; receiving from a user at a computing device, a search query containing the one or more search terms; retrieving the portion of the plurality of search results that are cached in the computer memory; receiving an additional search-term attribute that describes the search topic from a third party; compiling one or more additional model-based search queries by inserting the additional search-term attribute into one or more of the fields of one or more model-based search queries of the plurality of model-based search queries; and executing the one or more additional model-based search queries to obtain an additional search result; organizing the portion of the plurality of search results and the additional search result based on the model, the model being useable with a plurality of categories, wherein the plurality of categories are based on the model; and presenting the portion of the plurality of search results and the additional search result that are organized based on the model to the user. 10. The system of claim 9 , the method further comprises: receiving one or more user-context data items that describe one or more of a user' s preferences, demographics, search history, and browser history; and identifying the model based at least partially on the one or more user-context data items, the one or more user-context data items indicating a search result type, and the model being configured to obtain the portion of the plurality of search results of the search result type indicated by the user-context data items.
| 0.5 |
8,041,729 | 18 | 23 |
18. A volatile or non-volatile computer-readable storage medium storing one or more sequences of instructions which, when executed by one or more computing devices, cause the one or more computing devices to perform: associating a category to a set of nodes of a graph by: determining a first node that represents a first term that is in the category; locating a second node associated with the first node based at least in part on a first degree of cross-reference between the first node and the second node, the second node representing a second term, wherein the first degree of cross-reference is based at least in part on a frequency by which the first term appears in a set of documents with the second term; locating a third node associated with the second node based at least in part on a second degree of cross-reference between the second node and the third node, the third node representing a third term, wherein the second degree of cross-reference is based at least in part on a frequency by which the second term appears in a set of documents with the third term; based at least in part on both (a) the first degree of cross-reference between the first node and the second node, and (b) the second degree of cross-reference between the second node and the third node, determining whether or not the third term is in the category; in response to determining that the third term is in the category, storing information that indicates the third term is in the category.
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18. A volatile or non-volatile computer-readable storage medium storing one or more sequences of instructions which, when executed by one or more computing devices, cause the one or more computing devices to perform: associating a category to a set of nodes of a graph by: determining a first node that represents a first term that is in the category; locating a second node associated with the first node based at least in part on a first degree of cross-reference between the first node and the second node, the second node representing a second term, wherein the first degree of cross-reference is based at least in part on a frequency by which the first term appears in a set of documents with the second term; locating a third node associated with the second node based at least in part on a second degree of cross-reference between the second node and the third node, the third node representing a third term, wherein the second degree of cross-reference is based at least in part on a frequency by which the second term appears in a set of documents with the third term; based at least in part on both (a) the first degree of cross-reference between the first node and the second node, and (b) the second degree of cross-reference between the second node and the third node, determining whether or not the third term is in the category; in response to determining that the third term is in the category, storing information that indicates the third term is in the category. 23. The volatile or non-volatile computer-readable storage medium of claim 18 , wherein the threshold is a first threshold, wherein the one or more sequences of instructions, when executed by the one or more processors, cause the one or more processors to perform the step of determining whether or not the third term is in the category based at least in part on a multiplicative combination of: the first degree of cross-reference between the second node and the first node; and a second degree of cross-reference between the third node and the second node; wherein the one or more sequences of instructions, when executed by the one or more processors, cause determining whether or not the third term is in the category at least in part by determining whether the multiplicative combination satisfies a threshold.
| 0.510804 |
7,559,015 | 10 | 16 |
10. A data processing method, performed by a computer system, for processing media content comprised of a plurality of scenes, said method comprising: inputting context description data including a plurality of segments each for describing one of said plurality of scenes of media content, said context description data including: a context attribute having a value for describing a context of said media content, and a plurality of importance attributes each associated with one of said segments and having a value representing a degree of contextual importance of said corresponding one of said segments; and outputting at least one of said segments based on at least one of said importance attributes, wherein said context description data further includes a plurality of time attributes each associated with a corresponding one of said segments for determining a start time and one of an end time or a duration of the scene associated with said corresponding segment.
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10. A data processing method, performed by a computer system, for processing media content comprised of a plurality of scenes, said method comprising: inputting context description data including a plurality of segments each for describing one of said plurality of scenes of media content, said context description data including: a context attribute having a value for describing a context of said media content, and a plurality of importance attributes each associated with one of said segments and having a value representing a degree of contextual importance of said corresponding one of said segments; and outputting at least one of said segments based on at least one of said importance attributes, wherein said context description data further includes a plurality of time attributes each associated with a corresponding one of said segments for determining a start time and one of an end time or a duration of the scene associated with said corresponding segment. 16. The data processing method according to claim 10 , wherein a plurality of context attributes and a plurality of importance attributes are associated with one segment.
| 0.559585 |
7,536,475 | 7 | 9 |
7. The ACM in accordance with claim 2 wherein said SOAP/XML server configured to transfer ACM data from said ACM CPU and embed said ACM data within said SOAP/XML response based on function tags embedded within said SOAP/XML request.
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7. The ACM in accordance with claim 2 wherein said SOAP/XML server configured to transfer ACM data from said ACM CPU and embed said ACM data within said SOAP/XML response based on function tags embedded within said SOAP/XML request. 9. The ACM in accordance with claim 7 wherein said SOAP/XML request/response comprising at least one of hypertext transfer protocol (HTTP), hypertext markup language (HTML), and references to other files.
| 0.5 |
9,152,722 | 21 | 23 |
21. The non-transitory computer readable storage medium of claim 20 , wherein generating the first metadata item comprises: extracting text from the activity history; and generating the first metadata item based upon the text.
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21. The non-transitory computer readable storage medium of claim 20 , wherein generating the first metadata item comprises: extracting text from the activity history; and generating the first metadata item based upon the text. 23. The non-transitory computer readable storage medium of claim 21 , wherein generating the first metadata item based upon the text comprises: using a taxonomical lookup to generate a taxonomical category of the text; and generating the first metadata item based upon the taxonomical category.
| 0.5 |
9,626,685 | 7 | 11 |
7. The method of claim 1 further comprising: collecting data indicative of user actions from a plurality of user proxy devices; and generating an entity rank for each physical entity of the subset of physical entities based on data from the user proxy devices.
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7. The method of claim 1 further comprising: collecting data indicative of user actions from a plurality of user proxy devices; and generating an entity rank for each physical entity of the subset of physical entities based on data from the user proxy devices. 11. The method of claim 7 further comprising: identifying a second user action, the data corresponding to the second user action including an identification of an event defined by a second location and a time; determining that the event is a second physical entity; and associating the first user action with the second physical entity.
| 0.552 |
9,535,909 | 2 | 4 |
2. The computer implemented method of claim 1 , wherein scanning the pre-defined rules to select the first pre-defined rule that matches the event criteria further comprises: parsing the first pre-defined rule to identify an associated first condition; and selecting the first pre-defined rule if the first condition matches the event criteria.
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2. The computer implemented method of claim 1 , wherein scanning the pre-defined rules to select the first pre-defined rule that matches the event criteria further comprises: parsing the first pre-defined rule to identify an associated first condition; and selecting the first pre-defined rule if the first condition matches the event criteria. 4. The computer implemented method of claim 2 , wherein the first pre-defined rule includes multiple conditions each having a corresponding job description.
| 0.788618 |
8,527,594 | 1 | 7 |
1. A method for selecting and placing an advertisement, comprising the steps of: (a) defining, by a computer, and storing on a computer storage medium an Ad campaign project model; (b) defining, by a computer, for said Ad campaign advertising messages and targets for placement in blogs according to a plurality of facets; (c) said computer defining one of said facets as a why-facet for each of said advertising message and targets, wherein said why-facet defines the objective of the advertising message and the advertising target; (d) said computer defining one of said facets as a how-facet for each of said advertising message and targets, wherein said how-facet defines the style of said advertising message and said advertising target; (e) said computer defining one of said facets as a what-facet for each of said advertising message and targets, wherein said what-facet defines the type of product or service of said advertising message and said advertising target; (f) said computer defining one of said facets as a who-facet for each of said advertising message and targets, wherein said who-facet defines the type of audience or community for said advertising message and said advertising target; defining keyword features and non-keyword features for each of said why-facet, said how-facet, what-facet and who-facet; (h) said computer receiving a blog content from a blogosphere; (i) said computer analyzing said received blog by extracting keywords and non-keywords from said blog, wherein said extracting non-keywords from said blog is based on said computer performing a combination of.” (i) a structural analysis of said blog, (ii) a stylistic analysis of said blog, and (iii) a linguistic analysis of said blog; (j) said computer classifying from said Ad campagin project model which type of facet of said plurality of said defined facets corresponds to said extracted combined keywords and non-keywords; (k) said computer determining which advertisement message and advertising target to be placed in said blog based on said classified facet; and (l) said computer communicating with said blog the placement of said determined advertisement message and advertising target in said blog, wherein said method does not rely on a demographic description, a survey or a self-declared profile.
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1. A method for selecting and placing an advertisement, comprising the steps of: (a) defining, by a computer, and storing on a computer storage medium an Ad campaign project model; (b) defining, by a computer, for said Ad campaign advertising messages and targets for placement in blogs according to a plurality of facets; (c) said computer defining one of said facets as a why-facet for each of said advertising message and targets, wherein said why-facet defines the objective of the advertising message and the advertising target; (d) said computer defining one of said facets as a how-facet for each of said advertising message and targets, wherein said how-facet defines the style of said advertising message and said advertising target; (e) said computer defining one of said facets as a what-facet for each of said advertising message and targets, wherein said what-facet defines the type of product or service of said advertising message and said advertising target; (f) said computer defining one of said facets as a who-facet for each of said advertising message and targets, wherein said who-facet defines the type of audience or community for said advertising message and said advertising target; defining keyword features and non-keyword features for each of said why-facet, said how-facet, what-facet and who-facet; (h) said computer receiving a blog content from a blogosphere; (i) said computer analyzing said received blog by extracting keywords and non-keywords from said blog, wherein said extracting non-keywords from said blog is based on said computer performing a combination of.” (i) a structural analysis of said blog, (ii) a stylistic analysis of said blog, and (iii) a linguistic analysis of said blog; (j) said computer classifying from said Ad campagin project model which type of facet of said plurality of said defined facets corresponds to said extracted combined keywords and non-keywords; (k) said computer determining which advertisement message and advertising target to be placed in said blog based on said classified facet; and (l) said computer communicating with said blog the placement of said determined advertisement message and advertising target in said blog, wherein said method does not rely on a demographic description, a survey or a self-declared profile. 7. The method as set forth in claim 1 , wherein said type of audience or community as defined by said who-facet labels are labelled by said computer as: (i) an expert audience or community or (ii) a novice audience or community.
| 0.5 |
9,275,079 | 10 | 11 |
10. The computer readable storage medium of claim 7 , wherein the mobile device is a user-wearable computing device in the form of eyeglasses to render the augmentation data over a user's view of the object.
