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9,361,134 | 1 | 6 |
1. A method of validating translated files for inclusion in an application being developed, the method comprising the steps of: a computer sending a translatable file having externalized content in a single base natural language to a plurality of other computers, wherein the externalized content is external to code of the application being developed; in response to a generation of a first translated file including a first translation of the externalized content into a natural language other than the base natural language by another computer included in the plurality of other computers, the computer receiving the first translated file from the other computer; in a simulation environment, a processor of the computer simulating a loading of the first translation of the externalized content into a user interface of the application at a runtime of the application and simulating a presentation of the first translation of the externalized content in the user interface at the runtime, by calling one or more functions included in the application, without an actual presentation of the first translation of the externalized content in the user interface, wherein an execution at the runtime of the one or more functions displays the externalized content in the user interface; the computer determining whether the simulating of the loading of the first translation of the externalized content into the user interface and the simulating of the presentation of the first translation of the externalized content in the user interface by the calling the one or more functions indicates an error condition that causes the application to stop running at the runtime; based in part on the error condition being indicated by the simulating of the loading of the first translation and the simulating of the presentation of the first translation, the computer: determining the first translation of the externalized content includes a syntax error that causes the error condition, and initiating a correction of the syntax error by sending the report to the other computer, wherein the syntax error includes at least one of: (1) a first key included in the first translated file is not matched with exactly one corresponding first value in the first translated file, the first translated file being a first property file including first key-value pairs including the first key and the first value as one of the pairs; (2) a second key in the translatable file is not in the first translated file, the translatable file being a second property file including second key-value pairs, one of the second key-value pairs including the second key; and (3) one or more placeholders in the first translated file are not matched to respective one or more placeholders in the translatable file, the one or more placeholders in the first translated file and the one or more placeholders in the translatable file providing variable substitution by being filled in with actual values by the code of the application at the runtime; and in response to a receipt by the computer of a second translation of the externalized content which includes the correction of the syntax error, the second translation included in a second translated file generated by the other computer: the computer simulating a loading of the second translation of the externalized content into the user interface of the application at the runtime and simulating a presentation of the second translation of the externalized content in the user interface at the runtime, by calling the one or more functions; the computer determining whether the simulating of the loading of the second translation and the simulating of the presentation of the second translation by the calling the one or more functions indicates the error condition that causes the application to stop running at the runtime; and based in part on the error condition not being indicated by the simulating of the loading of the second translation and the simulating of the presentation of the second translation, the computer at the runtime presenting the second translation of the externalized content in the user interface without causing the application to stop running at the runtime.
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1. A method of validating translated files for inclusion in an application being developed, the method comprising the steps of: a computer sending a translatable file having externalized content in a single base natural language to a plurality of other computers, wherein the externalized content is external to code of the application being developed; in response to a generation of a first translated file including a first translation of the externalized content into a natural language other than the base natural language by another computer included in the plurality of other computers, the computer receiving the first translated file from the other computer; in a simulation environment, a processor of the computer simulating a loading of the first translation of the externalized content into a user interface of the application at a runtime of the application and simulating a presentation of the first translation of the externalized content in the user interface at the runtime, by calling one or more functions included in the application, without an actual presentation of the first translation of the externalized content in the user interface, wherein an execution at the runtime of the one or more functions displays the externalized content in the user interface; the computer determining whether the simulating of the loading of the first translation of the externalized content into the user interface and the simulating of the presentation of the first translation of the externalized content in the user interface by the calling the one or more functions indicates an error condition that causes the application to stop running at the runtime; based in part on the error condition being indicated by the simulating of the loading of the first translation and the simulating of the presentation of the first translation, the computer: determining the first translation of the externalized content includes a syntax error that causes the error condition, and initiating a correction of the syntax error by sending the report to the other computer, wherein the syntax error includes at least one of: (1) a first key included in the first translated file is not matched with exactly one corresponding first value in the first translated file, the first translated file being a first property file including first key-value pairs including the first key and the first value as one of the pairs; (2) a second key in the translatable file is not in the first translated file, the translatable file being a second property file including second key-value pairs, one of the second key-value pairs including the second key; and (3) one or more placeholders in the first translated file are not matched to respective one or more placeholders in the translatable file, the one or more placeholders in the first translated file and the one or more placeholders in the translatable file providing variable substitution by being filled in with actual values by the code of the application at the runtime; and in response to a receipt by the computer of a second translation of the externalized content which includes the correction of the syntax error, the second translation included in a second translated file generated by the other computer: the computer simulating a loading of the second translation of the externalized content into the user interface of the application at the runtime and simulating a presentation of the second translation of the externalized content in the user interface at the runtime, by calling the one or more functions; the computer determining whether the simulating of the loading of the second translation and the simulating of the presentation of the second translation by the calling the one or more functions indicates the error condition that causes the application to stop running at the runtime; and based in part on the error condition not being indicated by the simulating of the loading of the second translation and the simulating of the presentation of the second translation, the computer at the runtime presenting the second translation of the externalized content in the user interface without causing the application to stop running at the runtime. 6. The method of claim 1 , further comprising the step of prior to the step of sending the translatable file, the computer identifying and correcting one or more errors in the translatable file to conform to syntax rules of translation systems running on the other computers.
| 0.881568 |
8,645,388 | 8 | 9 |
8. The method of claim 7 , wherein the selectivity factor decreases as the number of referenced structured documents increases.
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8. The method of claim 7 , wherein the selectivity factor decreases as the number of referenced structured documents increases. 9. The method of claim 8 wherein when the selectivity factor of the first path-value pair is less than the selectivity factor of the second path-value pair, intersecting the structured documents includes comparing the at least one structured documents referenced by the second path-value pair to the at least one structured documents referenced by the first path-value pair, thereby allowing the indexing engine to skip non-matching structured documents referenced by the first path-value pair.
| 0.5 |
9,208,779 | 4 | 6 |
4. The method of claim 2 , further comprising: maximizing a combination of the probabilities by repeating the determination of at least one of the weights in the set of n-gram language model weights λ M , the determination of at least one of the weights in the set of sentence cluster weights γ C , and the probabilities p w of the sentences in the set of development sentences W.
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4. The method of claim 2 , further comprising: maximizing a combination of the probabilities by repeating the determination of at least one of the weights in the set of n-gram language model weights λ M , the determination of at least one of the weights in the set of sentence cluster weights γ C , and the probabilities p w of the sentences in the set of development sentences W. 6. The method of claim 4 , wherein: each of the n-gram language model weights λ m from n-gram language model weights λ M satisfies λ c , m ′ = 1 c ∑ w ∈ c ∑ i = 1 w λ c , m p m ( w i ❘ h i ) ∑ j = 1 M λ c , j p j ( w j ❘ h j ) , and each of the sentence cluster weights γ c from the set of sentence cluster weights γ C satisfies γ c = c W , where c is one of the clusters in the set of sentence clusters, |c| is the quantity of sentences in cluster c, m is a language model from the set of two or more language models G M , M is the quantity of language models in the set of two or more language models G M , |W| is the quantity of sentences in set of development sentences W, |w| is the quantity of n-grams in the sentence w from the set of development sentences W, and p m (w i |h i ) is the probability that n-gram w i follows history h i as estimated by a language model G m from the set of two or more language models G M .
| 0.58 |
9,489,629 | 1 | 5 |
1. A method, comprising: receiving, by a question answering system, a case from a user; assigning a first level of user sophistication, of a plurality of levels of user sophistication, to the user; determining a level of evidence sophistication, of a plurality of levels of evidence sophistication, associated with each of a plurality of items of supporting evidence in a corpus of information used to process the case; selecting, by operation of a computer processor, a subset of the plurality of items of supporting evidence based on the determined levels of user sophistication and evidence sophistication; and returning the selected subset to the user as part of a response to the case.
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1. A method, comprising: receiving, by a question answering system, a case from a user; assigning a first level of user sophistication, of a plurality of levels of user sophistication, to the user; determining a level of evidence sophistication, of a plurality of levels of evidence sophistication, associated with each of a plurality of items of supporting evidence in a corpus of information used to process the case; selecting, by operation of a computer processor, a subset of the plurality of items of supporting evidence based on the determined levels of user sophistication and evidence sophistication; and returning the selected subset to the user as part of a response to the case. 5. The method of claim 1 , wherein the first level of user sophistication of the user is further assigned based on a role of the user.
| 0.823684 |
9,785,674 | 9 | 11 |
9. An information processing system for decompressing results of a join query, the information processing system comprising: a memory; a processor communicatively coupled to the memory; and a result decoder communicatively coupled to the memory and the processor, wherein the processor is configured to perform a method comprising: receiving via a network, from a result set encoder of the information processing system, a result set from the join query; receiving a plurality of encoded tuples associated with the result set, wherein each encoded tuple in the plurality of encoded tuples comprises a sequence of values, where each value in the sequence of values corresponds to a position of an entry within a nested hierarchy dictionary in a set of nested hierarchy of dictionaries, and wherein a position of said each value in the sequence of values corresponds to a position of a column within the nested hierarchy dictionary corresponding to a value; receiving, with aid each encoded tuple in the plurality of encoded tuples, a set of dictionary entry information, wherein the set dictionary entry information comprises a value from a dictionary entry generated by the result set encoder and a location within the set of nested hierarchy of dictionaries to store the value; creating the set of nested hierarchy of dictionaries based on storing in a memory, for each set of dictionary entry information, each value of the sequence of values at the location in a dictionary of the set of nested hierarchy of dictionaries as identified by the set of dictionary entry information; using, by the processor, the set of nested hierarchy of dictionaries and the values from the set of dictionary entry information stored within the set of nested hierarchy of dictionaries to decode the plurality of encoded tuples so as to produce a plurality of decoded tuples of the result set, wherein decoding the plurality of encoded tuples further comprises: analyzing the plurality of encoded tuples; and selecting, for said each encoded tuple of the plurality of encoded tuples, at least a first entry within at least a first dictionary of the set of nested hierarchy of dictionaries based on information within a corresponding encoded tuple, wherein the selecting at least the first entry comprises: determining that the first entry that has been selected is mapped to a second entry in a second dictionary of the set of nested hierarchy of dictionaries; and decompressing, by the processor based on the nested hierarchy of dictionaries, the result set of the join query with the decoder.
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9. An information processing system for decompressing results of a join query, the information processing system comprising: a memory; a processor communicatively coupled to the memory; and a result decoder communicatively coupled to the memory and the processor, wherein the processor is configured to perform a method comprising: receiving via a network, from a result set encoder of the information processing system, a result set from the join query; receiving a plurality of encoded tuples associated with the result set, wherein each encoded tuple in the plurality of encoded tuples comprises a sequence of values, where each value in the sequence of values corresponds to a position of an entry within a nested hierarchy dictionary in a set of nested hierarchy of dictionaries, and wherein a position of said each value in the sequence of values corresponds to a position of a column within the nested hierarchy dictionary corresponding to a value; receiving, with aid each encoded tuple in the plurality of encoded tuples, a set of dictionary entry information, wherein the set dictionary entry information comprises a value from a dictionary entry generated by the result set encoder and a location within the set of nested hierarchy of dictionaries to store the value; creating the set of nested hierarchy of dictionaries based on storing in a memory, for each set of dictionary entry information, each value of the sequence of values at the location in a dictionary of the set of nested hierarchy of dictionaries as identified by the set of dictionary entry information; using, by the processor, the set of nested hierarchy of dictionaries and the values from the set of dictionary entry information stored within the set of nested hierarchy of dictionaries to decode the plurality of encoded tuples so as to produce a plurality of decoded tuples of the result set, wherein decoding the plurality of encoded tuples further comprises: analyzing the plurality of encoded tuples; and selecting, for said each encoded tuple of the plurality of encoded tuples, at least a first entry within at least a first dictionary of the set of nested hierarchy of dictionaries based on information within a corresponding encoded tuple, wherein the selecting at least the first entry comprises: determining that the first entry that has been selected is mapped to a second entry in a second dictionary of the set of nested hierarchy of dictionaries; and decompressing, by the processor based on the nested hierarchy of dictionaries, the result set of the join query with the decoder. 11. The information processing system of claim 9 , wherein at least one of the values within the set of dictionary entry information is received as plaintext with a flag that indicates that the plaintext is at least one dictionary entry.
| 0.733708 |
9,582,572 | 15 | 16 |
15. A method to provide a personalized search library based on continual concept correlation, the method comprising: receiving, by a computing device, event data representing content accessed by a user of a client computing device, wherein an order of the event data represents an order that the user of the client computing device accessed the content; analyzing, by the computing device, the event data to extract concepts of the content; correlating, by the computing device, the extracted concepts based on the order of the event data to generate a plurality of correlations between the extracted concepts, wherein each correlation is indicative of a relationship between a first extracted concept and a second extracted concept based on the order of the event data; adjusting, by the computing device, a weight associated with each extracted concept based on a frequency of the extracted concept occurring in the content to generate an adjusted weight; and storing, by the computing device, the correlations, adjusted weights, and extracted concepts in a concept model that identifies the relative correlation between each extracted concept and the adjusted weight associated with each extracted concept.