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10. The computer readable storage medium of claim 7 , wherein the mobile device is a user-wearable computing device in the form of eyeglasses to render the augmentation data over a user's view of the object. 11. The computer readable storage medium of claim 10 , wherein the digital image data is a live video feed.
| 0.5 |
7,962,325 | 1 | 7 |
1. A server device which can provide a client device with information in a plurality of languages, the device comprising: a central processing unit (CPU), and a memory, the memory including a supportable language recognizing unit recognizing a plurality of supportable languages using language identification information; a requested language recognizing unit recognizing a requested language desired by the client device using the language identification information; an attribute information acquiring unit acquiring attribute information associated with the language identification information; and a language selecting unit selecting one supportable language from the plurality of supportable languages as a support language in which information is provided to the client device, using the attribute information when the language identification information of the requested language does not match with that of any of the plurality of supportable languages; wherein the language identification information includes at least a first identifier, the attribute information includes priority information for determining a priority order of the supportable languages of which the first identifiers are the same as each other, and the language selecting unit selects the support language from the plurality of supportable languages of which the first identifiers match with that of the requested language on the basis of the priority order when there is no supportable language of which the language identification information matches with the language identification information of the requested language but there is the supportable languages of which the first identifiers match with that of the requested language; wherein the language identification information is preliminarily classified into a plurality of groups, the attribute information includes first group information for specifying a group to which the requested language belongs, and the language selecting unit selects the support language from the supportable languages depending on the group to which the requested language belongs.
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1. A server device which can provide a client device with information in a plurality of languages, the device comprising: a central processing unit (CPU), and a memory, the memory including a supportable language recognizing unit recognizing a plurality of supportable languages using language identification information; a requested language recognizing unit recognizing a requested language desired by the client device using the language identification information; an attribute information acquiring unit acquiring attribute information associated with the language identification information; and a language selecting unit selecting one supportable language from the plurality of supportable languages as a support language in which information is provided to the client device, using the attribute information when the language identification information of the requested language does not match with that of any of the plurality of supportable languages; wherein the language identification information includes at least a first identifier, the attribute information includes priority information for determining a priority order of the supportable languages of which the first identifiers are the same as each other, and the language selecting unit selects the support language from the plurality of supportable languages of which the first identifiers match with that of the requested language on the basis of the priority order when there is no supportable language of which the language identification information matches with the language identification information of the requested language but there is the supportable languages of which the first identifiers match with that of the requested language; wherein the language identification information is preliminarily classified into a plurality of groups, the attribute information includes first group information for specifying a group to which the requested language belongs, and the language selecting unit selects the support language from the supportable languages depending on the group to which the requested language belongs. 7. The server device according to claim 1 , wherein the language identification information includes at least a second identifier and the first group information is associated depending on the second identifiers.
| 0.855191 |
9,246,858 | 17 | 18 |
17. A computing system, comprising: a processor of a device of a user; an optical sensor in communication with the processor and configured for capturing, by a device of a user, an image of a machine-readable symbol encoding an identifier, the identifier identifying a locale associated with the machine-readable symbol; and a capture program executed by the processor and configured for: processing the captured image to discern the identifier encoded into the machine-readable symbol; presenting for display on the device an interface for receiving content from the user relating to the locale associated with the machine-readable symbol; and receiving, from the user at the device, content relating to the locale.
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17. A computing system, comprising: a processor of a device of a user; an optical sensor in communication with the processor and configured for capturing, by a device of a user, an image of a machine-readable symbol encoding an identifier, the identifier identifying a locale associated with the machine-readable symbol; and a capture program executed by the processor and configured for: processing the captured image to discern the identifier encoded into the machine-readable symbol; presenting for display on the device an interface for receiving content from the user relating to the locale associated with the machine-readable symbol; and receiving, from the user at the device, content relating to the locale. 18. The computing system of claim 17 , wherein the capture program is further configured for sending an indication to a server that the user of the device captured the machine-readable symbol, the indication including a user identifier linking the user to a user profile on the server.
| 0.5 |
7,546,316 | 8 | 35 |
8. A system comprising: at least one processor configured to execute: a heuristics module for modifying a query string of characters using a set of heuristics; a comparator module, coupled to the heuristics module, for performing a character-by-character comparison of the modified query string with at least one known string of characters in a corpus in order to find an exact match for the modified query string; a sub-string formation and information retrieval module for locating an equivalent for the modified query string, responsive to the comparator module not finding an exact match for the modified query string in the corpus, said sub-string formation module, coupled to the comparator module, for forming a plurality of sub-strings of characters from the modified query string, the sub-strings having varying lengths such that at least two of the formed sub-strings differ in length, each sub-string comprising a composition of characters selected based on a frequency of occurrence of the composition in the modified query string; and said information retrieval module, coupled to the sub-string formation module, for performing an information retrieval technique on the sub-strings formed from the modified query string to identify a known string of characters equivalent to the query string.
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8. A system comprising: at least one processor configured to execute: a heuristics module for modifying a query string of characters using a set of heuristics; a comparator module, coupled to the heuristics module, for performing a character-by-character comparison of the modified query string with at least one known string of characters in a corpus in order to find an exact match for the modified query string; a sub-string formation and information retrieval module for locating an equivalent for the modified query string, responsive to the comparator module not finding an exact match for the modified query string in the corpus, said sub-string formation module, coupled to the comparator module, for forming a plurality of sub-strings of characters from the modified query string, the sub-strings having varying lengths such that at least two of the formed sub-strings differ in length, each sub-string comprising a composition of characters selected based on a frequency of occurrence of the composition in the modified query string; and said information retrieval module, coupled to the sub-string formation module, for performing an information retrieval technique on the sub-strings formed from the modified query string to identify a known string of characters equivalent to the query string. 35. The system of claim 8 , wherein the length of a sub-string is determined based on one or more character sequences identified in the modified query string and a corresponding frequency of occurrence for each identified character sequence in the modified query string.
| 0.602941 |
9,020,212 | 11 | 12 |
11. A computer-readable storage medium storing computer-executable instructions that when executed by at least one processor cause the at least one processor to perform a method within a computing system, the method comprising: receiving a collection of web pages containing a plurality of images; for each of the images, identifying a set of names associated with the image based on a text analysis of at least one of the web pages; generating a plurality of face clusters based on face detection and clustering performed on the plurality of images, wherein each of the face clusters is associated with one person and includes a set of at least one image from the plurality of images in which a face of the one person was detected; for each of the face clusters, identifying a label for the face cluster based on the set of names associated with each image in the face cluster; and determining a name of a first person appearing in at least one of the plurality of images based on the identified label for one of the face clusters associated with the first person.
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11. A computer-readable storage medium storing computer-executable instructions that when executed by at least one processor cause the at least one processor to perform a method within a computing system, the method comprising: receiving a collection of web pages containing a plurality of images; for each of the images, identifying a set of names associated with the image based on a text analysis of at least one of the web pages; generating a plurality of face clusters based on face detection and clustering performed on the plurality of images, wherein each of the face clusters is associated with one person and includes a set of at least one image from the plurality of images in which a face of the one person was detected; for each of the face clusters, identifying a label for the face cluster based on the set of names associated with each image in the face cluster; and determining a name of a first person appearing in at least one of the plurality of images based on the identified label for one of the face clusters associated with the first person. 12. The computer-readable storage medium of claim 11 , wherein the method further comprises: for each of the face clusters, determining an intersection set of names based on a comparison of the set of names associated with each image in the face cluster, wherein the intersection set of names for each face cluster is used as the label for the face cluster.
| 0.571942 |
8,761,381 | 11 | 13 |
11. The system of claim 10 , wherein the plurality of call centers includes a how-to-use call center, and wherein identifying the routing destinations includes identifying a how-to-use destination in the how-to-use call center.
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11. The system of claim 10 , wherein the plurality of call centers includes a how-to-use call center, and wherein identifying the routing destinations includes identifying a how-to-use destination in the how-to-use call center. 13. The system of claim 11 , wherein the how-to-use destination includes a set of verbal prompts, voice recognition entry points, and messages.
| 0.553125 |
8,799,295 | 11 | 19 |
11. A non-transitory computer-readable storage medium storing a plurality of instructions which, when executed by a processor, cause the processor to perform a method comprising: determining a number of equivalent top-level domain names corresponding to the received domain name that are registered in the domain name system; assigning a first sub-score based on the number of equivalent top-level domain names registered; determining a number of related domain names that are registered in the domain name system; assigning a second sub-score based on the number related domain names registered in the domain name system; automatically collecting registration data for the received domain name; assigning a third sub-score based on the automatically-collected registration data; generating a domain name score for the received domain name, the domain name score being determined from the first, second, and third sub-scores; identifying a technique for increasing at least one of the first, second, and third subscores; and providing the domain name score and the identified technique to the user.
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11. A non-transitory computer-readable storage medium storing a plurality of instructions which, when executed by a processor, cause the processor to perform a method comprising: determining a number of equivalent top-level domain names corresponding to the received domain name that are registered in the domain name system; assigning a first sub-score based on the number of equivalent top-level domain names registered; determining a number of related domain names that are registered in the domain name system; assigning a second sub-score based on the number related domain names registered in the domain name system; automatically collecting registration data for the received domain name; assigning a third sub-score based on the automatically-collected registration data; generating a domain name score for the received domain name, the domain name score being determined from the first, second, and third sub-scores; identifying a technique for increasing at least one of the first, second, and third subscores; and providing the domain name score and the identified technique to the user. 19. The computer readable storage medium of claim 11 , wherein the suggested at least one additional domain name has a higher domain name score than the domain name score generated for the received domain name.
| 0.65798 |
9,639,504 | 16 | 17 |
16. The method of claim 12 , comprising: storing the default template to the generated template document; and storing the selected subsequent page design to the modified template.
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16. The method of claim 12 , comprising: storing the default template to the generated template document; and storing the selected subsequent page design to the modified template. 17. The method of claim 16 , comprising retrieving the stored default template.
| 0.579787 |
8,965,894 | 7 | 9 |
7. A method for classification of a web page, the method comprising: generating a plurality of tokens by evaluating a portion of the web page, wherein the portion of the web page consists of a uniform resource locator (URL) of the web page, a title of the web page, and at least one meta tag associated with the web page, and wherein each of the plurality of tokens is a string between predefined delimiters in at least one of the URL of the web page, the title of the web page, and the at least one meta tag associated with the web page; combining the stemmed tokens; determining and removing redundant tokens from the combination of the stemmed tokens; after the removal of the redundant tokens, storing remaining tokens as metadata of the web page; and classifying the web page based on the stored metadata of the web page.
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7. A method for classification of a web page, the method comprising: generating a plurality of tokens by evaluating a portion of the web page, wherein the portion of the web page consists of a uniform resource locator (URL) of the web page, a title of the web page, and at least one meta tag associated with the web page, and wherein each of the plurality of tokens is a string between predefined delimiters in at least one of the URL of the web page, the title of the web page, and the at least one meta tag associated with the web page; combining the stemmed tokens; determining and removing redundant tokens from the combination of the stemmed tokens; after the removal of the redundant tokens, storing remaining tokens as metadata of the web page; and classifying the web page based on the stored metadata of the web page. 9. The method as claimed in claim 7 , wherein the classifying the web page includes categorizing the web page in more than one category.
| 0.507246 |
9,454,597 | 1 | 3 |
1. A document management and retrieval system, comprising: (a) one or more processors; and (b) a non-transitory storage medium coupled to the one or more processors and containing instructions executable by the one or more processors such that when the instructions are executed on the one or more processors, one or more of the processors will: store appearance positions for a plurality of words in one or more of a plurality of documents in a word index store; store a plurality of tag indexes and an appearance position of the character string associated with each tag in a set of documents in a tag index store, each tag index being associated with a respective character string and comprising: a tag associated with its respective character string in one of the plurality of documents, the tag having a name and indicating attributes of the character string based upon the meaning of the character string; at least one of a right word string comprising one or more adjacent words that appears to the right of its respective character string and a left word string comprising one or more adjacent words that appears to the left of its respective character string; a combination of the tag with at least one of the left and right word strings; and operate as a document retrieval unit that receives as a search query an input of a phrase containing a search tag name and a search word, and returns a list of identified documents that contain the phrase by utilizing the combination entries stored in the tag index store; operate as a tag update unit that interprets a query for associating or disassociating a tag to or from a character string in a document and updates the associated tag index stored in the tag index store; and operate as a document index creating unit that updates a word index within the word index store when one or more documents are identified; and (c) a bit string store that stores a plurality of a bit strings, each bit string representing a set of documents of the plurality of documents that contain a high-frequency word or a set of documents that contain a specified tag, wherein: the tag update unit updating a bit string in the bit string store when a tag is associated or disassociated to or from a character string in a document; and the document retrieval unit using the high-frequency word and tag name contained in a query to refer to the bit string store prior to a search and to obtain a set of documents that contain all high-frequency words and tag names in the query.
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1. A document management and retrieval system, comprising: (a) one or more processors; and (b) a non-transitory storage medium coupled to the one or more processors and containing instructions executable by the one or more processors such that when the instructions are executed on the one or more processors, one or more of the processors will: store appearance positions for a plurality of words in one or more of a plurality of documents in a word index store; store a plurality of tag indexes and an appearance position of the character string associated with each tag in a set of documents in a tag index store, each tag index being associated with a respective character string and comprising: a tag associated with its respective character string in one of the plurality of documents, the tag having a name and indicating attributes of the character string based upon the meaning of the character string; at least one of a right word string comprising one or more adjacent words that appears to the right of its respective character string and a left word string comprising one or more adjacent words that appears to the left of its respective character string; a combination of the tag with at least one of the left and right word strings; and operate as a document retrieval unit that receives as a search query an input of a phrase containing a search tag name and a search word, and returns a list of identified documents that contain the phrase by utilizing the combination entries stored in the tag index store; operate as a tag update unit that interprets a query for associating or disassociating a tag to or from a character string in a document and updates the associated tag index stored in the tag index store; and operate as a document index creating unit that updates a word index within the word index store when one or more documents are identified; and (c) a bit string store that stores a plurality of a bit strings, each bit string representing a set of documents of the plurality of documents that contain a high-frequency word or a set of documents that contain a specified tag, wherein: the tag update unit updating a bit string in the bit string store when a tag is associated or disassociated to or from a character string in a document; and the document retrieval unit using the high-frequency word and tag name contained in a query to refer to the bit string store prior to a search and to obtain a set of documents that contain all high-frequency words and tag names in the query. 3. The document management and retrieval system of claim 1 , wherein the attribute indicated by at least one of the tags is one of a person's name, a company name and a location name.
| 0.862613 |
5,510,981 | 2 | 10 |
2. An apparatus as claimed in claim 1, characterized in that each target hypothesis comprises a series of target words selected from a vocabulary comprising words in the second language and a null word representing the absence of a word.
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2. An apparatus as claimed in claim 1, characterized in that each target hypothesis comprises a series of target words selected from a vocabulary comprising words in the second language and a null word representing the absence of a word. 10. An apparatus as claimed in claim 2, characterized in that the input means comprises a keyboard.
| 0.96317 |
10,073,840 | 8 | 9 |
8. A computer readable storage device containing computer executable instructions which, when executed by a computer, perform a method for training a relation detection model without supervision, the method comprising: selecting a relation from a knowledge graph; extracting at least a first pair of words from the knowledge graph, wherein the first pair of words is connected by the relation; receiving a set of documents as a search result based on a first query, wherein the first query comprises at least one instruction to select documents based on the first pair of words; extracting, from the set of documents, at least one textual snippet based on the first query, wherein the at least one textual snippet includes at least in part the first pair of words; extracting a second query from a query click log, wherein the query click log comprises at least one search query against at least a part of the set of documents and at least one link to at least one document, and wherein the second query is associated with at least one link to the at least one document containing the at least one textual snippet; generating a first set of training patterns, wherein the first set of training patterns is based on association between the at least one textual snippet and the relation; generating a second set of training patterns, wherein the second set of training patterns is based on association between the second query and the relation; generating a third set of natural language patterns for the knowledge graph, wherein generating the set of natural language patterns further comprises selectively combining the first set of training patterns and the second set of training patterns based on at least one weight between the first set of training patterns and the second set of training patterns; and applying the generated third set of natural language patterns to the knowledge graph to automatically train a natural language dialog system.
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8. A computer readable storage device containing computer executable instructions which, when executed by a computer, perform a method for training a relation detection model without supervision, the method comprising: selecting a relation from a knowledge graph; extracting at least a first pair of words from the knowledge graph, wherein the first pair of words is connected by the relation; receiving a set of documents as a search result based on a first query, wherein the first query comprises at least one instruction to select documents based on the first pair of words; extracting, from the set of documents, at least one textual snippet based on the first query, wherein the at least one textual snippet includes at least in part the first pair of words; extracting a second query from a query click log, wherein the query click log comprises at least one search query against at least a part of the set of documents and at least one link to at least one document, and wherein the second query is associated with at least one link to the at least one document containing the at least one textual snippet; generating a first set of training patterns, wherein the first set of training patterns is based on association between the at least one textual snippet and the relation; generating a second set of training patterns, wherein the second set of training patterns is based on association between the second query and the relation; generating a third set of natural language patterns for the knowledge graph, wherein generating the set of natural language patterns further comprises selectively combining the first set of training patterns and the second set of training patterns based on at least one weight between the first set of training patterns and the second set of training patterns; and applying the generated third set of natural language patterns to the knowledge graph to automatically train a natural language dialog system. 9. The computer readable storage device of claim 8 wherein associating the at least one textual snippet further comprises: selecting the smallest sequence of constituent elements in the at least one textual snippet that contains both words from the pair as the set of natural language patterns; and replacing the both words in the set of natural language patterns with tokens from the knowledge graph corresponding to each of both words.
| 0.5 |
9,225,745 | 9 | 10 |
9. A computing system comprising: a communications interface operable to receive a message, the message including a request to perform an action within a proposal system implemented in the computing system, the proposal system being operable to create a request for proposals based on user input, the request for proposals describing a business need associated with a business entity, the proposal system being further operable to process a plurality of proposal documents received in response to the request for proposals, the request being associated with a user account, wherein the plurality of proposal documents correspond to plans addressing the business need associated with the business entity; memory operable to store the received message; and a processor operable to: determine whether the requested action complies with an access policy, wherein the requested action comprises a request to create a designated proposal document in response to the request for proposals, and perform the requested action when it is determined that the requested action complies with the access policy, wherein performing the requested action comprises creating the designated proposal document based on user input and suggested content, the suggested content being determined by the proposal system, wherein the user account is a member of a first user account group, wherein the access policy identifies a first one or more permitted actions associated with the first user account group, and wherein determining whether the requested action complies with the access policy comprises determining whether the first one or more permitted actions includes the requested action, wherein the user account is a member of a second user account group, and wherein the access policy identifies a second one or more permitted actions associated with the second user account group, wherein determining whether the requested action complies with the access policy further comprises determining whether the second one or more permitted actions includes the requested action, and wherein the requested action is performed when either the first or second one or more permitted actions includes the requested action.