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15. A method to provide a personalized search library based on continual concept correlation, the method comprising: receiving, by a computing device, event data representing content accessed by a user of a client computing device, wherein an order of the event data represents an order that the user of the client computing device accessed the content; analyzing, by the computing device, the event data to extract concepts of the content; correlating, by the computing device, the extracted concepts based on the order of the event data to generate a plurality of correlations between the extracted concepts, wherein each correlation is indicative of a relationship between a first extracted concept and a second extracted concept based on the order of the event data; adjusting, by the computing device, a weight associated with each extracted concept based on a frequency of the extracted concept occurring in the content to generate an adjusted weight; and storing, by the computing device, the correlations, adjusted weights, and extracted concepts in a concept model that identifies the relative correlation between each extracted concept and the adjusted weight associated with each extracted concept. 16. The method of claim 15 , further comprising: searching, by the computing device, one or more content sources for relevant search results based on a current context of the concept model; indexing, by the computing device, the search results in the personalized search library according to the concept model; and facilitating, by the computing device, access to the personalized search library by the user.
| 0.5 |
7,516,442 | 1 | 14 |
1. A method for creating language-neutral and corresponding language-specific resource files for a component, the method comprising: obtaining a resource manifest file; retrieving a resource file by accessing the resource manifest file; creating a language-neutral file and a language-specific resource file for the retrieved resource file, the language-specific resource file having a plurality of language-specific resources, the language neutral file and the language-specific resource file being created by reading localizable resource information contained in the resource manifest file, the localizable resource information specifying locations of specific resources to be retrieved during runtime from the language-specific resource file, the locations of the specific resources being mapped to resource identifiers used by applications to identify the specific resources within the language-specific resource file, in the resource manifest file, the resource manifest file further specifying a type of resource to be retrieved, and indicating whether the resource is localizable; creating a checksum data; updating a field in the resource manifest file with the checksum data; and the language-neutral file and language-specific resource file being created by splitting localizable resources identified by the localizable resource information into neutral and localized files, and by creating a language-neutral image and a language-specific image of the retrieved resource file.
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1. A method for creating language-neutral and corresponding language-specific resource files for a component, the method comprising: obtaining a resource manifest file; retrieving a resource file by accessing the resource manifest file; creating a language-neutral file and a language-specific resource file for the retrieved resource file, the language-specific resource file having a plurality of language-specific resources, the language neutral file and the language-specific resource file being created by reading localizable resource information contained in the resource manifest file, the localizable resource information specifying locations of specific resources to be retrieved during runtime from the language-specific resource file, the locations of the specific resources being mapped to resource identifiers used by applications to identify the specific resources within the language-specific resource file, in the resource manifest file, the resource manifest file further specifying a type of resource to be retrieved, and indicating whether the resource is localizable; creating a checksum data; updating a field in the resource manifest file with the checksum data; and the language-neutral file and language-specific resource file being created by splitting localizable resources identified by the localizable resource information into neutral and localized files, and by creating a language-neutral image and a language-specific image of the retrieved resource file. 14. The method of claim 1 , wherein reading the plurality of fields further comprises reading: a twelfth data field containing data representing a file version of a resource file; and a thirteenth data field containing data representing a checksum value.
| 0.805513 |
4,362,152 | 20 | 21 |
20. A prosthetic device according to claim 19 wherein the tip end of the rod means is bent sufficiently upwardly so that, in use, the rod means is disposed below the uppermost size extent of the penis, and the arcuate members are disposed above the lowermost size extent of the penis.
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20. A prosthetic device according to claim 19 wherein the tip end of the rod means is bent sufficiently upwardly so that, in use, the rod means is disposed below the uppermost size extent of the penis, and the arcuate members are disposed above the lowermost size extent of the penis. 21. A prosthetic device according to claim 20 wherein the members are capable of rotation relatively further apart while remaining in mutual contact along the end portion.
| 0.5 |
9,420,204 | 1 | 5 |
1. An information processing apparatus, comprising: a frame selection unit that selects a characteristic frame from image data representing one or a plurality of objects and including a plurality of frames; an object selection unit that selects a characteristic object from the one or the plurality of objects; a textual information generation unit that generates textual information that indicates at least one of a movement of the characteristic object and a sound from the characteristic object; and a display controller that displays an image of the characteristic frame with the textual information associated with the characteristic object, wherein the frame selection unit selects the characteristic frame in a period within which an amount derived from a movement of the one or the plurality of objects or derived from the sound from the one or the plurality of objects is equal to or above a predetermined threshold value.
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1. An information processing apparatus, comprising: a frame selection unit that selects a characteristic frame from image data representing one or a plurality of objects and including a plurality of frames; an object selection unit that selects a characteristic object from the one or the plurality of objects; a textual information generation unit that generates textual information that indicates at least one of a movement of the characteristic object and a sound from the characteristic object; and a display controller that displays an image of the characteristic frame with the textual information associated with the characteristic object, wherein the frame selection unit selects the characteristic frame in a period within which an amount derived from a movement of the one or the plurality of objects or derived from the sound from the one or the plurality of objects is equal to or above a predetermined threshold value. 5. The information processing apparatus according to claim 1 , wherein the frame selection unit selects as the characteristic frame a frame in a period within which a length of time of a talk performed by the one or the plurality of objects is equal to or above a predetermined threshold talk time length.
| 0.831118 |
8,156,117 | 44 | 47 |
44. The method for removing information about objects of an arbitrary application domain from the system for storage and retrieval of a plurality of information objects according to claim 1 , comprising the steps of: a) marking an AUS to be removed, and b) comparing IOs of new AUSs added to said storage and search system with the IO of the AUS to be removed and, if the similarity criterion as per the metric of said IOs is met, replacing the AUS being removed with the added AUS on the logical network of said system.
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44. The method for removing information about objects of an arbitrary application domain from the system for storage and retrieval of a plurality of information objects according to claim 1 , comprising the steps of: a) marking an AUS to be removed, and b) comparing IOs of new AUSs added to said storage and search system with the IO of the AUS to be removed and, if the similarity criterion as per the metric of said IOs is met, replacing the AUS being removed with the added AUS on the logical network of said system. 47. The method according to claim 44 , wherein during each removal, modificating links in link lists of information objects connected with said IO.
| 0.906844 |
8,577,938 | 6 | 15 |
6. The system of claim 1 , wherein the class of the hierarchal classes further includes the attribute of including a classification type that is selected from pattern-based, keyword-based, rule-based, and classifier-based classification types.
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6. The system of claim 1 , wherein the class of the hierarchal classes further includes the attribute of including a classification type that is selected from pattern-based, keyword-based, rule-based, and classifier-based classification types. 15. The system of claim 6 , wherein the semantic classification module is to: use the pattern-based classification to generate the semantic classification based on a pattern of values; use the keyword-based classification to generate the semantic classification based on a comparison of keywords to values of the column of the data to determine whether a value matches a keyword; use the rule-based classification to generate the semantic classification based on a classification of values of the column of the data based on pre-defined rules; and use the classifier-based classification to generate the semantic classification based on a classification of values of the column of the data based on previously determined classifications to determine a match.
| 0.5 |
7,630,959 | 1 | 6 |
1. A method for processing a query on a database using a server operatively connected to the database, the method comprising: presenting from the server, to a user, a plurality of query elements, each query element having a plurality of allowed query element values, the plurality of allowed query element values for each query element being presented to the user for selection of a query element value from the plurality of allowed query element values, the query element values not selected by the user being non-selected query element values; receiving at the server, from the user, a selected query element value for at least one of the plurality of query elements; retrieving information objects stored in the database, the database storing a first relevance value which defines a relevance of at least one information object with respect to the selected query element value and a second relevance value which defines a relevance of the at least one information object with respect to the non-selected query element value, the first relevance value and the second relevance value being assigned by a human editor other than the user, the first relevance value and the second relevance value being assigned before the plurality of query elements is presented to the user, the information objects being retrieved based on the first relevance value and the second relevance value, the database further including an index, the index associating at least one information object stored in the database with at least the selected query element value; wherein retrieving the information objects based on the first relevance value and the second relevance value is performed by the server in response to receiving the selected query element value for at least one of the plurality of query elements, and returning query results to the user, the query results corresponding to the information objects.
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1. A method for processing a query on a database using a server operatively connected to the database, the method comprising: presenting from the server, to a user, a plurality of query elements, each query element having a plurality of allowed query element values, the plurality of allowed query element values for each query element being presented to the user for selection of a query element value from the plurality of allowed query element values, the query element values not selected by the user being non-selected query element values; receiving at the server, from the user, a selected query element value for at least one of the plurality of query elements; retrieving information objects stored in the database, the database storing a first relevance value which defines a relevance of at least one information object with respect to the selected query element value and a second relevance value which defines a relevance of the at least one information object with respect to the non-selected query element value, the first relevance value and the second relevance value being assigned by a human editor other than the user, the first relevance value and the second relevance value being assigned before the plurality of query elements is presented to the user, the information objects being retrieved based on the first relevance value and the second relevance value, the database further including an index, the index associating at least one information object stored in the database with at least the selected query element value; wherein retrieving the information objects based on the first relevance value and the second relevance value is performed by the server in response to receiving the selected query element value for at least one of the plurality of query elements, and returning query results to the user, the query results corresponding to the information objects. 6. The method of claim 1 , further comprising mapping the retrieved information objects into corresponding mapped information objects.
| 0.629834 |
9,947,019 | 10 | 16 |
10. A method of recommending objects comprising: storing a plurality of symmetrical objects and a plurality of past selections, the plurality of symmetrical objects including a plurality of consumer objects and a plurality of inanimate objects, the plurality of symmetrical objects having attribute names that match, each attribute name having a value, each past selection including two or more symmetrical objects; determining a time range, a location, and a consumer object based on received consumer information, the consumer object being one of the plurality of symmetrical objects; identifying a plurality of filtered selections from the plurality of past selections, the plurality of filtered selections having been selected in the past during the time range and from the location, the plurality of filtered selections including a plurality of filtered objects, each filtered object being one of the plurality of symmetrical objects; determining a plurality of total differences for the plurality of filtered objects such that a total difference is determined for each filtered object, determining the plurality of total differences including: for each filtered object, determining a plurality of attribute differences such that an attribute difference is determined for each attribute name of a filtered object, each attribute difference being a difference between a value of an attribute name of the consumer object and a value of a corresponding attribute name of the filtered object; and summing the plurality of attribute differences to determine the total difference for the filtered object; and selecting the filtered object that has a smallest total difference to be a recommended object.
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10. A method of recommending objects comprising: storing a plurality of symmetrical objects and a plurality of past selections, the plurality of symmetrical objects including a plurality of consumer objects and a plurality of inanimate objects, the plurality of symmetrical objects having attribute names that match, each attribute name having a value, each past selection including two or more symmetrical objects; determining a time range, a location, and a consumer object based on received consumer information, the consumer object being one of the plurality of symmetrical objects; identifying a plurality of filtered selections from the plurality of past selections, the plurality of filtered selections having been selected in the past during the time range and from the location, the plurality of filtered selections including a plurality of filtered objects, each filtered object being one of the plurality of symmetrical objects; determining a plurality of total differences for the plurality of filtered objects such that a total difference is determined for each filtered object, determining the plurality of total differences including: for each filtered object, determining a plurality of attribute differences such that an attribute difference is determined for each attribute name of a filtered object, each attribute difference being a difference between a value of an attribute name of the consumer object and a value of a corresponding attribute name of the filtered object; and summing the plurality of attribute differences to determine the total difference for the filtered object; and selecting the filtered object that has a smallest total difference to be a recommended object. 16. The method of claim 10 , further comprising transmitting the recommended object to a consumer device associated with the consumer object, wherein the values of the attribute names of the consumer object and the values of the attribute names of the filtered object selected as the recommended object are modified if the recommended object is selected.
| 0.5 |
8,477,611 | 14 | 16 |
14. The apparatus for packet classification in claim 10 , wherein the packet classifying control includes: a hash unit for generating one or more Bloom filter hashing indices and one or more hash table indices for each of the particular tuples, wherein the Bloom filter identifies bit values of entries indicated by the one or more Bloom filter hashing indices to determine the search pool including only the positive tuples resulting from the search; and a rule determiner for accessing hash table entries by using the one or more hash table indices corresponding to each of the tuples within the search pool to search for the rules stored in the hash table entries and determining the best matching rule from among the rules.
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14. The apparatus for packet classification in claim 10 , wherein the packet classifying control includes: a hash unit for generating one or more Bloom filter hashing indices and one or more hash table indices for each of the particular tuples, wherein the Bloom filter identifies bit values of entries indicated by the one or more Bloom filter hashing indices to determine the search pool including only the positive tuples resulting from the search; and a rule determiner for accessing hash table entries by using the one or more hash table indices corresponding to each of the tuples within the search pool to search for the rules stored in the hash table entries and determining the best matching rule from among the rules. 16. The apparatus for packet classification in claim 14 , wherein the hash unit includes: a cyclic redundancy check generator for generating a fixed length string upon receiving a string generated through extracting from each of the fields a higher order bit corresponding to the matching length represented by the particular tuple and adding a predetermined bit pattern to the value of each of the extracted bits for padding the value with a fixed length value followed by a concatenation of the padded values; and a hashing index generator for generating a plurality of the Bloom filter hashing indices and one of the hash table indices from the fixed length string.
| 0.5 |
7,555,490 | 37 | 44 |
37. A graphical user interface (GUI) comprising: a display screen; numeric attribute selector, set forth in an image on the display screen, to permit a user to select a numeric attribute that is associated with items in a database and to facilitate the communication of a selected numeric attribute to a processor, at least some of the items in the database have a numeric value for the selected attribute, wherein the numeric attribute describes a physical characteristic or property of the items; a descriptive word list selector, set forth in an image on the display screen, to permit a user to link a selected numeric attribute to a selected descriptive word in a descriptive word list and to facilitate the communication of such a linking to a processor; a function selector, set forth in an image on the display screen, to permit a user to link a selected numeric attribute to a function and to facilitate the communication of such a linking to a processor; and a scheduler, set forth in an image on the display screen, to permit a user to schedule execution of a selected function on a plurality of numeric values each of which is associated with an item and a selected numeric attribute in the database for the assignment of a selected descriptive word to one or more items based on the results of the execution of the function and such that the assigned descriptive word can be used in conducting a subsequent search of the database to identify the one or more items to which the descriptive word has been assigned and functions as an alias for the numeric value of the selected numeric attribute associated with each of the one or more items and to facilitate the communication of a scheduling of an execution of a selected function to a processor.