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9. A computing system comprising: a communications interface operable to receive a message, the message including a request to perform an action within a proposal system implemented in the computing system, the proposal system being operable to create a request for proposals based on user input, the request for proposals describing a business need associated with a business entity, the proposal system being further operable to process a plurality of proposal documents received in response to the request for proposals, the request being associated with a user account, wherein the plurality of proposal documents correspond to plans addressing the business need associated with the business entity; memory operable to store the received message; and a processor operable to: determine whether the requested action complies with an access policy, wherein the requested action comprises a request to create a designated proposal document in response to the request for proposals, and perform the requested action when it is determined that the requested action complies with the access policy, wherein performing the requested action comprises creating the designated proposal document based on user input and suggested content, the suggested content being determined by the proposal system, wherein the user account is a member of a first user account group, wherein the access policy identifies a first one or more permitted actions associated with the first user account group, and wherein determining whether the requested action complies with the access policy comprises determining whether the first one or more permitted actions includes the requested action, wherein the user account is a member of a second user account group, and wherein the access policy identifies a second one or more permitted actions associated with the second user account group, wherein determining whether the requested action complies with the access policy further comprises determining whether the second one or more permitted actions includes the requested action, and wherein the requested action is performed when either the first or second one or more permitted actions includes the requested action. 10. The system recited in claim 9 , wherein creating the designated proposal document comprises processing the user input and suggested content to arrange the proposal document on a single page.
| 0.756281 |
7,552,005 | 31 | 33 |
31. A method of analyzing a turbine engine to determine a normal engine condition or a faulty engine condition, said method comprising the steps of: acquiring a plurality of engine operating parameters from the turbine engine under analysis; calculating a corresponding plurality of engine residual values by comparing each of said engine operating parameters with standard engine characteristics obtained from an engine model; computing the mean and the standard deviation of each of said plurality of engine residual values; normalizing each of said plurality of engine residual values by normalizing said mean to zero and by normalizing said standard deviation to unity to yield a plurality of normalized engine residuals, said step of normalizing using normalization factors obtained from a parameter distribution of a normally-operating baseline engine; mapping, via a clustering technique, said normalized engine residuals as input vectors into an engine condition space having a plurality of clusters, each said cluster representing either a normal vector engine condition or a faulty engine vector condition; identifying a closest cluster within said engine condition space, said closest cluster being closer to said input vectors than any other of said plurality of clusters; and determining a normal engine condition for the engine under analysis if said closest cluster represents a normal vector engine condition, and determining a faulty engine condition for the engine under analysis if said closest cluster represents a faulty vector engine condition.
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31. A method of analyzing a turbine engine to determine a normal engine condition or a faulty engine condition, said method comprising the steps of: acquiring a plurality of engine operating parameters from the turbine engine under analysis; calculating a corresponding plurality of engine residual values by comparing each of said engine operating parameters with standard engine characteristics obtained from an engine model; computing the mean and the standard deviation of each of said plurality of engine residual values; normalizing each of said plurality of engine residual values by normalizing said mean to zero and by normalizing said standard deviation to unity to yield a plurality of normalized engine residuals, said step of normalizing using normalization factors obtained from a parameter distribution of a normally-operating baseline engine; mapping, via a clustering technique, said normalized engine residuals as input vectors into an engine condition space having a plurality of clusters, each said cluster representing either a normal vector engine condition or a faulty engine vector condition; identifying a closest cluster within said engine condition space, said closest cluster being closer to said input vectors than any other of said plurality of clusters; and determining a normal engine condition for the engine under analysis if said closest cluster represents a normal vector engine condition, and determining a faulty engine condition for the engine under analysis if said closest cluster represents a faulty vector engine condition. 33. The method of claim 31 wherein said clustering technique mapping comprises a method from the group consisting of self-organizing mapping, fuzzy clustering, adaptive resonance theory, K-means algorithm, and Gaussian mixture method.
| 0.842318 |
7,533,107 | 23 | 39 |
23. A computer program for integrating a plurality of different data sources, the computer program comprising computer executable instructions stored in a computer readable medium that when executed cause the computer to: obtain semantic information from each of the plurality of data sources; create a conceptual model for each of the plurality of data sources using said semantic information; access a secondary knowledge source having information relating the data sources to one another; and create an integrated semantic model using said conceptual models and said secondary knowledge source; wherein said semantic information comprises characterization of at least one of constraints that hold for subsets of data in the plurality of data sources and relationships that hold between the data; wherein said semantic information further comprises information expressing properties of the data that have not been explicitly encoded in an alphanumeric representation of the data or in a syntactic structure that holds together different data elements.
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23. A computer program for integrating a plurality of different data sources, the computer program comprising computer executable instructions stored in a computer readable medium that when executed cause the computer to: obtain semantic information from each of the plurality of data sources; create a conceptual model for each of the plurality of data sources using said semantic information; access a secondary knowledge source having information relating the data sources to one another; and create an integrated semantic model using said conceptual models and said secondary knowledge source; wherein said semantic information comprises characterization of at least one of constraints that hold for subsets of data in the plurality of data sources and relationships that hold between the data; wherein said semantic information further comprises information expressing properties of the data that have not been explicitly encoded in an alphanumeric representation of the data or in a syntactic structure that holds together different data elements. 39. A method as in claim 23 wherein said secondary knowledge source information is not available from any of the plurality of databases.
| 0.838095 |
9,594,828 | 8 | 11 |
8. A non-transitory computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by one or more computing devices, cause the computing devices to perform actions including: transmitting a pilot query to an unstructured data store that stores records in JSON-based format; responsive to transmitting the pilot query, receiving a plurality of records generated by execution of the pilot query against the unstructured data store, each record including one or more pairs of field names and values in JSON-based format; automatically identifying field names and data types from the pairs in the plurality of records generated by execution of the pilot query against the unstructured data store that stores records in JSON-based format and defining a first set of fields that corresponds to the identified field names and data types; receiving, at a query converter, a structured query whose portions all correspond to a structured query language; converting, by the query converter, the structured query into an unstructured query in an unstructured query language associated with searching the records in the unstructured data store, the unstructured query referencing at least one field in the first set of fields; and transmitting an instruction to the unstructured data store requesting that the unstructured data store execute the unstructured query against the records.
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8. A non-transitory computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by one or more computing devices, cause the computing devices to perform actions including: transmitting a pilot query to an unstructured data store that stores records in JSON-based format; responsive to transmitting the pilot query, receiving a plurality of records generated by execution of the pilot query against the unstructured data store, each record including one or more pairs of field names and values in JSON-based format; automatically identifying field names and data types from the pairs in the plurality of records generated by execution of the pilot query against the unstructured data store that stores records in JSON-based format and defining a first set of fields that corresponds to the identified field names and data types; receiving, at a query converter, a structured query whose portions all correspond to a structured query language; converting, by the query converter, the structured query into an unstructured query in an unstructured query language associated with searching the records in the unstructured data store, the unstructured query referencing at least one field in the first set of fields; and transmitting an instruction to the unstructured data store requesting that the unstructured data store execute the unstructured query against the records. 11. The computer-readable storage medium of claim 8 , wherein the pilot query is transmitted before receiving the structured query.
| 0.853139 |
9,984,310 | 1 | 2 |
1. A method, performed by a computing system having a memory and a processor, the method comprising: receiving an indication of a plurality of content items; for each of the plurality of content items, identifying at least one content element of the content item, and for each identified content element of the content item, extracting information for the content element, computing a plurality of semantic feature values for the content element based at least in part on the extracted information, computing a plurality of visual feature values for the content element based at least in part on the extracted information, and storing the feature values computed, for the content element, based at least in part on the extracted information; receiving an indication of a first set of content items, wherein each content item of the first set of content items is a member of the plurality of content items; for each pair of content items from among the first set of content items, applying a similarity function to the pair of content items to generate a similarity value, and storing, in association with each content item of the pair of content items, the generated similarity value; for each content item of the first set of content items, identifying similar content items based at least in part on the generated similarity values, and storing, in association with the content item, references to at least one of the identified similar content items.
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1. A method, performed by a computing system having a memory and a processor, the method comprising: receiving an indication of a plurality of content items; for each of the plurality of content items, identifying at least one content element of the content item, and for each identified content element of the content item, extracting information for the content element, computing a plurality of semantic feature values for the content element based at least in part on the extracted information, computing a plurality of visual feature values for the content element based at least in part on the extracted information, and storing the feature values computed, for the content element, based at least in part on the extracted information; receiving an indication of a first set of content items, wherein each content item of the first set of content items is a member of the plurality of content items; for each pair of content items from among the first set of content items, applying a similarity function to the pair of content items to generate a similarity value, and storing, in association with each content item of the pair of content items, the generated similarity value; for each content item of the first set of content items, identifying similar content items based at least in part on the generated similarity values, and storing, in association with the content item, references to at least one of the identified similar content items. 2. The method of claim 1 , wherein the received indication of the first set of content items comprises a link to at least one content item of the first set of content items.
| 0.904102 |
8,230,016 | 18 | 22 |
18. A system comprising: at least one processor; and a non-transitory computer-readable medium coupled to the at least one processor having instructions stored thereon which, when executed by the at least one processor, causes the at least one processor to: receive an indication that a user selected a user recommendation control in an application running on a user device, the application including a plurality of components; traverse a view hierarchy of the application to determine a view of the application containing the selected user recommendation control; determine that the user recommended at least one of the plurality of components of the application based on the view of the application containing the selected user recommendation control; identify a group of components of the application based on the view of the application containing the selected user recommendation control; query the user to select at least one of the group of components applicable to the recommendation; receive from the user, in response to the query, a selection corresponding to at least one of the group of components; generate at least one social annotation based on the selection received from the user; and serve, via a network, the at least one social annotation to a second user device in a format suitable for presentation on the second user device.
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18. A system comprising: at least one processor; and a non-transitory computer-readable medium coupled to the at least one processor having instructions stored thereon which, when executed by the at least one processor, causes the at least one processor to: receive an indication that a user selected a user recommendation control in an application running on a user device, the application including a plurality of components; traverse a view hierarchy of the application to determine a view of the application containing the selected user recommendation control; determine that the user recommended at least one of the plurality of components of the application based on the view of the application containing the selected user recommendation control; identify a group of components of the application based on the view of the application containing the selected user recommendation control; query the user to select at least one of the group of components applicable to the recommendation; receive from the user, in response to the query, a selection corresponding to at least one of the group of components; generate at least one social annotation based on the selection received from the user; and serve, via a network, the at least one social annotation to a second user device in a format suitable for presentation on the second user device. 22. The system of claim 18 , wherein the at least one processor is further caused to: serve, via the network, the at least one social annotation to the second user device in a format suitable for presentation on the second user device, the at least one social annotation identifying the at least one of the group of components of the application corresponding to the selection received from the user.
| 0.664992 |
9,549,225 | 1 | 11 |
1. A method comprising: identifying, by a processing system comprising a processor, a first group of users of a plurality of users of a social media network based on social media posts associated with media content that are posted by the first group of users; identifying, by the processing system, a second group of users of the plurality of users of the social media network, wherein the second group of users have consumed the media content; identifying, by the processing system, a third group of users of the plurality of users of the social media network, wherein the third group of users have indicated during user interaction with the social media network an interest in commercial items; determining, by the processing system, a first subset of the first group of users that are part of the third group of users and a second subset of the second group of users that are part of the third group of users; generating, by the processing system, a user model that correlates a characteristic associated with the first subset of the first group of users and the second subset of the second group of users; identifying, by the processing system, a target consumer of the third group according to the user model based on a first social media post by the target consumer via a first device utilized by the target consumer and a first media consumption by the target consumer via a second device utilized by the target consumer; predicting, by the processing system, a target media program that the target consumer is likely to view according to the user model; for the target consumer of the target media program, selecting, by the processing system, a target commercial from a set of commercials based on the user model accessing, by the processing system and from a first device used by the target consumer, instructions that pair the first device to a second device used by the target consumer; and transmitting, by the processing system, the target commercial to the second device according to the instructions.
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1. A method comprising: identifying, by a processing system comprising a processor, a first group of users of a plurality of users of a social media network based on social media posts associated with media content that are posted by the first group of users; identifying, by the processing system, a second group of users of the plurality of users of the social media network, wherein the second group of users have consumed the media content; identifying, by the processing system, a third group of users of the plurality of users of the social media network, wherein the third group of users have indicated during user interaction with the social media network an interest in commercial items; determining, by the processing system, a first subset of the first group of users that are part of the third group of users and a second subset of the second group of users that are part of the third group of users; generating, by the processing system, a user model that correlates a characteristic associated with the first subset of the first group of users and the second subset of the second group of users; identifying, by the processing system, a target consumer of the third group according to the user model based on a first social media post by the target consumer via a first device utilized by the target consumer and a first media consumption by the target consumer via a second device utilized by the target consumer; predicting, by the processing system, a target media program that the target consumer is likely to view according to the user model; for the target consumer of the target media program, selecting, by the processing system, a target commercial from a set of commercials based on the user model accessing, by the processing system and from a first device used by the target consumer, instructions that pair the first device to a second device used by the target consumer; and transmitting, by the processing system, the target commercial to the second device according to the instructions. 11. The method of claim 1 , wherein the target commercial is determined based on group level information, wherein the group level information comprises aggregated location information for the plurality of users of the social media network, and wherein a portion of the group level information is obtained without an opt-in user authorization.
| 0.568182 |
8,793,715 | 1 | 5 |
1. A method comprising: dividing a media instance into a first component and a second component, the first component and second component concurrently presented; correlating first physiological response data from a first subject exposed to media with the first component and the second component to form first correlated data and second correlated data; processing, using a processor, the first correlated data to identify a first transition representative of a first change; processing, using the processor, the second correlated data to identify a second transition representative of a second change; parsing the first component into a first plurality of events based on the first transition; parsing the second component into a second plurality of events based on the second transition; identifying a first event of the first plurality of events as a first candidate for modification based on the first change; and identifying a second event of the second plurality of events as a second candidate for modification based on the second change.
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1. A method comprising: dividing a media instance into a first component and a second component, the first component and second component concurrently presented; correlating first physiological response data from a first subject exposed to media with the first component and the second component to form first correlated data and second correlated data; processing, using a processor, the first correlated data to identify a first transition representative of a first change; processing, using the processor, the second correlated data to identify a second transition representative of a second change; parsing the first component into a first plurality of events based on the first transition; parsing the second component into a second plurality of events based on the second transition; identifying a first event of the first plurality of events as a first candidate for modification based on the first change; and identifying a second event of the second plurality of events as a second candidate for modification based on the second change. 5. The method of claim 1 , wherein the first transition is representative of the first change between a positive response and a negative response for the first component and the second transition is representative of the second change between a positive response and a negative response for the second component.
| 0.841141 |
9,607,340 | 19 | 20 |
19. A computer system for analyzing author data, comprising: a computer processor to execute a set of program code instructions; and a memory to hold the program code instructions, in which the program code instructions comprises program code to perform, wherein the program code instructions comprises instructions which, when executed by the computer processor, cause the computer processor at least to: receive writings created by a plurality of authors, perform a semantic analysis upon the writings, generate a plurality of author profiles for the writings using results from the semantic analysis, the plurality of author profiles respectively identifying topics of interest to the plurality of authors, and groups of authors being identified from one or more of the topics of interest, and identify a first group of multiple authors that corresponds to a first topical subject and multiple author profiles for the multiple authors, the first group of multiple authors identified from the groups and corresponding to the multiple author profiles identified from the plurality of author profiles; identify a second topical subject shared among at least some authors of the multiple authors in the first group at least by performing a correlation analysis that analyzes at least some author profiles in the multiple author profiles of the at least some authors; identify a second group of authors from the plurality of authors that exhibit affinity for the second topical subject at least by identifying author vectors corresponding to the second group of authors with respect to the second topical subject; and correlate the first group of multiple authors with the second group of authors in response to the identification of the second topical subject, wherein the writings are received from the plurality of authors without targeting specific groups of authors; classifying the writings into a plurality of classes based in part or in whole upon topics of interests determined by the semantic analysis, classifying the writing including: creating a set of themes from results of the semantic analysis; analyzing the set of themes created from the results of the semantic analysis; determining subjects of the topics of interest based in part or in whole upon the set of themes; determining similarity among the subjects of the topics of interest at least by analyzing the plurality of author profiles; clustering the topics of interests into the plurality of classes based in part or in whole upon the similarity among the subjects; determining respective strength numbers for the plurality of authors, a strength number for a user indicating relative affinity of the user to a category relative to one or more remaining categories; associating the respective strength numbers that correspond to the plurality of authors with a plurality of categories; creating a vector for each author of the plurality of authors, wherein vectors for the plurality of authors indicate respective affinities among the plurality of authors to one or more common topics of interests or one or more subjects; establishing an author profile for the each author by using the vector for the each author; storing the author profile for the author in the plurality of author profiles; reducing noise in the writings at least by performing a semantic filtering process; improving accuracy of the plurality of classes from classifying the writings at least by reducing false positives, false negatives, and inappropriate contents with the semantic filtering process; identifying an actionable data based in part or in whole upon results of the semantic analysis, wherein the writings created by the plurality of authors include contents transcribed from non-social data; determining, at a rule and workflow module stored at least partially in memory, the plurality of computing systems to receive the actionable data based in part or in whole upon a set of rules that identifies how the actionable data is to be handled and directed; performing the semantic analysis upon the writings at least by performing a statistical language modeling; performing the semantic analysis upon the writings at least by performing a latent semantic analysis; preconfiguring a plurality of types of topics of interest; determining a first set of authors that corresponds to the one or more first types of topics of interest at least by analyzing the plurality of author profiles to identify a first set of author profiles corresponding to the first set of authors; determining commonality of one or more second types of topics of interest without pre-defining the one or more second types of topics of interest; identifying commonality among the plurality of writings in response to the one or more second types of topics of interest based in part or in whole upon results of the semantic analysis; identifying a group of authors that corresponds to a first affinity for a first subject; determining a second affinity and a third affinity shared by at least a threshold percentage of authors of the group of authors at least by analyzing a set of author profiles corresponding to the group of authors and by performing one or more first correlation analyses, wherein the second affinity and the third affinity are not known or expected in advance; generating correlation data based in part or in whole upon results of determining the second affinity and the third affinity; generating an action for the group of authors based on the second affinity and the third affinity; receiving the writings created by the plurality of authors without targeting one or more specific groups of authors; generating the plurality of author profiles for the writings based in part or in whole upon respective strength numbers for the plurality of authors; identifying a plurality of themes from the writings based in part or in whole upon results of the semantic analysis and results of classifying the writings; performing a themes analysis; generating the plurality of author profiles for the writing based in part or in whole upon the plurality of themes; determining a first set of actionable data for the plurality of authors based in part or in whole upon results of correlating an at least one group with the authors; identifying a set of rules from a rulebase; dispatching, at a rules and workflow engine, actionable data for the plurality of authors to a plurality of computing systems based in part or in whole upon the set of rules, wherein a rule provides how the actionable data is to be dispatched; determining, at a computer system, contextual and semantic significance in the writings at least by performing classification and filtering on the writings of the plurality of authors; identifying specific themes within the writings based in part or in whole upon topics and subjects revealed from the semantic analysis and the classification; performing categorization on the topics and the subjects of the writings to create a number of categories; associating a set of strength numbers with the number of categories, a strength number indicating relative affinity of each author of the plurality of authors to a particular topic, a particular subject, or a particular theme; and defining a vector for the each author using at least the set of strength numbers and the number of categories, a vector establishing an author profile for a specific author and being used to describe and analyze the specific author with respect to one or more affinities of the specific author.