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37. A graphical user interface (GUI) comprising: a display screen; numeric attribute selector, set forth in an image on the display screen, to permit a user to select a numeric attribute that is associated with items in a database and to facilitate the communication of a selected numeric attribute to a processor, at least some of the items in the database have a numeric value for the selected attribute, wherein the numeric attribute describes a physical characteristic or property of the items; a descriptive word list selector, set forth in an image on the display screen, to permit a user to link a selected numeric attribute to a selected descriptive word in a descriptive word list and to facilitate the communication of such a linking to a processor; a function selector, set forth in an image on the display screen, to permit a user to link a selected numeric attribute to a function and to facilitate the communication of such a linking to a processor; and a scheduler, set forth in an image on the display screen, to permit a user to schedule execution of a selected function on a plurality of numeric values each of which is associated with an item and a selected numeric attribute in the database for the assignment of a selected descriptive word to one or more items based on the results of the execution of the function and such that the assigned descriptive word can be used in conducting a subsequent search of the database to identify the one or more items to which the descriptive word has been assigned and functions as an alias for the numeric value of the selected numeric attribute associated with each of the one or more items and to facilitate the communication of a scheduling of an execution of a selected function to a processor. 44. The GUI of claim 37 , wherein the scheduler permits the user to schedule repeating executing the function upon any change of items in the database and repeating assigning a descriptive word after repeating executing the function.
| 0.56203 |
8,150,676 | 7 | 8 |
7. At least one computer readable medium encoded with instructions that, when executed on a computer system, perform a method for automatically generating text, the method comprising acts of: accessing human-language text automatically generated using at least one template that includes at least some fixed text and at least one tag that serves as a placeholder to be filled in with automatically generated text; identifying instances of at least one portion of the human-language text appearing multiple times in the human language text; and automatically generating output text in a human-readable language at least in part by substituting one or more synonyms of the at least one portion for one or more of the identified instances of the at least one portion in the human-language text; wherein the one or more synonyms comprises a first synonym; wherein identifying instances of the at least one portion comprises identifying two instances of the at least one portion that appear in close proximity to each other in the human-language text; wherein substituting the one or more synonyms comprises substituting the first synonym for one of the two identified instances of the at least one portion in the human-language text; and wherein identifying the two instances of the at least one portion comprises identifying two instances that appear within a threshold number of characters or words of one another in the human-language text.
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7. At least one computer readable medium encoded with instructions that, when executed on a computer system, perform a method for automatically generating text, the method comprising acts of: accessing human-language text automatically generated using at least one template that includes at least some fixed text and at least one tag that serves as a placeholder to be filled in with automatically generated text; identifying instances of at least one portion of the human-language text appearing multiple times in the human language text; and automatically generating output text in a human-readable language at least in part by substituting one or more synonyms of the at least one portion for one or more of the identified instances of the at least one portion in the human-language text; wherein the one or more synonyms comprises a first synonym; wherein identifying instances of the at least one portion comprises identifying two instances of the at least one portion that appear in close proximity to each other in the human-language text; wherein substituting the one or more synonyms comprises substituting the first synonym for one of the two identified instances of the at least one portion in the human-language text; and wherein identifying the two instances of the at least one portion comprises identifying two instances that appear within a threshold number of characters or words of one another in the human-language text. 8. The at least one computer readable medium of claim 7 , wherein the at least one portion is a word and each of the one or more synonyms is a synonym of the word.
| 0.629545 |
9,183,323 | 24 | 25 |
24. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving a query; obtaining one or more search results responsive to the query, wherein each search result identifies a respective resource, each respective resource including text; processing each of the one or more search results, including: identifying a plurality of clauses in text of a respective resource identified by the search result, and calculating a similarity measure for each clause in the plurality of clauses, the similarity measure for a clause being a measure of the similarity between the clause and the query; determining that a first clause within a first resource identified by a first search result of the one or more search results has a if the similarity measure with the query that satisfies a threshold; in response to determining that a first clause within a first resource identified by a first search result of the one or more search results has a similarity measure with the query that satisfies a threshold, selecting the first clause as a suggested query phrase for the query; generating a search result snippet to be presented as part of the first search result in a presentation of the one or more search results, wherein the search result snippet includes the suggested query phrase as a selectable user interface element within the selected contiguous portion of the text of the resource, wherein user selection of the suggested query phrase within the search result snippet invokes the suggested query phrase as a new query; and providing the presentation of the one or more search results, including the search result snippet as part of the first search result, in response to the query.
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24. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving a query; obtaining one or more search results responsive to the query, wherein each search result identifies a respective resource, each respective resource including text; processing each of the one or more search results, including: identifying a plurality of clauses in text of a respective resource identified by the search result, and calculating a similarity measure for each clause in the plurality of clauses, the similarity measure for a clause being a measure of the similarity between the clause and the query; determining that a first clause within a first resource identified by a first search result of the one or more search results has a if the similarity measure with the query that satisfies a threshold; in response to determining that a first clause within a first resource identified by a first search result of the one or more search results has a similarity measure with the query that satisfies a threshold, selecting the first clause as a suggested query phrase for the query; generating a search result snippet to be presented as part of the first search result in a presentation of the one or more search results, wherein the search result snippet includes the suggested query phrase as a selectable user interface element within the selected contiguous portion of the text of the resource, wherein user selection of the suggested query phrase within the search result snippet invokes the suggested query phrase as a new query; and providing the presentation of the one or more search results, including the search result snippet as part of the first search result, in response to the query. 25. The system of claim 24 , wherein generating the search result snippet to be presented as part of the first search result in a presentation of the one or more search results comprises visually distinguishing the suggested query phrase from other text in the search result snippet.
| 0.5 |
9,798,748 | 25 | 31 |
25. A method of constructing a query with which to retrieve information from a database, the method comprising: representing graphically a first dataset of a database as a first icon and a second dataset of the database as a second icon in a canvas presented to a user on a display of a computer system; receiving an indication to combine the first dataset with the second dataset, wherein the indication is received in response to the first icon being graphically associated with the second icon in the canvas; based on the received indication and metadata associated with the first dataset and the second dataset, presenting to the user on the display of the computer system natural language query options for combining elements of the first and second datasets, the natural language query options including intuitive descriptions of the combining elements based on the metadata and an experience level of the user, the intuitive descriptions being different for users with different experience levels; in response to the user's selection of one of the presented natural language query options, generating a third icon in the canvas representing a combination dataset of elements of the first and second datasets; representing user-defined relationships between the first and second datasets and the combination dataset as a connected graph of the first, second and third icons, wherein the connected graph presents a graphical representation of the query to the user; constructing, by at least one processor of the computer system, a machine-readable structured query based on the connected graph; and returning data from the database, the returned data corresponding to an execution of the machine-readable structured query against the database.
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25. A method of constructing a query with which to retrieve information from a database, the method comprising: representing graphically a first dataset of a database as a first icon and a second dataset of the database as a second icon in a canvas presented to a user on a display of a computer system; receiving an indication to combine the first dataset with the second dataset, wherein the indication is received in response to the first icon being graphically associated with the second icon in the canvas; based on the received indication and metadata associated with the first dataset and the second dataset, presenting to the user on the display of the computer system natural language query options for combining elements of the first and second datasets, the natural language query options including intuitive descriptions of the combining elements based on the metadata and an experience level of the user, the intuitive descriptions being different for users with different experience levels; in response to the user's selection of one of the presented natural language query options, generating a third icon in the canvas representing a combination dataset of elements of the first and second datasets; representing user-defined relationships between the first and second datasets and the combination dataset as a connected graph of the first, second and third icons, wherein the connected graph presents a graphical representation of the query to the user; constructing, by at least one processor of the computer system, a machine-readable structured query based on the connected graph; and returning data from the database, the returned data corresponding to an execution of the machine-readable structured query against the database. 31. The method of claim 25 , wherein the user's selection of one of the presented natural language query options includes a selection to combine columns of the first and second datasets to create the combination dataset, which includes columns found in the first and second datasets that are chosen based on criteria specified by the user, wherein the method further comprises receiving the specified criteria from the user.
| 0.5 |
7,707,023 | 1 | 6 |
1. A method of automatically finding one or more answers to a natural language question in a computer stored natural language text database, wherein said natural language text database has been analyzed with respect to syntactic functions of constituents, lexical meaning of word tokens, and initial clause boundaries, and wherein said natural language question comprises a question clause, comprising the steps of: analyzing, in an analyzing means of a computer system, a computer readable representation of said question clause with respect to syntactic functions of its constituents and the lexical meaning of its word tokens; defining, in a defining means of a computer system, in response to the analysis step, a set of conditions for an initial clause in said natural language text database to constitute an answer to said question clause, said conditions comprising a condition stipulating that, for an initial clause in said natural language text database to constitute an answer to said questions clause, at least one of the constituents of said question clause should have a corresponding constituent in said initial clause having the same syntactic function and an equivalent lexical meaning; identifying, in an answer finding means of a computer system, initial clauses in said natural language text database that satisfy said conditions; and returning, in an answer finding means of a computer system, answers to said question clause by means of the identified initial clauses that match said conditions.
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1. A method of automatically finding one or more answers to a natural language question in a computer stored natural language text database, wherein said natural language text database has been analyzed with respect to syntactic functions of constituents, lexical meaning of word tokens, and initial clause boundaries, and wherein said natural language question comprises a question clause, comprising the steps of: analyzing, in an analyzing means of a computer system, a computer readable representation of said question clause with respect to syntactic functions of its constituents and the lexical meaning of its word tokens; defining, in a defining means of a computer system, in response to the analysis step, a set of conditions for an initial clause in said natural language text database to constitute an answer to said question clause, said conditions comprising a condition stipulating that, for an initial clause in said natural language text database to constitute an answer to said questions clause, at least one of the constituents of said question clause should have a corresponding constituent in said initial clause having the same syntactic function and an equivalent lexical meaning; identifying, in an answer finding means of a computer system, initial clauses in said natural language text database that satisfy said conditions; and returning, in an answer finding means of a computer system, answers to said question clause by means of the identified initial clauses that match said conditions. 6. The method according to claim 1 , wherein said set of conditions in the defining step comprises: a place adverb condition stipulating that a clause constitutes an answer to said question clause if a lexically headed constituent having the syntactic function of place adverb of said questions clause has a corresponding lexically headed constituent in said clause having the syntactic function of place adverb and having an equivalent lexical meaning.
| 0.502198 |
8,380,706 | 1 | 6 |
1. A computer-implemented method, comprising: receiving a search query, wherein the search query is comprised of two or more search terms; determining if the search query would ordinarily result in a results page having no sponsored search advertising, wherein sponsored search advertising is advertising wherein a sponsor pays to have a particular search term combination return a results page in which the sponsor's link is displayed as a substantive search result; if the search query would ordinarily result in a results page having no sponsored search advertising: aggregating sponsored search advertising that would ordinarily be displayed in response to each of the two or more search terms individually; selecting from the aggregated sponsored search advertising one or more sponsored search advertisements to display; providing a financial incentive, to pay for sponsored search advertising for the search query, to one or more sponsored search advertisers of the selected one or more sponsored search advertisements, wherein the financial incentive is one that is not offered when the search query would ordinarily result in a results page having sponsored search advertising; and displaying the selected advertisements on a results page responsive to the search query.
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1. A computer-implemented method, comprising: receiving a search query, wherein the search query is comprised of two or more search terms; determining if the search query would ordinarily result in a results page having no sponsored search advertising, wherein sponsored search advertising is advertising wherein a sponsor pays to have a particular search term combination return a results page in which the sponsor's link is displayed as a substantive search result; if the search query would ordinarily result in a results page having no sponsored search advertising: aggregating sponsored search advertising that would ordinarily be displayed in response to each of the two or more search terms individually; selecting from the aggregated sponsored search advertising one or more sponsored search advertisements to display; providing a financial incentive, to pay for sponsored search advertising for the search query, to one or more sponsored search advertisers of the selected one or more sponsored search advertisements, wherein the financial incentive is one that is not offered when the search query would ordinarily result in a results page having sponsored search advertising; and displaying the selected advertisements on a results page responsive to the search query. 6. The computer-implemented method of claim 1 , wherein the sponsored search advertising is pay-per-click advertising.
| 0.811502 |
8,041,805 | 10 | 12 |
10. One or more computer-readable non-transitory storage media embodying software operable when executed to: provide a first element viewable on each of a plurality of particular web pages of a website upon initial display of a particular web page and soliciting page-specific user feedback concerning the particular web page upon initial display of the particular web page, the first element appearing identically and behaving consistently on each of the plurality of particular web pages; provide a second element displayed in response to user selection of the first element and soliciting one or more page-specific subjective ratings of the particular web page and one or more associated page-specific open-ended comments concerning the particular web page, the second element appearing identically and behaving consistently each time it is displayed in response to user selection of the first element viewable on a particular web page; receive the user selection of the first element and initiate display of the second element in response; and receive the page-specific user feedback comprising one or more page-specific subjective ratings of the particular web page and one or more associated page-specific open-ended comments concerning the particular web page for reporting to an interested party, the page-specific user feedback concerning the particular webpages having been provided by a user while the user remained at the particular web page, and the page-specific user feedback allowing the interested party to access page-specific subjective ratings and associated page-specific open-ended comments across the plurality of particular web pages to identify one or more particular web pages for which the page-specific user feedback is notable relative to page-specific user feedback for other particular web pages; wherein the first element is viewable within a browser window upon initial display of the particular web page and remains viewable within the browse window, at east prior to the user selection, regardless of user scrolling.