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19. A computer system for analyzing author data, comprising: a computer processor to execute a set of program code instructions; and a memory to hold the program code instructions, in which the program code instructions comprises program code to perform, wherein the program code instructions comprises instructions which, when executed by the computer processor, cause the computer processor at least to: receive writings created by a plurality of authors, perform a semantic analysis upon the writings, generate a plurality of author profiles for the writings using results from the semantic analysis, the plurality of author profiles respectively identifying topics of interest to the plurality of authors, and groups of authors being identified from one or more of the topics of interest, and identify a first group of multiple authors that corresponds to a first topical subject and multiple author profiles for the multiple authors, the first group of multiple authors identified from the groups and corresponding to the multiple author profiles identified from the plurality of author profiles; identify a second topical subject shared among at least some authors of the multiple authors in the first group at least by performing a correlation analysis that analyzes at least some author profiles in the multiple author profiles of the at least some authors; identify a second group of authors from the plurality of authors that exhibit affinity for the second topical subject at least by identifying author vectors corresponding to the second group of authors with respect to the second topical subject; and correlate the first group of multiple authors with the second group of authors in response to the identification of the second topical subject, wherein the writings are received from the plurality of authors without targeting specific groups of authors; classifying the writings into a plurality of classes based in part or in whole upon topics of interests determined by the semantic analysis, classifying the writing including: creating a set of themes from results of the semantic analysis; analyzing the set of themes created from the results of the semantic analysis; determining subjects of the topics of interest based in part or in whole upon the set of themes; determining similarity among the subjects of the topics of interest at least by analyzing the plurality of author profiles; clustering the topics of interests into the plurality of classes based in part or in whole upon the similarity among the subjects; determining respective strength numbers for the plurality of authors, a strength number for a user indicating relative affinity of the user to a category relative to one or more remaining categories; associating the respective strength numbers that correspond to the plurality of authors with a plurality of categories; creating a vector for each author of the plurality of authors, wherein vectors for the plurality of authors indicate respective affinities among the plurality of authors to one or more common topics of interests or one or more subjects; establishing an author profile for the each author by using the vector for the each author; storing the author profile for the author in the plurality of author profiles; reducing noise in the writings at least by performing a semantic filtering process; improving accuracy of the plurality of classes from classifying the writings at least by reducing false positives, false negatives, and inappropriate contents with the semantic filtering process; identifying an actionable data based in part or in whole upon results of the semantic analysis, wherein the writings created by the plurality of authors include contents transcribed from non-social data; determining, at a rule and workflow module stored at least partially in memory, the plurality of computing systems to receive the actionable data based in part or in whole upon a set of rules that identifies how the actionable data is to be handled and directed; performing the semantic analysis upon the writings at least by performing a statistical language modeling; performing the semantic analysis upon the writings at least by performing a latent semantic analysis; preconfiguring a plurality of types of topics of interest; determining a first set of authors that corresponds to the one or more first types of topics of interest at least by analyzing the plurality of author profiles to identify a first set of author profiles corresponding to the first set of authors; determining commonality of one or more second types of topics of interest without pre-defining the one or more second types of topics of interest; identifying commonality among the plurality of writings in response to the one or more second types of topics of interest based in part or in whole upon results of the semantic analysis; identifying a group of authors that corresponds to a first affinity for a first subject; determining a second affinity and a third affinity shared by at least a threshold percentage of authors of the group of authors at least by analyzing a set of author profiles corresponding to the group of authors and by performing one or more first correlation analyses, wherein the second affinity and the third affinity are not known or expected in advance; generating correlation data based in part or in whole upon results of determining the second affinity and the third affinity; generating an action for the group of authors based on the second affinity and the third affinity; receiving the writings created by the plurality of authors without targeting one or more specific groups of authors; generating the plurality of author profiles for the writings based in part or in whole upon respective strength numbers for the plurality of authors; identifying a plurality of themes from the writings based in part or in whole upon results of the semantic analysis and results of classifying the writings; performing a themes analysis; generating the plurality of author profiles for the writing based in part or in whole upon the plurality of themes; determining a first set of actionable data for the plurality of authors based in part or in whole upon results of correlating an at least one group with the authors; identifying a set of rules from a rulebase; dispatching, at a rules and workflow engine, actionable data for the plurality of authors to a plurality of computing systems based in part or in whole upon the set of rules, wherein a rule provides how the actionable data is to be dispatched; determining, at a computer system, contextual and semantic significance in the writings at least by performing classification and filtering on the writings of the plurality of authors; identifying specific themes within the writings based in part or in whole upon topics and subjects revealed from the semantic analysis and the classification; performing categorization on the topics and the subjects of the writings to create a number of categories; associating a set of strength numbers with the number of categories, a strength number indicating relative affinity of each author of the plurality of authors to a particular topic, a particular subject, or a particular theme; and defining a vector for the each author using at least the set of strength numbers and the number of categories, a vector establishing an author profile for a specific author and being used to describe and analyze the specific author with respect to one or more affinities of the specific author. 20. The system of claim 19 , wherein an author profile comprises a vector comprising values for the topics of interest for an author.
| 0.938197 |
7,603,330 | 1 | 10 |
1. A computer-implemented method for automatically classifying a first question, the method comprising: receiving unlabeled audio or digital text data from an input module, said unlabeled audio or digital text data comprising data that is not previously associated with an expected answer; automatically labeling said unlabeled audio or digital text data using a processor to produce first labeled audio or digital text data associating a first answer with the unlabeled audio or digital text data using a first artificial neural network, said first artificial neural network comprising a first set of weights, said first artificial neural network producing the first labeled audio or digital text data by performing one or more auxiliary tasks analyzing characteristics of said unlabeled audio or digital text data; transferring said first set of weights to a second artificial neural network; receiving second labeled audio or digital text data comprising a second question and a corresponding answer; training said second artificial neural network with the processor using said second labeled audio or digital text data by modifying a second set of weights associated with the second artificial neural network responsive to the second labeled audio or digital text data and freezing the first set of weights; receiving the first question from the input module; and associating a question category with the first question using said second artificial neural network, said question category identifying a source for retrieving text data or audio data describing an answer corresponding to the first question.
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1. A computer-implemented method for automatically classifying a first question, the method comprising: receiving unlabeled audio or digital text data from an input module, said unlabeled audio or digital text data comprising data that is not previously associated with an expected answer; automatically labeling said unlabeled audio or digital text data using a processor to produce first labeled audio or digital text data associating a first answer with the unlabeled audio or digital text data using a first artificial neural network, said first artificial neural network comprising a first set of weights, said first artificial neural network producing the first labeled audio or digital text data by performing one or more auxiliary tasks analyzing characteristics of said unlabeled audio or digital text data; transferring said first set of weights to a second artificial neural network; receiving second labeled audio or digital text data comprising a second question and a corresponding answer; training said second artificial neural network with the processor using said second labeled audio or digital text data by modifying a second set of weights associated with the second artificial neural network responsive to the second labeled audio or digital text data and freezing the first set of weights; receiving the first question from the input module; and associating a question category with the first question using said second artificial neural network, said question category identifying a source for retrieving text data or audio data describing an answer corresponding to the first question. 10. The method of claim 1 , wherein said unlabeled audio or digital text data is contributed by human subjects via distributed data collection.
| 0.845905 |
10,067,740 | 8 | 17 |
8. A system, comprising: a plurality of input devices that provide data corresponding to input modalities; one or more processors programmed to: receive, from the input devices, sets of input data corresponding to the input modalities, the received sets of input data including a first set of input data and a second set of input data, the first set of input data being associated with a first input modality from the plurality of input modalities, the second set of input data being associated with a second input modality from the plurality of input modalities, the first input modality being a speech or text input modality and the second input modality being a gesture input modality; select the first set of input data and the second set of input data; identify, within a dictionary, speech or text input for the first set of input data and a gesture for the second set of input data to determine a meaning of a combination of the first and second set of input data; and provide output data for input by a program, the output data corresponding to the meaning of the first and second set of input data.
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8. A system, comprising: a plurality of input devices that provide data corresponding to input modalities; one or more processors programmed to: receive, from the input devices, sets of input data corresponding to the input modalities, the received sets of input data including a first set of input data and a second set of input data, the first set of input data being associated with a first input modality from the plurality of input modalities, the second set of input data being associated with a second input modality from the plurality of input modalities, the first input modality being a speech or text input modality and the second input modality being a gesture input modality; select the first set of input data and the second set of input data; identify, within a dictionary, speech or text input for the first set of input data and a gesture for the second set of input data to determine a meaning of a combination of the first and second set of input data; and provide output data for input by a program, the output data corresponding to the meaning of the first and second set of input data. 17. The system of claim 8 wherein the one or more processors are further programmed to use preference data in determining the meaning of the first and second set of input data.
| 0.725 |
9,342,504 | 1 | 4 |
1. A voice-interactive dialog system for facilitating conversational speech across different domains, comprising: a phrase database storing (i) phrases grouped into equivalence classes and (ii) a probability of occurrence for each phrase within a corpus; wherein each equivalence class is associated with one or more domains; a user interface configured to allow interaction with an interactive part of the system; a recognition server coupled with the phrase database and the user interface and configured to: identify a user request upon receiving a user utterance via the user interface, wherein the user request is associated with a domain; formulate a system prompt in response to the user utterance, the prompt comprising a combination of one or more phrases from the phrase database that are associated with the domain; upon receiving a user response to the system prompt via the user interface, generate a recognition grammar from phrases in the phrase database that fall within an equivalent class associated with the user response; and generate a recommendation to the user via the user interface, wherein the recommendation is associated with the domain.
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1. A voice-interactive dialog system for facilitating conversational speech across different domains, comprising: a phrase database storing (i) phrases grouped into equivalence classes and (ii) a probability of occurrence for each phrase within a corpus; wherein each equivalence class is associated with one or more domains; a user interface configured to allow interaction with an interactive part of the system; a recognition server coupled with the phrase database and the user interface and configured to: identify a user request upon receiving a user utterance via the user interface, wherein the user request is associated with a domain; formulate a system prompt in response to the user utterance, the prompt comprising a combination of one or more phrases from the phrase database that are associated with the domain; upon receiving a user response to the system prompt via the user interface, generate a recognition grammar from phrases in the phrase database that fall within an equivalent class associated with the user response; and generate a recommendation to the user via the user interface, wherein the recommendation is associated with the domain. 4. The system of claim 1 , wherein the domain is one of banking transactions, news, and weather.
| 0.551402 |
8,954,314 | 1 | 8 |
1. A method comprising: receiving an input in a first language; obtaining a plurality of probable translation alternatives for a translation result, each probable translation alternative being a translation of the input into a second language; presenting a first of the plurality of probable translation alternatives on a display; determining that the device is being shaken; responsive to a determination that the device is being shaken, presenting a second of the plurality of probable translation alternatives in the alternate translation result dialog screen on the display; and responsive to subsequent determinations that the device is being shaken, presenting the alternative translation results in a loop through all of the alternative translation results until the user selects a translation result from the presented list.