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10. One or more computer-readable non-transitory storage media embodying software operable when executed to: provide a first element viewable on each of a plurality of particular web pages of a website upon initial display of a particular web page and soliciting page-specific user feedback concerning the particular web page upon initial display of the particular web page, the first element appearing identically and behaving consistently on each of the plurality of particular web pages; provide a second element displayed in response to user selection of the first element and soliciting one or more page-specific subjective ratings of the particular web page and one or more associated page-specific open-ended comments concerning the particular web page, the second element appearing identically and behaving consistently each time it is displayed in response to user selection of the first element viewable on a particular web page; receive the user selection of the first element and initiate display of the second element in response; and receive the page-specific user feedback comprising one or more page-specific subjective ratings of the particular web page and one or more associated page-specific open-ended comments concerning the particular web page for reporting to an interested party, the page-specific user feedback concerning the particular webpages having been provided by a user while the user remained at the particular web page, and the page-specific user feedback allowing the interested party to access page-specific subjective ratings and associated page-specific open-ended comments across the plurality of particular web pages to identify one or more particular web pages for which the page-specific user feedback is notable relative to page-specific user feedback for other particular web pages; wherein the first element is viewable within a browser window upon initial display of the particular web page and remains viewable within the browse window, at east prior to the user selection, regardless of user scrolling. 12. The media of claim 10 , wherein the software associated with the second element comprises a call to a directory containing a script to receive the page-specific user feedback.
| 0.747175 |
8,505,094 | 14 | 18 |
14. A method of detecting a malicious URL, said method comprising: retrieving HTML code representing a parent Web page; scanning said HTML code and identifying at least one embedded URL of said HTML code; retrieving the content of a child Web page identified by said embedded URL; determining relevancy features representing the relevancy between said HTML code and said retrieved content; producing a numerical relevancy vector indicating said relevancy features; inputting said numerical relevancy vector into a classifier algorithm; and outputting a score from said classifier algorithm indicating the likelihood that said embedded URL of said HTML code is a malicious URL.
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14. A method of detecting a malicious URL, said method comprising: retrieving HTML code representing a parent Web page; scanning said HTML code and identifying at least one embedded URL of said HTML code; retrieving the content of a child Web page identified by said embedded URL; determining relevancy features representing the relevancy between said HTML code and said retrieved content; producing a numerical relevancy vector indicating said relevancy features; inputting said numerical relevancy vector into a classifier algorithm; and outputting a score from said classifier algorithm indicating the likelihood that said embedded URL of said HTML code is a malicious URL. 18. A method as recited in claim 14 further comprising: determining a page rank of said child Web page identified by said embedded URL; producing a numerical referring vector that at least indicates said determined page rank; and inputting said numerical referring vector into said classifier algorithm.
| 0.662584 |
9,542,165 | 1 | 8 |
1. A computer-implemented method for model-to-model transformation, the method being executed using one or more processors and comprising: providing a source meta-model, the source meta-model having a plurality of classes and one or more references; receiving, by the one or more processors, a first user input to a graphical editor; processing the first user input to define a transformation by providing a plurality of class modules and one or more reference modules, each class module corresponding to a class of the plurality of classes and comprising a module type and each reference module corresponding to a reference of the one or more references; automatically generating a transformation code based on the plurality of class modules and the one or more reference modules, the transformation code defining the transformation of a source model comprising a business process modeling notation into a petri-net model and using the module type of each class module to determine how and when to fuse the plurality of class modules of the source model based on a pattern of the plurality of class modules; receiving, by the one or more processors, the source model, the source model being provided based on the source meta-model and being provided in a computer-readable file that is stored in memory; and generating, by the one or more processors, a simulation model based on the source model and the transformation code.
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1. A computer-implemented method for model-to-model transformation, the method being executed using one or more processors and comprising: providing a source meta-model, the source meta-model having a plurality of classes and one or more references; receiving, by the one or more processors, a first user input to a graphical editor; processing the first user input to define a transformation by providing a plurality of class modules and one or more reference modules, each class module corresponding to a class of the plurality of classes and comprising a module type and each reference module corresponding to a reference of the one or more references; automatically generating a transformation code based on the plurality of class modules and the one or more reference modules, the transformation code defining the transformation of a source model comprising a business process modeling notation into a petri-net model and using the module type of each class module to determine how and when to fuse the plurality of class modules of the source model based on a pattern of the plurality of class modules; receiving, by the one or more processors, the source model, the source model being provided based on the source meta-model and being provided in a computer-readable file that is stored in memory; and generating, by the one or more processors, a simulation model based on the source model and the transformation code. 8. The method of claim 1 , wherein each module includes a plurality of identifiers associated therewith.
| 0.858696 |
8,750,630 | 14 | 15 |
14. A computer system for watermarking content stored in a plurality of corpora each having a plurality of corpuses, the computer system comprising: at least one processing unit; memory operably associated with the at least one processing unit; and a feature watermark tool storable in memory and executable by the at least one processing unit, the tool comprising: an input component configured to receive a data set of content from a corpus within one of the plurality of corpora; a natural language processing (NLP) stack including a plurality of NLP analytics each configured to extract features from the data set; and a feature watermark generator configured to generate a feature watermark for each of the plurality of NLP analytics for features extracted therefrom, the feature watermark generator further configured to form a watermark tree from each of the feature watermarks, the watermark tree representing a hierarchical relationship between each of the feature watermarks generated from each of the plurality of NLP analytics, the watermark tree defining hierarchical pointers that point out inherited watermarks that exist between the feature watermarks according to the hierarchical relationship, the watermark tree including a time stamp specifying a time that the data set was accessed from the corpus.
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14. A computer system for watermarking content stored in a plurality of corpora each having a plurality of corpuses, the computer system comprising: at least one processing unit; memory operably associated with the at least one processing unit; and a feature watermark tool storable in memory and executable by the at least one processing unit, the tool comprising: an input component configured to receive a data set of content from a corpus within one of the plurality of corpora; a natural language processing (NLP) stack including a plurality of NLP analytics each configured to extract features from the data set; and a feature watermark generator configured to generate a feature watermark for each of the plurality of NLP analytics for features extracted therefrom, the feature watermark generator further configured to form a watermark tree from each of the feature watermarks, the watermark tree representing a hierarchical relationship between each of the feature watermarks generated from each of the plurality of NLP analytics, the watermark tree defining hierarchical pointers that point out inherited watermarks that exist between the feature watermarks according to the hierarchical relationship, the watermark tree including a time stamp specifying a time that the data set was accessed from the corpus. 15. The computer system according to claim 14 , wherein the feature watermark generator is further configured to store the watermark tree in the corpus with a corpus time stamp and apply a corpora time stamp to the corpora associated with the corpus in response to storing the watermark in the corpus, the corpus including a plurality of watermark trees each formed at a different time stamp, and the corpora including a plurality of corpora time stamps each associated with storing one of the plurality of watermark trees in the corpus.
| 0.5 |
8,370,352 | 16 | 17 |
16. The system of claim 15 , wherein the text is determined to be within the context of the entered query string when it precedes or follows the instance of the entered query string up to a terminal character.
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16. The system of claim 15 , wherein the text is determined to be within the context of the entered query string when it precedes or follows the instance of the entered query string up to a terminal character. 17. The method of claim 16 , wherein the terminal character comprises one of punctuation, a bullet, or a space.
| 0.5 |
10,082,931 | 16 | 17 |
16. A computer-readable memory device with instructions stored thereon to transition a command user interface (UI) between a toolbar UI and a full menu UI based on a use context, the instructions comprising: detecting a request to interact with a message, wherein the request includes one of a first intent to read to message and a second intent to author the message; analyzing attributes associated with a usage of the message, wherein the attributes include at least a historical use pattern associated with the usage of the message; automatically identifying the use context based on the analysis of the attributes associated with the usage of the message; automatically identifying one of a reading mode and an authoring mode as a presentation mode based on the use context; automatically selecting one of the toolbar UI and the full menu UI as the command UI based on the identified presentation mode, wherein the toolbar UI includes reading mode commands and the full menu UI includes authoring mode commands; and displaying the selected command UI in the identified presentation mode.
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16. A computer-readable memory device with instructions stored thereon to transition a command user interface (UI) between a toolbar UI and a full menu UI based on a use context, the instructions comprising: detecting a request to interact with a message, wherein the request includes one of a first intent to read to message and a second intent to author the message; analyzing attributes associated with a usage of the message, wherein the attributes include at least a historical use pattern associated with the usage of the message; automatically identifying the use context based on the analysis of the attributes associated with the usage of the message; automatically identifying one of a reading mode and an authoring mode as a presentation mode based on the use context; automatically selecting one of the toolbar UI and the full menu UI as the command UI based on the identified presentation mode, wherein the toolbar UI includes reading mode commands and the full menu UI includes authoring mode commands; and displaying the selected command UI in the identified presentation mode. 17. The computer-readable memory device of claim 16 , wherein the instructions further comprise: detecting an intent to read the message, wherein the intent includes one or more of a read selection to read the message and a read command to read the message; automatically identifying the reading mode as the presentation mode; and automatically placing the reading mode commands on the toolbar UI to present the toolbar UI as the command UI, wherein the reading mode commands include one or more of a reply command, a reply to all command, a forward command, and a delete command.
| 0.5 |
9,817,808 | 10 | 16 |
10. An apparatus comprising: a memory; and a processor operatively coupled to the memory and configured to: generate a translation pair database by: building a crawl database by crawling one or more web addresses listed in a web address database, and collecting one or more documents associated with a source language and one or more documents associated with a target language; building a machine translated database by translating the one or more documents associated with the source language into the target language and the one or more documents associated with the target language into the source language; extracting one or more sets of related terms from the crawl database and one or more sets of related terms from the machine translated database; and building the translation pair database by comparing the sets of related terms extracted from the crawl database and the machine translated database to generate one or more translation pairs; translate a communication received from a device and associated with the source language to generate a translated communication associated with the target language, wherein the translated communication comprises a transliterated term; and provide a correct translation for the transliterated term to the device, wherein, in providing the correct translation for the transliterated term to the device, the processor is configured to: extract a set of terms from one of the received communication and the translated communication, the set of terms comprising the transliterated term and a term related to the transliterated term; obtain at least one translation pair from the translation pair database; compare the extracted set of terms with the at least one translation pair to identify at least one candidate translation for the transliterated term; and select the correct translation for the transliterated term from the at least one candidate translation, wherein the correct translation selected from the at least one candidate translation is provided to the device.
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10. An apparatus comprising: a memory; and a processor operatively coupled to the memory and configured to: generate a translation pair database by: building a crawl database by crawling one or more web addresses listed in a web address database, and collecting one or more documents associated with a source language and one or more documents associated with a target language; building a machine translated database by translating the one or more documents associated with the source language into the target language and the one or more documents associated with the target language into the source language; extracting one or more sets of related terms from the crawl database and one or more sets of related terms from the machine translated database; and building the translation pair database by comparing the sets of related terms extracted from the crawl database and the machine translated database to generate one or more translation pairs; translate a communication received from a device and associated with the source language to generate a translated communication associated with the target language, wherein the translated communication comprises a transliterated term; and provide a correct translation for the transliterated term to the device, wherein, in providing the correct translation for the transliterated term to the device, the processor is configured to: extract a set of terms from one of the received communication and the translated communication, the set of terms comprising the transliterated term and a term related to the transliterated term; obtain at least one translation pair from the translation pair database; compare the extracted set of terms with the at least one translation pair to identify at least one candidate translation for the transliterated term; and select the correct translation for the transliterated term from the at least one candidate translation, wherein the correct translation selected from the at least one candidate translation is provided to the device. 16. The apparatus of claim 10 , wherein the one or more sets of related terms are extracted based on an “is-a” relationship.
| 0.907186 |
9,665,642 | 9 | 14 |
9. A non-transitory computer-readable medium encoded with instructions for performing a method for identifying digital content related to a portion of a block of text, the method comprising: receiving a block of text for which related digital content is to be identified, wherein the digital content comprises still or moving digital images; providing the block of text to a computer-implemented service that: separates the block of text into one or more segments comprising phrases or individual words; searches a dataset of digital content based on the one or more segmented phrases or individual words to identify one or more digital content items related to the one or more segmented phrases or individual words; and retrieves from the dataset one or more digital content identifiers associated with the identified one or more digital content items; receiving, from the computer-implemented service, an indication of the segmented phrases or individual words for which the digital content identifiers were retrieved; receiving, from the computer-implemented service, the retrieved digital content identifiers; augmenting the block of text with visual indicators indicating the segmented phrases or individual words in the block of text for which a digital content identifier was retrieved; receiving a selection of one of the segmented phrases or individual words and presenting to a user the digital content items associated with the digital content identifiers for the selected segmented phrase or individual word; receiving a selection of one or more of the presented digital content items from the user; and updating the block of text with the selected digital content items.