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1. A method comprising: receiving an input in a first language; obtaining a plurality of probable translation alternatives for a translation result, each probable translation alternative being a translation of the input into a second language; presenting a first of the plurality of probable translation alternatives on a display; determining that the device is being shaken; responsive to a determination that the device is being shaken, presenting a second of the plurality of probable translation alternatives in the alternate translation result dialog screen on the display; and responsive to subsequent determinations that the device is being shaken, presenting the alternative translation results in a loop through all of the alternative translation results until the user selects a translation result from the presented list. 8. The method of claim 1 , wherein the first of the plurality of probable translation alternatives has a highest probability score of the plurality of probable translation alternatives.
| 0.727139 |
9,172,666 | 17 | 18 |
17. The system of claim 14 , further comprising a language model built from the aggregated messages, the language model based on the content of the aggregated messages from common locations.
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17. The system of claim 14 , further comprising a language model built from the aggregated messages, the language model based on the content of the aggregated messages from common locations. 18. The system of claim 17 , wherein the language model includes a measure of word or phrase frequency for those of the aggregated messages which are from a common location.
| 0.5 |
5,457,783 | 14 | 15 |
14. A speech coder for converting analog speech signals to digital speech signals for transmission, said speech coder comprising: a first filter for filtering out the spectral information from said speech signal and for providing said spectral information for transmission; a second filter for filtering out the pitch information from said speech signal and for providing said pitch information for transmission; a first codevector generator for determining first characteristics of a bi-pulse codevector representative of the speech signal after said spectral information and said pitch information have been filtered out and for providing said first characteristics for transmission; a second codevector generator for determining second characteristics of a bi-pulse codevector representative of the speech signal after said spectral information and said pitch information have been filtered out and for providing said second characteristics for transmission, said second codevector generator comprising frequency-domain transform means for transforming codevector possibilities from being representative of pulse-like sound to being representative of noise-like sound; a third codevector generator for determining third characteristics of a single-pulse codevector representative of the speech signal after said spectral information and said pitch information have been filtered out and for providing said third characteristics for transmission; and a comparator for evaluating the characteristics determined by said first second and third codebook generators and choosing one of said first, second or third characteristics.
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14. A speech coder for converting analog speech signals to digital speech signals for transmission, said speech coder comprising: a first filter for filtering out the spectral information from said speech signal and for providing said spectral information for transmission; a second filter for filtering out the pitch information from said speech signal and for providing said pitch information for transmission; a first codevector generator for determining first characteristics of a bi-pulse codevector representative of the speech signal after said spectral information and said pitch information have been filtered out and for providing said first characteristics for transmission; a second codevector generator for determining second characteristics of a bi-pulse codevector representative of the speech signal after said spectral information and said pitch information have been filtered out and for providing said second characteristics for transmission, said second codevector generator comprising frequency-domain transform means for transforming codevector possibilities from being representative of pulse-like sound to being representative of noise-like sound; a third codevector generator for determining third characteristics of a single-pulse codevector representative of the speech signal after said spectral information and said pitch information have been filtered out and for providing said third characteristics for transmission; and a comparator for evaluating the characteristics determined by said first second and third codebook generators and choosing one of said first, second or third characteristics. 15. The coder of claim 14, further comprising a weightor, for applying a weighting factor to one of said first, second and third characteristics.
| 0.5 |
9,633,483 | 9 | 15 |
9. A method for filtering, segmenting and recognizing objects, the method comprising an act of: causing one or more processors to execute instructions encoded on a non-transitory computer-readable medium, such that upon execution, the one or more processors perform operations of: down sampling a three-dimensional (3D) point cloud having a plurality of data points in 3D space to generate a down-sampled 3D point cloud P with reduced data points in the 3D space; identifying and removing a ground plane in the down-sampled 3D point cloud to leave non-ground data points in the down-sampled 3D point cloud; generating a set of 3D candidate blobs by clustering the non-ground data points to generate a plurality of 3D blobs, each of the 3D blobs having a cluster size; extracting features from each 3D candidate blob, the features being vectors that represent morphological characteristics of each 3D candidate blob; and classifying at least one of the 3D candidate blobs as a pre-defined object class based on the extracted features by assigning a semantic meaning to a segmented real-world individual object.
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9. A method for filtering, segmenting and recognizing objects, the method comprising an act of: causing one or more processors to execute instructions encoded on a non-transitory computer-readable medium, such that upon execution, the one or more processors perform operations of: down sampling a three-dimensional (3D) point cloud having a plurality of data points in 3D space to generate a down-sampled 3D point cloud P with reduced data points in the 3D space; identifying and removing a ground plane in the down-sampled 3D point cloud to leave non-ground data points in the down-sampled 3D point cloud; generating a set of 3D candidate blobs by clustering the non-ground data points to generate a plurality of 3D blobs, each of the 3D blobs having a cluster size; extracting features from each 3D candidate blob, the features being vectors that represent morphological characteristics of each 3D candidate blob; and classifying at least one of the 3D candidate blobs as a pre-defined object class based on the extracted features by assigning a semantic meaning to a segmented real-world individual object. 15. The method as set forth in claim 9 , wherein clustering the non-ground data point to generate a plurality of 3D blobs further comprises an act of, for every point in the down-sampled 3D point cloud P, recursively adding all neighboring points in a sphere with a fixed radius to a queue.
| 0.818068 |
6,119,086 | 1 | 6 |
1. A speech coding system responsive to an input speech signal provided by a system user, the system comprising: a first speech transcribing means comprising a speech recognition means having a word vocabulary associated therewith, the speech recognition means recognizing words in the input speech signal in accordance with the vocabulary and generating at least one phonetic token representative of the input speech signal; a second speech transcribing means for generating at least one phonetic token representative of a word in the input speech signal which is not in the word vocabulary; channel means, responsive to at least one of the phonetic tokens, for handling at least one of the phonetic tokens in accordance with an application of the speech coding system; and speech synthesizing means, responsive to the channel means, for generating a synthesized speech signal using at least one of a plurality of pre-enrolled phonetic tokens that substantially matches at least one of the phonetic tokens which is representative of the input speech signal provided by the system user.
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1. A speech coding system responsive to an input speech signal provided by a system user, the system comprising: a first speech transcribing means comprising a speech recognition means having a word vocabulary associated therewith, the speech recognition means recognizing words in the input speech signal in accordance with the vocabulary and generating at least one phonetic token representative of the input speech signal; a second speech transcribing means for generating at least one phonetic token representative of a word in the input speech signal which is not in the word vocabulary; channel means, responsive to at least one of the phonetic tokens, for handling at least one of the phonetic tokens in accordance with an application of the speech coding system; and speech synthesizing means, responsive to the channel means, for generating a synthesized speech signal using at least one of a plurality of pre-enrolled phonetic tokens that substantially matches at least one of the phonetic tokens which is representative of the input speech signal provided by the system user. 6. The speech coding system of claim 1, wherein the at least one phonetic token generated by the speech recognition means and the at least one phonetic token generated by the second speech transcribing means have a measure associated therewith, respectively, indicative of the similarity of the phonetic token to the input speech.
| 0.673267 |
7,650,286 | 225 | 227 |
225. A computer program product, to be used on a computer, for identifying a matching resume for a job description, comprising: a computer readable medium storing: program code for receiving the job description that includes at least one job requirement, each said at least one job requirement comprising a required skill or experience-related phrase and a required term of experience for the required skill or experience-related phrase; program code for storing the job description; program code for associating, for each said at least one job requirement, the required skill or experience-related phrase with at least one implying skill or experience-related phrase; program code for storing at least one searchable phrase for each said at least one job requirement, one of said at least one searchable phrase including the required skill or experience-related phrase, and said at least one searchable phrase including each said at least one implying skill or experience-related phrase; program code for receiving at least one resume; program code for parsing each said at least one resume to: locate at least one of said at least one searchable phrase in the resume; determine an experience range for each searchable phrase located in the resume by examining a use of each searchable phrase in the resume; and compute a term of experience for each searchable phrase located in the resume based on the experience range, wherein the term of experience for each said at least one skill or experience-related phrase is a summation of the term of experience for each occurrence of the phrase associated with a different experience range, wherein each resume summarizes a candidate's career and qualifications, and wherein each resume conveys personal and business-related characteristics that the candidate believes to be relevant to a prospective employer; program code for storing each said at least one resume; program code for computing, for each said at least one resume, a term of experience for the required skill or experience-related phrase for each said at least one job requirement; and program code for determining whether each said at least one resume is the matching resume that satisfies the job description.
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225. A computer program product, to be used on a computer, for identifying a matching resume for a job description, comprising: a computer readable medium storing: program code for receiving the job description that includes at least one job requirement, each said at least one job requirement comprising a required skill or experience-related phrase and a required term of experience for the required skill or experience-related phrase; program code for storing the job description; program code for associating, for each said at least one job requirement, the required skill or experience-related phrase with at least one implying skill or experience-related phrase; program code for storing at least one searchable phrase for each said at least one job requirement, one of said at least one searchable phrase including the required skill or experience-related phrase, and said at least one searchable phrase including each said at least one implying skill or experience-related phrase; program code for receiving at least one resume; program code for parsing each said at least one resume to: locate at least one of said at least one searchable phrase in the resume; determine an experience range for each searchable phrase located in the resume by examining a use of each searchable phrase in the resume; and compute a term of experience for each searchable phrase located in the resume based on the experience range, wherein the term of experience for each said at least one skill or experience-related phrase is a summation of the term of experience for each occurrence of the phrase associated with a different experience range, wherein each resume summarizes a candidate's career and qualifications, and wherein each resume conveys personal and business-related characteristics that the candidate believes to be relevant to a prospective employer; program code for storing each said at least one resume; program code for computing, for each said at least one resume, a term of experience for the required skill or experience-related phrase for each said at least one job requirement; and program code for determining whether each said at least one resume is the matching resume that satisfies the job description. 227. The computer program product of claim 225 , wherein the storing of each said at least one resume is to a database.
| 0.876812 |
4,783,758 | 22 | 28 |
22. In a method for automated linguistic expression substitution on a digital data processor, the improvement wherein said digital data processor executes steps comprising A. accepting into said digital data processor a suspect expression signal representative of a linguistic expression consisting of characters, B. accepting into said digital data processor an alternate expression signal representative of a permissible linguistic expression consisting of characters, C. comparing within said digital data processor said suspect expression signal with said alternate expression signal and producing a disparity signal numerically representative of differences between a spelling of the linguistic expression represented by said suspect expression signals and a spelling of the linguistic expression represented by said alternate expression signal, said comparing step including the step of producing said disparity signal to be numerically representative of the type and magnitude of differences between said suspect expression signal and said alternate expression signal, said comparing step further including the steps of responding to the detection of transposition, character deletion, unmatched character, and character length disparity types for producing a signal indicative of the numerically-weighted structural significance of that detected type, and D. evaluating within said digital data processor a numerical value represented by said disparity signal for determining whether said alternate expression signal is substitutable for said suspect expression signal and for producing an output signal indicative thereof.