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9. A non-transitory computer-readable medium encoded with instructions for performing a method for identifying digital content related to a portion of a block of text, the method comprising: receiving a block of text for which related digital content is to be identified, wherein the digital content comprises still or moving digital images; providing the block of text to a computer-implemented service that: separates the block of text into one or more segments comprising phrases or individual words; searches a dataset of digital content based on the one or more segmented phrases or individual words to identify one or more digital content items related to the one or more segmented phrases or individual words; and retrieves from the dataset one or more digital content identifiers associated with the identified one or more digital content items; receiving, from the computer-implemented service, an indication of the segmented phrases or individual words for which the digital content identifiers were retrieved; receiving, from the computer-implemented service, the retrieved digital content identifiers; augmenting the block of text with visual indicators indicating the segmented phrases or individual words in the block of text for which a digital content identifier was retrieved; receiving a selection of one of the segmented phrases or individual words and presenting to a user the digital content items associated with the digital content identifiers for the selected segmented phrase or individual word; receiving a selection of one or more of the presented digital content items from the user; and updating the block of text with the selected digital content items. 14. The non-transitory computer-readable medium of claim 9 , wherein the digital content items are ranked based on which items are likely to be more relevant to the user, and wherein presenting the digital content items is based on the items' rankings.
| 0.75534 |
8,583,707 | 8 | 11 |
8. The method according to claim 6 , further comprising the steps of: receiving an input of changed information for said tabular view; receiving an input of said conversion processing description with a view update rule, wherein said view update rule is a rule for an update of said hierarchical multi-dimensional data model; and reflecting a content of said changed information in said hierarchical multi-dimensional data model, wherein said hierarchical multi-dimensional data model complies with said conversion processing description.
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8. The method according to claim 6 , further comprising the steps of: receiving an input of changed information for said tabular view; receiving an input of said conversion processing description with a view update rule, wherein said view update rule is a rule for an update of said hierarchical multi-dimensional data model; and reflecting a content of said changed information in said hierarchical multi-dimensional data model, wherein said hierarchical multi-dimensional data model complies with said conversion processing description. 11. The method according to claim 8 , further comprising the step of: using said extraction queries as said conversion processing description, wherein said changed information comprises a change of a value of a cell in said tabular view.
| 0.512346 |
8,689,117 | 17 | 19 |
17. A system comprising: a processor; a storage device coupled to the processor and configurable for storing instructions, which, when executed by the processor cause the processor to perform operations comprising: generating, by the system, a markup language document for a webpage, the markup language document comprising JavaScript code, a comment tag, a variable, and an attribute that is separate from the variable and that is associated with a second markup language tag other than the comment tag, the comment tag being separate from the JavaScript code and arranged to indicate particular content in the markup language document that is not to be processed for display when the comment tag is interpreted as a comment tag by a first computing device that is caused to execute the markup language document; wherein the markup language document is structured to cause the first computing device, when the markup language document is executed by the first computing device and in response to the variable being determined to hold a first value, (i) to render a first version of the webpage, wherein the rendering includes using the JavaScript code to write the second markup language tag in the markup language document such that the characters that form the comment tag are set as a value of the attribute and the comment tag is ignored as a comment tag as a result of the first computing device being caused to interpret the characters that form the comment tag as the value of the attribute, and (ii) to provide, to the system for monitoring a conversion rate of the webpage, first conversion data that indicates that the first version of the webpage has been rendered, and wherein the markup language document is further structured to cause the first computing device, when the markup language document is executed by the first computing device and in response to the variable being determined to hold a second value that is different from the first value, (i) to render a second version of the webpage, wherein the rendering includes interpreting the comment tag normally such that the particular content in the markup language document indicated by the comment tag is not processed by the first computing device for display, and (ii) to provide, to the system for monitoring a conversion rate of the webpage, second conversion data that indicates that the second version of the webpage has been rendered; and transmitting the markup language document to the first computing device.
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17. A system comprising: a processor; a storage device coupled to the processor and configurable for storing instructions, which, when executed by the processor cause the processor to perform operations comprising: generating, by the system, a markup language document for a webpage, the markup language document comprising JavaScript code, a comment tag, a variable, and an attribute that is separate from the variable and that is associated with a second markup language tag other than the comment tag, the comment tag being separate from the JavaScript code and arranged to indicate particular content in the markup language document that is not to be processed for display when the comment tag is interpreted as a comment tag by a first computing device that is caused to execute the markup language document; wherein the markup language document is structured to cause the first computing device, when the markup language document is executed by the first computing device and in response to the variable being determined to hold a first value, (i) to render a first version of the webpage, wherein the rendering includes using the JavaScript code to write the second markup language tag in the markup language document such that the characters that form the comment tag are set as a value of the attribute and the comment tag is ignored as a comment tag as a result of the first computing device being caused to interpret the characters that form the comment tag as the value of the attribute, and (ii) to provide, to the system for monitoring a conversion rate of the webpage, first conversion data that indicates that the first version of the webpage has been rendered, and wherein the markup language document is further structured to cause the first computing device, when the markup language document is executed by the first computing device and in response to the variable being determined to hold a second value that is different from the first value, (i) to render a second version of the webpage, wherein the rendering includes interpreting the comment tag normally such that the particular content in the markup language document indicated by the comment tag is not processed by the first computing device for display, and (ii) to provide, to the system for monitoring a conversion rate of the webpage, second conversion data that indicates that the second version of the webpage has been rendered; and transmitting the markup language document to the first computing device. 19. The system of claim 17 , wherein: as a result of the variable being determined to hold the first value, ignoring the comment tag as a comment tag comprises disregarding the comment tag even though the comment tag remains present in the markup language document.
| 0.638965 |
10,147,423 | 11 | 13 |
11. The method of claim 10 ; further comprising receiving a sensor input comprising an image, audio or biometric data.
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11. The method of claim 10 ; further comprising receiving a sensor input comprising an image, audio or biometric data. 13. The method of claim 11 , further comprising: determining the intended recipient of the user speech based on the sensor input.
| 0.705479 |
7,580,931 | 9 | 13 |
9. A computer-readable storage device containing instructions for controlling a computer system to identify web pages for a search result for a query, the web pages being organized into web sites, the web pages of a web site being hierarchically organized wherein the web pages of the web site have ancestor/descendant relationships, each web page of a web site being a root web page of a subsite of the web site, a subsite includes the root web page of the subsite and its descendant web pages, each web page having a feature, comprising: receiving from a user a query; identifying web pages that are related to the received query; and for each identified web page, calculating a subsite feature for the subsite with the identified web page being the root web page of the subsite based on a contribution from the feature of the identified web page and a contribution from the features of the descendant web pages of the identified web page, such that the contribution of the features of a descendant web page decreases as an ancestral distance between the identified web page and the descendant web page increases; and determining relevance of the identified web page to the query based on the calculated subsite feature of the subsite; and presenting to the user an indication of the identified web pages in an order that is based at least in part on the determined relevance of the identified web pages wherein the subsite feature is represented by the following: F [ S ( p s ) ] = α ( 0 ) f ( p s ) + 1 R ( p s ) ∑ u = 1 h ( p s ) - 1 [ α ( u ) ∑ p i 1 ∈ R ( p s ) ∑ p i 2 ∈ R ( p i 1 ) ⋯ ∑ p i u ∈ R ( p i u - 1 ) f ( p i u ) ∏ k = 1 u R ( p i k ) ] where F[S(p s )] represents the feature of the subsites with root web page p s , h(p s ) represents height of a subtree with the root web page of p s , R(p s ) represents the child documents of p s , ∥a∥ represents the number of elements of a, f(p iu ) represents the feature of web page p iu , and where α is represented as follows:
α( Δl )=λ Δl where Δl represents the ancestral distance between the root web page and a descendant web page and λ represents a parameter to control the amount of decrease.
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9. A computer-readable storage device containing instructions for controlling a computer system to identify web pages for a search result for a query, the web pages being organized into web sites, the web pages of a web site being hierarchically organized wherein the web pages of the web site have ancestor/descendant relationships, each web page of a web site being a root web page of a subsite of the web site, a subsite includes the root web page of the subsite and its descendant web pages, each web page having a feature, comprising: receiving from a user a query; identifying web pages that are related to the received query; and for each identified web page, calculating a subsite feature for the subsite with the identified web page being the root web page of the subsite based on a contribution from the feature of the identified web page and a contribution from the features of the descendant web pages of the identified web page, such that the contribution of the features of a descendant web page decreases as an ancestral distance between the identified web page and the descendant web page increases; and determining relevance of the identified web page to the query based on the calculated subsite feature of the subsite; and presenting to the user an indication of the identified web pages in an order that is based at least in part on the determined relevance of the identified web pages wherein the subsite feature is represented by the following: F [ S ( p s ) ] = α ( 0 ) f ( p s ) + 1 R ( p s ) ∑ u = 1 h ( p s ) - 1 [ α ( u ) ∑ p i 1 ∈ R ( p s ) ∑ p i 2 ∈ R ( p i 1 ) ⋯ ∑ p i u ∈ R ( p i u - 1 ) f ( p i u ) ∏ k = 1 u R ( p i k ) ] where F[S(p s )] represents the feature of the subsites with root web page p s , h(p s ) represents height of a subtree with the root web page of p s , R(p s ) represents the child documents of p s , ∥a∥ represents the number of elements of a, f(p iu ) represents the feature of web page p iu , and where α is represented as follows:
α( Δl )=λ Δl where Δl represents the ancestral distance between the root web page and a descendant web page and λ represents a parameter to control the amount of decrease. 13. The computer-readable storage device of claim 9 wherein the subsite features are calculated independently of a query and stored for use when identifying search results of queries.
| 0.667273 |
7,878,810 | 6 | 7 |
6. The method of claim 1 , wherein the first task measures mathematical ability or content knowledge of the user.
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6. The method of claim 1 , wherein the first task measures mathematical ability or content knowledge of the user. 7. The method of claim 6 , wherein the second task measures conscientiousness or test anxiety of the user.
| 0.5 |
8,406,997 | 12 | 15 |
12. The apparatus of claim 11 , wherein the processor configured to process is further configured to: correlate the first and second roads with stored information to identify first and second route links associated with corresponding ones of the first and second roads; determine whether the first road intersects the second road; and construct a portion of the routing graph that includes the identified route links when the first and second roads intersect, the identified route links being connected within the routing graph by a corresponding one of the nodes.
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12. The apparatus of claim 11 , wherein the processor configured to process is further configured to: correlate the first and second roads with stored information to identify first and second route links associated with corresponding ones of the first and second roads; determine whether the first road intersects the second road; and construct a portion of the routing graph that includes the identified route links when the first and second roads intersect, the identified route links being connected within the routing graph by a corresponding one of the nodes. 15. The apparatus of claim 12 , wherein the processor configured to determine is further configured to: determine that the first and second roads are directly connected when the first and second roads are associated with the same route link.
| 0.646628 |
8,566,313 | 1 | 9 |
1. A computer-implemented method for enabling a host application to retrieve document images of a plurality of physical documents stored in a document management system through an enabler application that is used for creating an association between at least one field on an interface of the host application and at least one field of the document management system, the method comprising steps of: (a) executing the enabler application and the host application by an execution application; (b) displaying the interface of the host application while the host application is executing during run-time, wherein the interface of the host application contains the at least one field; and (c) while the interface is being displayed, creating an association using the enabler application between the at least one field on the interface of the host application and the at least one field of the document management system that is used for retrieving the document images from the document management system, wherein the association identifies the at least one field on the interface based upon a position of the at least one field on the interface, (d) in response to receiving an indication at the enabler application that the at least one field on the interface is an interactive field, when a user performs an interaction with the interactive field, a query is immediately performed based on existing data in the interactive field, and any document images retrieved from the document management system are displayed based on the performed interaction; wherein data provided for the at least one field on the interface of the host application during the execution of the host application is captured based upon the position of the at least one field on the interface, and wherein the data captured from the at least one field is used to generate bar codes based upon the created association; the generated bar codes are associated with one or more physical documents for automating input of the one or more physical documents into the document management system; the bar codes are generated before the one or more physical documents exist; an indexing process is performed from the executing application, wherein the indexing process assigns an index record including keyword values to corresponding physical documents from the data provided to the interface, during the indexing process, the user matches data currently displayed on the interface of the host application with a particular physical document to be indexed in the document management system to eliminate needs to re-key index information of the particular physical document in the document management system; and the index record is associated with the plurality of physical documents.
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1. A computer-implemented method for enabling a host application to retrieve document images of a plurality of physical documents stored in a document management system through an enabler application that is used for creating an association between at least one field on an interface of the host application and at least one field of the document management system, the method comprising steps of: (a) executing the enabler application and the host application by an execution application; (b) displaying the interface of the host application while the host application is executing during run-time, wherein the interface of the host application contains the at least one field; and (c) while the interface is being displayed, creating an association using the enabler application between the at least one field on the interface of the host application and the at least one field of the document management system that is used for retrieving the document images from the document management system, wherein the association identifies the at least one field on the interface based upon a position of the at least one field on the interface, (d) in response to receiving an indication at the enabler application that the at least one field on the interface is an interactive field, when a user performs an interaction with the interactive field, a query is immediately performed based on existing data in the interactive field, and any document images retrieved from the document management system are displayed based on the performed interaction; wherein data provided for the at least one field on the interface of the host application during the execution of the host application is captured based upon the position of the at least one field on the interface, and wherein the data captured from the at least one field is used to generate bar codes based upon the created association; the generated bar codes are associated with one or more physical documents for automating input of the one or more physical documents into the document management system; the bar codes are generated before the one or more physical documents exist; an indexing process is performed from the executing application, wherein the indexing process assigns an index record including keyword values to corresponding physical documents from the data provided to the interface, during the indexing process, the user matches data currently displayed on the interface of the host application with a particular physical document to be indexed in the document management system to eliminate needs to re-key index information of the particular physical document in the document management system; and the index record is associated with the plurality of physical documents. 9. The method of claim 1 , wherein documents images stored in the document management system are retrieved by performing a keyboard event on the host application's field that contains a keyword value.
| 0.766355 |
7,778,864 | 6 | 13 |
6. A system for identifying sourcing event metrics for analyzing a supplier, comprising: a consumer computing device for identifying a sourcing event and for querying potential suppliers, the consumer computing device operable to select a first previously created sourcing event and a second previously created sourcing event, wherein two or more predefined metrics are associated with the first previously created sourcing event; a database for storing two or more predefined metrics associated with new sourcing event; a metric identification engine of the consumer computing device for populating a new sourcing event with the predefined metrics, each predefined metric operable to trigger a response from a potential supplier, and the second previously created sourcing event, wherein the second previously created sourcing event is used as a predefined metric of the first previously created sourcing event, the metric identification engine of the consumer computing device further programmed to: determine a weighting factor predefined for one or more of the predefined metrics, for each predefined metric having a fixed set of possible responses, determine a points value predefined for one or more possible response, provide a comment field and receive comments related to a metric from the supplier; for each predefined metric providing for the comment field, assign a points value to one or more keywords appearing in the comment field before receiving a response to the new sourcing event, redefine a points value for one or more of the predefined metrics; and a metric evaluation engine of the consumer computing device for determining whether previously obtained data associated with the predefined metrics exists for each of a plurality of potential suppliers, and for each potential supplier: pre-populate a response field associated with each of the predefined metrics where previously obtained data exists for that predefined metric, query the potential supplier for response data associated with the predefined metrics, received a second set of response data in response to the query, determine the points values earned for each of the predefined metrics based upon the response data, multiply the points values for each of the predefined metrics by the weighting factor for the respective predefined metric to generate a weighted points value for each predefined metric, sum the weighted points values to generate an overall point total, and rank each supplier based on the overall point total and displaying the ranking to a user, whereby the user is able to select a supplier based on the rank.
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6. A system for identifying sourcing event metrics for analyzing a supplier, comprising: a consumer computing device for identifying a sourcing event and for querying potential suppliers, the consumer computing device operable to select a first previously created sourcing event and a second previously created sourcing event, wherein two or more predefined metrics are associated with the first previously created sourcing event; a database for storing two or more predefined metrics associated with new sourcing event; a metric identification engine of the consumer computing device for populating a new sourcing event with the predefined metrics, each predefined metric operable to trigger a response from a potential supplier, and the second previously created sourcing event, wherein the second previously created sourcing event is used as a predefined metric of the first previously created sourcing event, the metric identification engine of the consumer computing device further programmed to: determine a weighting factor predefined for one or more of the predefined metrics, for each predefined metric having a fixed set of possible responses, determine a points value predefined for one or more possible response, provide a comment field and receive comments related to a metric from the supplier; for each predefined metric providing for the comment field, assign a points value to one or more keywords appearing in the comment field before receiving a response to the new sourcing event, redefine a points value for one or more of the predefined metrics; and a metric evaluation engine of the consumer computing device for determining whether previously obtained data associated with the predefined metrics exists for each of a plurality of potential suppliers, and for each potential supplier: pre-populate a response field associated with each of the predefined metrics where previously obtained data exists for that predefined metric, query the potential supplier for response data associated with the predefined metrics, received a second set of response data in response to the query, determine the points values earned for each of the predefined metrics based upon the response data, multiply the points values for each of the predefined metrics by the weighting factor for the respective predefined metric to generate a weighted points value for each predefined metric, sum the weighted points values to generate an overall point total, and rank each supplier based on the overall point total and displaying the ranking to a user, whereby the user is able to select a supplier based on the rank. 13. The system of claim 6 , wherein the query for the response data is a query automatically associated with the predefined metrics.
| 0.832487 |
7,865,955 | 9 | 10 |
9. The apparatus according to claim 8 , wherein the substring frequency table includes a field denoting an observation period of each entry for performing an initialization operation per each entry, and the substring extractor increases substring generation frequency value of the entry when an observation period of a corresponding entry is not elapsed, or initializes a substring generation frequency value of the entry and then increases the substring generation frequency value from a corresponding initialization value when an observation period of a corresponding entry is elapsed.
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9. The apparatus according to claim 8 , wherein the substring frequency table includes a field denoting an observation period of each entry for performing an initialization operation per each entry, and the substring extractor increases substring generation frequency value of the entry when an observation period of a corresponding entry is not elapsed, or initializes a substring generation frequency value of the entry and then increases the substring generation frequency value from a corresponding initialization value when an observation period of a corresponding entry is elapsed. 10. The apparatus according to claim 9 , wherein the substring extractor initializes the substring generation frequency value of the corresponding entry to 0 or by a constant rate of a generating value.
| 0.5 |
8,688,412 | 5 | 6 |
5. The apparatus of claim 4 , wherein: the model information further includes algebraic states and differential states; the algebraic states correspond to the algebraic equations; and the differential states correspond to the differential equations.
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5. The apparatus of claim 4 , wherein: the model information further includes algebraic states and differential states; the algebraic states correspond to the algebraic equations; and the differential states correspond to the differential equations. 6. The apparatus of claim 5 , wherein: the model information further includes inputs; and one or more of the differential equations represent one or more corresponding relationships between one or more of the differential states, one or more of the inputs, and one or more of the algebraic states.
| 0.5 |
8,150,874 | 1 | 4 |
1. A computer implemented method for selecting external corpora to integrate into primary internet search engine results in response to a query, comprising: receiving a query, at least one server computer, from a client computer over a network; storing an offline model probability in memory; processing said query by: computing a first probabilistic estimate of relevance of external corpora to said query from offline query-related data from said offline model probability; combining said offline query-related data with user feedback data to determine a second probabilistic estimate of relevance of said external corpora to said query; selecting said external corpora to integrate into a response to said query based on said second probabilistic estimate of relevance of said external corpora to said query; and transmitting over said network, for display on said client computer search results for said query that include said external corpora selected.
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1. A computer implemented method for selecting external corpora to integrate into primary internet search engine results in response to a query, comprising: receiving a query, at least one server computer, from a client computer over a network; storing an offline model probability in memory; processing said query by: computing a first probabilistic estimate of relevance of external corpora to said query from offline query-related data from said offline model probability; combining said offline query-related data with user feedback data to determine a second probabilistic estimate of relevance of said external corpora to said query; selecting said external corpora to integrate into a response to said query based on said second probabilistic estimate of relevance of said external corpora to said query; and transmitting over said network, for display on said client computer search results for said query that include said external corpora selected. 4. The method of claim 1 , wherein combining said offline query-related data with user feedback data to determine a second probabilistic estimate of relevance of said external corpora to said query comprises: a) forming a prior probability distribution from the offline model probability; b) adding the user feedback data to the prior probability distribution, and; c) from elements a) and b), obtaining a posterior probability distribution, said posterior probability distribution being a second probabilistic estimate of relevance of said external corpora to said first query.
| 0.5 |
9,519,463 | 1 | 17 |
1. A method for generating a flowchart for a test program, comprising: selecting, using a processor, one of a plurality of programming language configuration files stored in at least one memory component, based on a programming language of the test program, each of the programming language configuration files being specific to one programming language and including regular expressions to identify lines of program code of the test program using keywords and constructs of the programming language; selecting, using a processor, one of a plurality of test station configuration files stored in at least one memory component, based on a test station for which the test program is written, each of the test station configuration files being specific to one test station and including regular expressions to identify lines of code of the test program that use application programming interfaces (APIs) to control specific instruments of the test station; parsing the test program into parsed data, using a processor and the regular expressions in the selected one of the programming language configuration files and the regular expressions in the selected one of the test station configuration files; and interpreting, using a processor, the parsed data to enable generation of the flowchart.
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1. A method for generating a flowchart for a test program, comprising: selecting, using a processor, one of a plurality of programming language configuration files stored in at least one memory component, based on a programming language of the test program, each of the programming language configuration files being specific to one programming language and including regular expressions to identify lines of program code of the test program using keywords and constructs of the programming language; selecting, using a processor, one of a plurality of test station configuration files stored in at least one memory component, based on a test station for which the test program is written, each of the test station configuration files being specific to one test station and including regular expressions to identify lines of code of the test program that use application programming interfaces (APIs) to control specific instruments of the test station; parsing the test program into parsed data, using a processor and the regular expressions in the selected one of the programming language configuration files and the regular expressions in the selected one of the test station configuration files; and interpreting, using a processor, the parsed data to enable generation of the flowchart. 17. The method of claim 1 , wherein the parsing of the test program into parsed data is performed by parsing at least one whole line of code of the test program at the same time or parsing multiple lines of code of the test program at the same time.
| 0.762405 |
8,250,048 | 12 | 14 |
12. A computing system that processes queries on graph data, comprising: a memory; a security policy logic that stores data access constraints as match pattern and apply pattern pairs in the memory, where each match pattern specifies a selection criteria that identifies the resources that are subject to a security policy, and where associated apply pattern specifies one or more security conditions, in the form of graph patterns to be included with any query that satisfies the match pattern criteria; an access control enforcement logic that expresses query selection criteria in a tree representation of an abstract syntactic structure using metadata, analyzes the tree representation to collect properties and any terms or variables in subject or object positions with respect to the collected properties; determines a match pattern corresponding to at least one of the collected properties, terms, or variables in the query selection criteria; and rewrites the query to include, as security conditions, the graph patterns from the apply pattern that is paired with the determined match pattern; and a query processor for executing the rewritten query on graph data such that data instances returned by the rewritten query satisfy the data access constraints specified by the match pattern and apply pattern.
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12. A computing system that processes queries on graph data, comprising: a memory; a security policy logic that stores data access constraints as match pattern and apply pattern pairs in the memory, where each match pattern specifies a selection criteria that identifies the resources that are subject to a security policy, and where associated apply pattern specifies one or more security conditions, in the form of graph patterns to be included with any query that satisfies the match pattern criteria; an access control enforcement logic that expresses query selection criteria in a tree representation of an abstract syntactic structure using metadata, analyzes the tree representation to collect properties and any terms or variables in subject or object positions with respect to the collected properties; determines a match pattern corresponding to at least one of the collected properties, terms, or variables in the query selection criteria; and rewrites the query to include, as security conditions, the graph patterns from the apply pattern that is paired with the determined match pattern; and a query processor for executing the rewritten query on graph data such that data instances returned by the rewritten query satisfy the data access constraints specified by the match pattern and apply pattern. 14. The computing system of claim 12 comprising a context generation logic that retrieves context information regarding a query processing session and further where the access control enforcement logic inserts context information in the apply pattern when rewriting the query.
| 0.5 |
8,799,276 | 11 | 20 |
11. One or more non-transitory machine-readable media storing instructions that are executable by one or more processing devices to perform operations comprising: receiving an input query for a subject having a web site; determining that the input query is a navigational query, the navigational query comprising a query to locate the web site of the subject; and in response to determining that the input query is a navigational query: identifying a first page on a social network, the first page being a page specific to the subject within the social network; obtaining content from the first page; obtaining search results corresponding to the input query; identifying a second page for the subject from among the search results, the second page comprising a page of the web site and being represented in the search results by a snippet of content associated with the second page; combining the content from the first page with the snippet to thereby produce combined content; and outputting data corresponding to the combined content to display the combined content in search results.
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11. One or more non-transitory machine-readable media storing instructions that are executable by one or more processing devices to perform operations comprising: receiving an input query for a subject having a web site; determining that the input query is a navigational query, the navigational query comprising a query to locate the web site of the subject; and in response to determining that the input query is a navigational query: identifying a first page on a social network, the first page being a page specific to the subject within the social network; obtaining content from the first page; obtaining search results corresponding to the input query; identifying a second page for the subject from among the search results, the second page comprising a page of the web site and being represented in the search results by a snippet of content associated with the second page; combining the content from the first page with the snippet to thereby produce combined content; and outputting data corresponding to the combined content to display the combined content in search results. 20. The one or more non-transitory machine-readable media of claim 11 , wherein obtaining content from the first page comprises obtaining the content based on popularity of the content.
| 0.787356 |
8,086,039 | 6 | 7 |
6. The method according to claim 1 wherein the forming of the fine-grain fingerprint includes forming the fine-grain fingerprints to be a word size.