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22. In a method for automated linguistic expression substitution on a digital data processor, the improvement wherein said digital data processor executes steps comprising A. accepting into said digital data processor a suspect expression signal representative of a linguistic expression consisting of characters, B. accepting into said digital data processor an alternate expression signal representative of a permissible linguistic expression consisting of characters, C. comparing within said digital data processor said suspect expression signal with said alternate expression signal and producing a disparity signal numerically representative of differences between a spelling of the linguistic expression represented by said suspect expression signals and a spelling of the linguistic expression represented by said alternate expression signal, said comparing step including the step of producing said disparity signal to be numerically representative of the type and magnitude of differences between said suspect expression signal and said alternate expression signal, said comparing step further including the steps of responding to the detection of transposition, character deletion, unmatched character, and character length disparity types for producing a signal indicative of the numerically-weighted structural significance of that detected type, and D. evaluating within said digital data processor a numerical value represented by said disparity signal for determining whether said alternate expression signal is substitutable for said suspect expression signal and for producing an output signal indicative thereof. 28. In a method for automated linguistic expression substitution on a digital data processor according to claim 22, the improvement in which said comparing step further comprises the step of computing a disparity value as the summation of values indicated by each said significance signal.
| 0.718324 |
7,536,641 | 21 | 22 |
21. A client system comprising: one or more processors; memory storing one or more programs to be executed by the one or more processors, the one or more programs including a browser application; and an authoring web page stored in the memory, the authoring web page comprising: an authoring tool embedded in the authoring web page, the authoring tool including a graphical user interface for composing a user-defined web page; instructions for displaying in a first browser window the authoring web page including the graphical user interface of the authoring tool; instructions for using the displayed graphical user interface of the authoring tool, responding to user instructions by placing instances of predefined structured fields in the user-defined web page and associating user-specified content with the instances of the predefined structured fields, wherein a respective instance of the predefined structured fields in the user-defined web page corresponds to a geometric region of the user-defined web page having a visible and adjustable boundary; instructions for displaying a preview of the user-defined web page in a second browser window; and instructions for sending the user-defined web page to a server for publication.
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21. A client system comprising: one or more processors; memory storing one or more programs to be executed by the one or more processors, the one or more programs including a browser application; and an authoring web page stored in the memory, the authoring web page comprising: an authoring tool embedded in the authoring web page, the authoring tool including a graphical user interface for composing a user-defined web page; instructions for displaying in a first browser window the authoring web page including the graphical user interface of the authoring tool; instructions for using the displayed graphical user interface of the authoring tool, responding to user instructions by placing instances of predefined structured fields in the user-defined web page and associating user-specified content with the instances of the predefined structured fields, wherein a respective instance of the predefined structured fields in the user-defined web page corresponds to a geometric region of the user-defined web page having a visible and adjustable boundary; instructions for displaying a preview of the user-defined web page in a second browser window; and instructions for sending the user-defined web page to a server for publication. 22. The client system of claim 21 , wherein the authoring web page includes instructions for: allowing the user to select a content object within an instance of a predefined structured field; displaying a dialog box near the content object, the dialog box including information related to the content object; and allowing the user to modify the content object by updating the information in the dialog box.
| 0.622677 |
8,077,973 | 1 | 2 |
1. A method for classifying an image representation of a handwritten word of cursive script, said method comprising: optically reading a handwritten word so as to form an image representation thereof comprising a bit map of pixels; extracting a pixel contour of said bit map; detecting vertical peak and minima pixel extrema on upper and lower zones of said contour respectively; detecting local vertical peak pixel extrema on an upper zone of said external contour by determining if a given local pixel is a vertical peak relative to neighbouring pixels; detecting local vertical minima pixel extrema on a lower zone of said external contour by determining if a given local pixel is a vertical minimum relative to neighbouring pixels; organizing said peak and minima pixel extrema into respective independent peak and minima sequences comprising extracting features at each said extrema and further classifying extrema into two sequences of extrema on respective said upper and lower zones of said word image pixel contour; determining the respective feature vectors of said peak and minima sequences; and classifying said word image according to said peak and minima feature vectors, wherein at least one or more of said features extracted at each said extrema is selected from the group consisting of: the number of local extrema neighboring a given said extrema on a same closed curve of said word image contour, said local extrema having a convex attribute corresponding to that of said given extrema; the number of local extrema neighboring a given said extrema on a same closed curve of said word image contour, said local extrema having a different convex attribute from said given extrema; the lesser of the height difference between a given said extrema and a left neighbouring extrema and of the height difference between said given extrema and a right neighbouring extrema, wherein said left and right neighbouring extrema have convex attribute corresponding to that of said given extrema; the lesser of the height difference between a given said extrema and a left neighbouring extrema and of the height difference between said given extrema and a right neighbouring extrema, wherein said left and right neighbouring extrema have a different convex attribute than that of said given extrema; the number of peaks above a said given extrema divided by the total number of peaks on said pixel contour; the number of peaks below a said given extrema divided by the total number of peaks on said pixel contour; the y/h position of said given extrema, wherein y represents the y-axis coordinate of said given extrema and h represents the height of said word image; the lesser of a contour portion length between a given said extrema and a left neighbouring peak and of a contour portion length between a given said extrema and a right neighbouring peak, wherein said neighbouring peaks and said given extrema are on a same closed curve; the lesser of a contour portion length between a given said extrema and a left neighbouring minima and of a contour portion length between a given said extrema and a right neighbouring minima, wherein said neighbouring minima and said given extrema are on a same closed curve; the lesser of a height difference between a given said extrema and a left neighbouring peak and of a given said extrema and a and right neighbouring peak, wherein said neighbouring peaks and said given extrema are on a same closed curve; the lesser of a height difference between a given said extrema and a left neighbouring minima and of a given said extrema and a and right neighbouring minima, wherein said neighbouring minima and said given extrema are on a same closed curve; the height ratio of a given said extrema and neighboring left and right extrema as defined by
(y A −y tl )/(y n −y tln ) wherein a given said extrema is represented by A, a lowest extrema of said left or right neighbouring extrema is represented by n, y tl represents the y-coordinate of the top-left corner of a said contour or a said closed curve, y tln represents the top-left corner of a said contour or a said closed curve where point n is located y A and y n represent the y-coordinate of A and n respectfully; the distance between a given said extrema and a vertical intersection point; and any combination thereof.
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1. A method for classifying an image representation of a handwritten word of cursive script, said method comprising: optically reading a handwritten word so as to form an image representation thereof comprising a bit map of pixels; extracting a pixel contour of said bit map; detecting vertical peak and minima pixel extrema on upper and lower zones of said contour respectively; detecting local vertical peak pixel extrema on an upper zone of said external contour by determining if a given local pixel is a vertical peak relative to neighbouring pixels; detecting local vertical minima pixel extrema on a lower zone of said external contour by determining if a given local pixel is a vertical minimum relative to neighbouring pixels; organizing said peak and minima pixel extrema into respective independent peak and minima sequences comprising extracting features at each said extrema and further classifying extrema into two sequences of extrema on respective said upper and lower zones of said word image pixel contour; determining the respective feature vectors of said peak and minima sequences; and classifying said word image according to said peak and minima feature vectors, wherein at least one or more of said features extracted at each said extrema is selected from the group consisting of: the number of local extrema neighboring a given said extrema on a same closed curve of said word image contour, said local extrema having a convex attribute corresponding to that of said given extrema; the number of local extrema neighboring a given said extrema on a same closed curve of said word image contour, said local extrema having a different convex attribute from said given extrema; the lesser of the height difference between a given said extrema and a left neighbouring extrema and of the height difference between said given extrema and a right neighbouring extrema, wherein said left and right neighbouring extrema have convex attribute corresponding to that of said given extrema; the lesser of the height difference between a given said extrema and a left neighbouring extrema and of the height difference between said given extrema and a right neighbouring extrema, wherein said left and right neighbouring extrema have a different convex attribute than that of said given extrema; the number of peaks above a said given extrema divided by the total number of peaks on said pixel contour; the number of peaks below a said given extrema divided by the total number of peaks on said pixel contour; the y/h position of said given extrema, wherein y represents the y-axis coordinate of said given extrema and h represents the height of said word image; the lesser of a contour portion length between a given said extrema and a left neighbouring peak and of a contour portion length between a given said extrema and a right neighbouring peak, wherein said neighbouring peaks and said given extrema are on a same closed curve; the lesser of a contour portion length between a given said extrema and a left neighbouring minima and of a contour portion length between a given said extrema and a right neighbouring minima, wherein said neighbouring minima and said given extrema are on a same closed curve; the lesser of a height difference between a given said extrema and a left neighbouring peak and of a given said extrema and a and right neighbouring peak, wherein said neighbouring peaks and said given extrema are on a same closed curve; the lesser of a height difference between a given said extrema and a left neighbouring minima and of a given said extrema and a and right neighbouring minima, wherein said neighbouring minima and said given extrema are on a same closed curve; the height ratio of a given said extrema and neighboring left and right extrema as defined by
(y A −y tl )/(y n −y tln ) wherein a given said extrema is represented by A, a lowest extrema of said left or right neighbouring extrema is represented by n, y tl represents the y-coordinate of the top-left corner of a said contour or a said closed curve, y tln represents the top-left corner of a said contour or a said closed curve where point n is located y A and y n represent the y-coordinate of A and n respectfully; the distance between a given said extrema and a vertical intersection point; and any combination thereof. 2. A method according to claim 1 , wherein said determining comprises determining feature vectors of said vertical peak and minima sequences.
| 0.98935 |
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