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6. The method according to claim 1 wherein the forming of the fine-grain fingerprint includes forming the fine-grain fingerprints to be a word size. 7. The method according to claim 6 wherein the forming of the word size fingerprints includes: calculating a center location of all candidate keypoints in a word or local neighborhood; defining the center location as the fingerprint location; sorting the candidate keypoints in an increasing clockwise orientation order, both for orientation and distance; computing the fine-grain fingerprint by measuring distances of the keypoints to the center location; and determining an integer for each keypoint by quantizing the distance from the keypoint to the center location using a set of predefined quantization thresholds, the quantization threshold values being determined empirically by studying the distribution of keypoints.
| 0.5 |
8,041,119 | 11 | 12 |
11. The method of claim 1 , wherein step (e) includes the steps of: computing an average and a standard deviation by computing the average and the standard deviation of those vertical projections being greater than 0, respectively; computing a second threshold being the average subtracted by a second threshold coefficient multiplying the standard deviation; selecting at least one trough coordinate, the vertical projection of which is smaller than the second threshold; and selecting the third coordinate from the trough coordinates.
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11. The method of claim 1 , wherein step (e) includes the steps of: computing an average and a standard deviation by computing the average and the standard deviation of those vertical projections being greater than 0, respectively; computing a second threshold being the average subtracted by a second threshold coefficient multiplying the standard deviation; selecting at least one trough coordinate, the vertical projection of which is smaller than the second threshold; and selecting the third coordinate from the trough coordinates. 12. The method of claim 11 further comprising the step of counting the number of the trough coordinates.
| 0.5 |
7,805,673 | 9 | 13 |
9. The method of claim 1 , wherein the redaction has a content associated with it, the content specifying what was redacted.
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9. The method of claim 1 , wherein the redaction has a content associated with it, the content specifying what was redacted. 13. The method of claim 9 , wherein the content comprises, for a motion document, a starting and ending time stamp of an interval to be redacted.
| 0.549689 |
9,336,186 | 8 | 9 |
8. The method of claim 7 , wherein generating the transform of the given sentence includes: parsing the given sentence with a dependency parser to generate a source dependency tree having a plurality of part of speech labels connecting the sentence terms; and transforming the source dependency tree to create the transform of the given sentence.
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8. The method of claim 7 , wherein generating the transform of the given sentence includes: parsing the given sentence with a dependency parser to generate a source dependency tree having a plurality of part of speech labels connecting the sentence terms; and transforming the source dependency tree to create the transform of the given sentence. 9. The method of claim 8 , wherein transforming the source dependency tree to create the transform of the given sentence includes: collapsing any auxiliary, determiner, preposition, negation, or possessive of the sentence terms with its head; replacing any preposition part of speech label with its argument; and connecting a dummy root node to every inflected verb.
| 0.5 |
7,496,854 | 57 | 59 |
57. A method for information handling within a document operated on by a first application program, the document containing first information that can be utilized in a second application program the method comprising the steps of: identifying without user intervention or designation the first information; and responding to a user selection by performing an operation related to a second information, the second information associated with the first information from the second application program.
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57. A method for information handling within a document operated on by a first application program, the document containing first information that can be utilized in a second application program the method comprising the steps of: identifying without user intervention or designation the first information; and responding to a user selection by performing an operation related to a second information, the second information associated with the first information from the second application program. 59. The method of claim 57 , wherein the first information is a name, and the operation performed is selected from a group consisting of generating an electronic mail, a telex, a facsimile or a letter addressed to the name indicated by the first information.
| 0.5 |
10,108,604 | 12 | 13 |
12. A machine for automatically extracting a conceptual graph, comprising: a microprocessor or processor coupled to a memory, wherein the microprocessor or processor is programmed to extract a conceptual graph from a text by: first determining a set of pedagogically significant key terms, wherein the key terms are derived from text that does not include the text from which the conceptual graphs are generated; subsequently obtaining a set of pre-determined semantic relations between the pedagogically significant key terms based on a particular subject matter area or domain, and according to a prescribed set of edge relations; subsequently obtaining parse output by semantically parsing one or more text files in computer-readable form related to the subject matter area or domain; and after semantically parsing the one or more text files, deriving semantic relations between terms in the parse output according to the set of pre-determined semantic relations between the pedagogically significant key terms based on the subject matter area or domain, and according to the prescribed set of edge relations; further wherein the microprocessor processor is programmed to create one or more questions in an automated tutor system from said conceptual graph.
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12. A machine for automatically extracting a conceptual graph, comprising: a microprocessor or processor coupled to a memory, wherein the microprocessor or processor is programmed to extract a conceptual graph from a text by: first determining a set of pedagogically significant key terms, wherein the key terms are derived from text that does not include the text from which the conceptual graphs are generated; subsequently obtaining a set of pre-determined semantic relations between the pedagogically significant key terms based on a particular subject matter area or domain, and according to a prescribed set of edge relations; subsequently obtaining parse output by semantically parsing one or more text files in computer-readable form related to the subject matter area or domain; and after semantically parsing the one or more text files, deriving semantic relations between terms in the parse output according to the set of pre-determined semantic relations between the pedagogically significant key terms based on the subject matter area or domain, and according to the prescribed set of edge relations; further wherein the microprocessor processor is programmed to create one or more questions in an automated tutor system from said conceptual graph. 13. The machine of claim 12 , further wherein a maximal start node and a maximal end node are extracted from the text file or files.
| 0.702703 |
9,377,864 | 14 | 15 |
14. A method executed on a computing device for transforming visualized data through a visual analytics engine based on interactivity, the method comprising: displaying a visualization; detecting a gesture associated with a portion of the visualization; determining a contextual information associated with the gesture and a portion of the visualization based on prior visualizations, user attributes, a gesture history, and a visualization history; evaluating the contextual information to determine an action including one of: a combination, a split, an expansion, a reduction action; in response to a failure to identify the action, performing a search on a history of prior actions associated with the contextual information; determining a suggested action based on the history of the prior actions; presenting the suggested action as a graphical representation of a potential new visualization; and detecting a selection of the graphical representation of the potential new visualization to execute the suggested action and generate the new visualization; executing, the action based on the contextual information; determining attributes of a new visualization based on the contextual information, wherein the attributes include a dimensionality, a style, a format, and a type; integrating the attributes of the new visualization in the action; executing the action to generate the new visualization; selecting a default screen as a screen in focus to render the new visualization in an environment including a plurality of screens; and rendering the new visualization in the default screen to present the new visualization.
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14. A method executed on a computing device for transforming visualized data through a visual analytics engine based on interactivity, the method comprising: displaying a visualization; detecting a gesture associated with a portion of the visualization; determining a contextual information associated with the gesture and a portion of the visualization based on prior visualizations, user attributes, a gesture history, and a visualization history; evaluating the contextual information to determine an action including one of: a combination, a split, an expansion, a reduction action; in response to a failure to identify the action, performing a search on a history of prior actions associated with the contextual information; determining a suggested action based on the history of the prior actions; presenting the suggested action as a graphical representation of a potential new visualization; and detecting a selection of the graphical representation of the potential new visualization to execute the suggested action and generate the new visualization; executing, the action based on the contextual information; determining attributes of a new visualization based on the contextual information, wherein the attributes include a dimensionality, a style, a format, and a type; integrating the attributes of the new visualization in the action; executing the action to generate the new visualization; selecting a default screen as a screen in focus to render the new visualization in an environment including a plurality of screens; and rendering the new visualization in the default screen to present the new visualization. 15. The method of claim 14 , further comprising: displaying elements on the visualization.
| 0.637097 |
9,195,442 | 1 | 6 |
1. A method for supporting context-dependent expression compilation in a programming language environment operating on one or more microprocessors, comprising: providing a context object that operates to compile an expression in at least one of a plurality of programming contexts encapsulated by the context object; deriving, via the context object, a target type associated with the expression, wherein the target type associated with the expression is dependent on the at least one of a plurality of programming contexts; deriving a function descriptor for the target type; and performing, via the context object, a compatibility check for the expression in the programming language environment including checking whether any type associated with the expression is consistent with the function descriptor for the target type.
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1. A method for supporting context-dependent expression compilation in a programming language environment operating on one or more microprocessors, comprising: providing a context object that operates to compile an expression in at least one of a plurality of programming contexts encapsulated by the context object; deriving, via the context object, a target type associated with the expression, wherein the target type associated with the expression is dependent on the at least one of a plurality of programming contexts; deriving a function descriptor for the target type; and performing, via the context object, a compatibility check for the expression in the programming language environment including checking whether any type associated with the expression is consistent with the function descriptor for the target type. 6. The method according to claim 1 , further comprising: providing another context object that operates to compile the expression in another programming context.
| 0.611111 |
5,500,919 | 1 | 2 |
1. A text-to-speech controller for controllably feeding a text file from a text buffer to a text-to-speech converter, the text file being comprised by text characters organized into words, including: an interface for inputting user commands which indicate how the text characters in the text file are fed from the text buffer to the text-to-speech converter; and a controller for effectuating the user commands at interword text boundaries such that the text characters are fed at interword text boundaries from the text buffer to the text-to-speech converter in accordance with the user commands.
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1. A text-to-speech controller for controllably feeding a text file from a text buffer to a text-to-speech converter, the text file being comprised by text characters organized into words, including: an interface for inputting user commands which indicate how the text characters in the text file are fed from the text buffer to the text-to-speech converter; and a controller for effectuating the user commands at interword text boundaries such that the text characters are fed at interword text boundaries from the text buffer to the text-to-speech converter in accordance with the user commands. 2. A controller according to claim 1, wherein said interface is comprised by a graphical user interface for inputting the user commands to alter how text is fed from the text buffer to the text-to-speech converter.
| 0.5 |
9,761,032 | 1 | 5 |
1. An apparatus for rendering an avatar, comprising: one or more processors; and an avatar animation engine, to be operated by the one or more processors, to receive a plurality of facial motion parameters and a plurality of head gestures parameters, respectively associated with a face and a head of a user, and drive an avatar model with facial and skeleton animations to animate an avatar, employing at least a head bone and a torso bone connected at a joint and using the facial motion parameters and the head gestures parameters, to replicate a facial expression of the user on the avatar, that includes impact of head post rotation of the user; wherein the plurality of facial motion parameters depict facial action movements of the face, and the plurality of head gestures parameters depict head pose gestures of the head; wherein the avatar animation engine is to apply head rotation impact weights when driving the avatar model with facial and skeleton animations; and wherein the avatar animation engine is to apply head rotation impact weights from a head rotation impact weight map pre-generated employing the at least a head bone and a torso bone connected at a joint.
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1. An apparatus for rendering an avatar, comprising: one or more processors; and an avatar animation engine, to be operated by the one or more processors, to receive a plurality of facial motion parameters and a plurality of head gestures parameters, respectively associated with a face and a head of a user, and drive an avatar model with facial and skeleton animations to animate an avatar, employing at least a head bone and a torso bone connected at a joint and using the facial motion parameters and the head gestures parameters, to replicate a facial expression of the user on the avatar, that includes impact of head post rotation of the user; wherein the plurality of facial motion parameters depict facial action movements of the face, and the plurality of head gestures parameters depict head pose gestures of the head; wherein the avatar animation engine is to apply head rotation impact weights when driving the avatar model with facial and skeleton animations; and wherein the avatar animation engine is to apply head rotation impact weights from a head rotation impact weight map pre-generated employing the at least a head bone and a torso bone connected at a joint. 5. The apparatus of claim 1 , wherein the avatar animation engine is to further employ a 2-dimensional texture map, and the head rotation impact weight map has layout or dimensions that correspond to the 2-dimensional texture map; and wherein the animation engine is to retrieve an impact weight for a vertex from the head rotation impact weight map, using corresponding coordinates of the vertex in the 2-dimensional texture map.
| 0.693295 |
10,037,533 | 7 | 9 |
7. A computer implemented method comprising: retrieving, via a computer system, a list of known fraudulent merchants; retrieving, via the computer system, a plurality of historic cryptocurrency transactions; generating, via the computer system, an electronic fingerprint for each fraudulent merchant of the known fraudulent merchants, based on one or more related transactions associated with the fraudulent merchant in the plurality of historic cryptocurrency transactions; identifying, via the computer system, relationships between the known fraudulent merchants based on electronic fingerprints associated with the known fraudulent merchants; generating, via the computer system, data representing a first graph based on the identified relationships; receiving, via the computer system, a set of investigation information about a first merchant, the investigation information comprising an identification of the first merchant and one or more proposed cryptocurrency transactions associated with the first merchant; generating, via the computer system, a first merchant electronic fingerprint for the first merchant based on the set of investigation information; performing via a query engine and based on the first merchant electronic fingerprint, a first query against the data representing the first graph to identify a first plurality of relationships of the first merchant to one or more of the known fraudulent merchants; determining, via the computer system, that a first count of the identified first plurality of relationships of the first merchant to one or more of the known fraudulent merchants is above a first threshold; performing, using the query engine, a second query against the data representing the first graph to identify a second plurality of relationships of the first merchant to one or more of the known fraudulent merchants in the data representing the first graph; and rejecting via the computerized merchant fraud screening system, the one or more proposed cryptocurrency transactions in response to determining that a second count of the identified second plurality of relationships of the first merchant is above a second threshold.
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7. A computer implemented method comprising: retrieving, via a computer system, a list of known fraudulent merchants; retrieving, via the computer system, a plurality of historic cryptocurrency transactions; generating, via the computer system, an electronic fingerprint for each fraudulent merchant of the known fraudulent merchants, based on one or more related transactions associated with the fraudulent merchant in the plurality of historic cryptocurrency transactions; identifying, via the computer system, relationships between the known fraudulent merchants based on electronic fingerprints associated with the known fraudulent merchants; generating, via the computer system, data representing a first graph based on the identified relationships; receiving, via the computer system, a set of investigation information about a first merchant, the investigation information comprising an identification of the first merchant and one or more proposed cryptocurrency transactions associated with the first merchant; generating, via the computer system, a first merchant electronic fingerprint for the first merchant based on the set of investigation information; performing via a query engine and based on the first merchant electronic fingerprint, a first query against the data representing the first graph to identify a first plurality of relationships of the first merchant to one or more of the known fraudulent merchants; determining, via the computer system, that a first count of the identified first plurality of relationships of the first merchant to one or more of the known fraudulent merchants is above a first threshold; performing, using the query engine, a second query against the data representing the first graph to identify a second plurality of relationships of the first merchant to one or more of the known fraudulent merchants in the data representing the first graph; and rejecting via the computerized merchant fraud screening system, the one or more proposed cryptocurrency transactions in response to determining that a second count of the identified second plurality of relationships of the first merchant is above a second threshold. 9. The computer-implemented method of claim 7 , wherein in response to the determination that the first count of the identified first plurality of relationships of the first merchant to one or more of the known fraudulent merchants is above the first threshold, the instructions are further configured to: pull, from a historic cryptocurrency transaction database, additional information related to the set of investigation information, wherein the additional information includes one or more historical cryptocurrency transactions associated with the first merchant.
| 0.523529 |
9,262,722 | 19 | 21 |
19. A system comprising of: (a) a computer processor module; (b) a computer-readable storage module maintaining at least one registration data file for receiving registration of at least one or more social networking websites for a social networker, for measuring the social networker's collective influence on the registered at least one or more social networking websites, said computer-readable storage medium being in communication with said computer processor; (c) the social networking application module configured for automatically collecting empirical data regarding the social networker's use and activity level for the at least one or more registered social networking websites that includes, but is not limited to, measuring frequency of an activity posting by the social networker to the at least one or more registered social networking websites within a predetermined period; determining number of responses to the activity posting; measuring the length, quantitative and qualitative discussion emanating from the social networker's activity posting; measuring one or more third party's use of the social networker's activity posting; assigning individual weighted scores for each quantitative and qualitative empirical data; generating an impact score by tabulating an aggregate of the individual weighted scores derived from the plurality of weighted scores; and posting the impact score on a social networking website viewable by the social networker and a community of friends.
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19. A system comprising of: (a) a computer processor module; (b) a computer-readable storage module maintaining at least one registration data file for receiving registration of at least one or more social networking websites for a social networker, for measuring the social networker's collective influence on the registered at least one or more social networking websites, said computer-readable storage medium being in communication with said computer processor; (c) the social networking application module configured for automatically collecting empirical data regarding the social networker's use and activity level for the at least one or more registered social networking websites that includes, but is not limited to, measuring frequency of an activity posting by the social networker to the at least one or more registered social networking websites within a predetermined period; determining number of responses to the activity posting; measuring the length, quantitative and qualitative discussion emanating from the social networker's activity posting; measuring one or more third party's use of the social networker's activity posting; assigning individual weighted scores for each quantitative and qualitative empirical data; generating an impact score by tabulating an aggregate of the individual weighted scores derived from the plurality of weighted scores; and posting the impact score on a social networking website viewable by the social networker and a community of friends. 21. The system according to claim 19 , wherein the social networking application module is further configured for assigning a weighted frequency score for the measured frequency of activity postings.
| 0.644643 |
7,840,586 | 1 | 7 |
1. A method, comprising: selecting an item from a plurality of items each having associated metadata attributes; displaying a first value of a first metadata attribute of the selected item; defining a first modifier that modifies the first value of the first metadata attribute; searching the plurality of items in accordance with the first value and the first modifier; and displaying a result of the searching.
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1. A method, comprising: selecting an item from a plurality of items each having associated metadata attributes; displaying a first value of a first metadata attribute of the selected item; defining a first modifier that modifies the first value of the first metadata attribute; searching the plurality of items in accordance with the first value and the first modifier; and displaying a result of the searching. 7. The method of claim 1 , wherein the first value includes text.
| 0.917929 |
3,947,825 | 15 | 17 |
15. Apparatus for generating at least one abstract that is useful during searching and retrieval procedures involving information stored on a record medium comprising: 1. means for sensing information comprising individual words in a selected language stored on said record medium, each word comprising one or more individual characters; 2. means for categorizing selected ones only of the characters in said words into predefined character groups that are based on a probability distribution of characters in the language selected; 3. means maintaining a count of the number of characters categorized groups; and 4. means for storing said count on said record medium as an abstract of said information.
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15. Apparatus for generating at least one abstract that is useful during searching and retrieval procedures involving information stored on a record medium comprising: 1. means for sensing information comprising individual words in a selected language stored on said record medium, each word comprising one or more individual characters; 2. means for categorizing selected ones only of the characters in said words into predefined character groups that are based on a probability distribution of characters in the language selected; 3. means maintaining a count of the number of characters categorized groups; and 4. means for storing said count on said record medium as an abstract of said information. 17. The apparatus of claim 15 wherein said count maintaining means has count capacity of sufficient size for counting an average number of characters in each character group, and further comprising: means for recognizing when the character count for any predefined character group exceeds said limited count capacity; and means controlled by said recognizing means for generating and recording a force search indication rather than an abstract in order to signify that a search of the related information is required.
| 0.678483 |
9,158,847 | 1 | 8 |
1. A non-transitory computer readable medium including therein a data structure, which is a Cognitive Signature, comprising: a field to identify a contraction level of a plurality of contraction levels of a network; a field entry for a Globally Unique Identity Designation (GUID); a field T of an ordered list of first vectors, each first vector corresponding to the contraction level of the network; a field G of a list of second vectors, each second vector corresponding to the contraction level of the network; a field F to contain a Bloom Filter as a binary vector comprised of values of each of the first vectors in field T and the second vectors in field G, the binary vector being computed based on a first threshold vector corresponding to field T and a second threshold vector corresponding to field G; a field to contain a set of symbols S that label the network; a field for a Discrete Unlabeled Network Representation Code (DUNRC); a field for a Discrete Colored Network Representation Code (DCNRC); a field for contraction tree operator expressions to identify whether the network was contracted by a contraction rule; and a field for a pointer to a next Cognitive Signature at an incremented level of contraction.
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1. A non-transitory computer readable medium including therein a data structure, which is a Cognitive Signature, comprising: a field to identify a contraction level of a plurality of contraction levels of a network; a field entry for a Globally Unique Identity Designation (GUID); a field T of an ordered list of first vectors, each first vector corresponding to the contraction level of the network; a field G of a list of second vectors, each second vector corresponding to the contraction level of the network; a field F to contain a Bloom Filter as a binary vector comprised of values of each of the first vectors in field T and the second vectors in field G, the binary vector being computed based on a first threshold vector corresponding to field T and a second threshold vector corresponding to field G; a field to contain a set of symbols S that label the network; a field for a Discrete Unlabeled Network Representation Code (DUNRC); a field for a Discrete Colored Network Representation Code (DCNRC); a field for contraction tree operator expressions to identify whether the network was contracted by a contraction rule; and a field for a pointer to a next Cognitive Signature at an incremented level of contraction. 8. The non-transitory computer readable medium of claim 1 , wherein the DUNRC is computed based on a plurality of codes obtained from an upper triangular portion of a connectivity matrix of the network.
| 0.555066 |
9,916,386 | 11 | 13 |
11. An apparatus for presenting a search result in response to a current search term, comprising: a hardware processor; and a memory having one or more programs stored thereon for instructing said hardware processor to: determine a pre-established first model corresponding to preselected user information and including historical user search data; identify a selected historical search term in the historical user search data that corresponds with the current search term; identify a selected historical selection result in the historical user search data that corresponds with the identified historical search term; determine the search result based upon the identified historical selection result, wherein determining the search result includes determining an online recommendation result based upon the identified historical selection result; process the online recommendation result to generate a generated recommendation result; and present the generated recommendation result, wherein said hardware processor is configured to process the recommendation result via at least one process selected from a process group including: calculating a literal association degree between the online recommendation result and the current search term according to a logistic regression process such that the online recommendation result has the literal association degree that is greater than a preselected third threshold value; calculating a semantic association degree between the online recommendation result and the current search term according to a gradient boost decision tree such that the online recommendation result has the semantic association degree higher than a preselected fourth threshold value; or a combination thereof.
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11. An apparatus for presenting a search result in response to a current search term, comprising: a hardware processor; and a memory having one or more programs stored thereon for instructing said hardware processor to: determine a pre-established first model corresponding to preselected user information and including historical user search data; identify a selected historical search term in the historical user search data that corresponds with the current search term; identify a selected historical selection result in the historical user search data that corresponds with the identified historical search term; determine the search result based upon the identified historical selection result, wherein determining the search result includes determining an online recommendation result based upon the identified historical selection result; process the online recommendation result to generate a generated recommendation result; and present the generated recommendation result, wherein said hardware processor is configured to process the recommendation result via at least one process selected from a process group including: calculating a literal association degree between the online recommendation result and the current search term according to a logistic regression process such that the online recommendation result has the literal association degree that is greater than a preselected third threshold value; calculating a semantic association degree between the online recommendation result and the current search term according to a gradient boost decision tree such that the online recommendation result has the semantic association degree higher than a preselected fourth threshold value; or a combination thereof. 13. The apparatus of claim 11 , wherein the one or more programs instruct said hardware processor to: receive the current search term; and present the search result, wherein the historical user search data includes a plurality of historical search terms and a plurality of historical selection results that corresponds with the plurality of the historical search terms, and wherein said hardware processor is adapted to determine the preselected user information for the pre-established first model.
| 0.592985 |
7,912,715 | 1 | 3 |
1. A method comprising: comparing by a processor a first feature vector in a sequence of feature vectors formed from a digitized incoming signal to be recognized, with a first number of templates from a set of templates representing candidate patterns, based on said comparison, selecting by a processor in response to a control signal, a second number of templates from said template set, the second number being smaller than the first number, comparing by a processor a second feature vector only with said selected templates, and generating by a processor a signal corresponding to a recognized pattern of said digitized incoming signal as a result of said comparing said second feature vector only with said selected templates.
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1. A method comprising: comparing by a processor a first feature vector in a sequence of feature vectors formed from a digitized incoming signal to be recognized, with a first number of templates from a set of templates representing candidate patterns, based on said comparison, selecting by a processor in response to a control signal, a second number of templates from said template set, the second number being smaller than the first number, comparing by a processor a second feature vector only with said selected templates, and generating by a processor a signal corresponding to a recognized pattern of said digitized incoming signal as a result of said comparing said second feature vector only with said selected templates. 3. A method according to claim 1 , wherein said selected templates include the templates resulting in the lowest distortion measures when compared to said first feature vector.
| 0.649402 |
8,499,032 | 5 | 6 |
5. The method of claim 3 , wherein the compiling, by the computing device, a set of domain names further comprises determining, by the computing device, whether each domain name in the set of domain names is owned by the seed term owner.
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5. The method of claim 3 , wherein the compiling, by the computing device, a set of domain names further comprises determining, by the computing device, whether each domain name in the set of domain names is owned by the seed term owner. 6. The method of claim 5 , wherein the determining, by the computing device, whether each domain name is owned by the seed term owner further comprises notifying the seed term owner to recover a particular domain name of the set of domain names when the particular domain name is not owned by the seed term owner.
| 0.5 |
8,214,310 | 1 | 5 |
1. A method of cross descriptor learning using unlabeled exemplars, said method comprising of the steps of: a) extracting descriptors for each of a plurality of unlabeled exemplars, each extracted descriptor being a representation of a corresponding one of said plurality of unlabeled exemplars; b) automatically generating labels from said each extracted descriptor, said each extracted descriptor being used to automatically generate labels for other ones of said descriptors; c) developing a predictor for said each extracted descriptor from generated said labels; and d) combining predictions across predictors, labels being generated from combined said predictions for each of said unlabeled exemplars.
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1. A method of cross descriptor learning using unlabeled exemplars, said method comprising of the steps of: a) extracting descriptors for each of a plurality of unlabeled exemplars, each extracted descriptor being a representation of a corresponding one of said plurality of unlabeled exemplars; b) automatically generating labels from said each extracted descriptor, said each extracted descriptor being used to automatically generate labels for other ones of said descriptors; c) developing a predictor for said each extracted descriptor from generated said labels; and d) combining predictions across predictors, labels being generated from combined said predictions for each of said unlabeled exemplars. 5. A method as in claim 1 , wherein predictions are automatically combined in the step (d) of combining predictions.
| 0.857143 |
10,055,599 | 1 | 7 |
1. A processing method performed by a system comprising a first processor unit in a first terminal, a second processor unit in a remote authentication server, and a third processor unit in a mobile telephone, to enable a user to access sensitive text data from a secure electronic document, the method comprising: a) the first processor unit obtaining the secure electronic document; b) the first processor unit triggering a display of the secure electronic document on the first terminal; c) the user selecting at least one marker that is contained in said secure electronic document and that is viewable by the user on a display of the first terminal, wherein: each marker of the at least one marker comprises a first identifier and a machine-readable code, and the at least one marker is selected by said third processor unit under user control when the at least one marker displayed on the first terminal is viewed by a camera device in the mobile telephone controlled by the third processor unit; c′) sending, by the third processor unit, the at least one selected marker to the second processor unit; d) on the basis of said at least one received marker, determining, by said second processor unit, secure data from which it is possible to recover at least one item of sensitive text data, wherein said secure data is recovered from outside said secure electronic document; f) the second processor unit determining said at least one item of sensitive text data for viewing from said secure data and transmitting determined data to the third processor unit, wherein the second processor unit transmitting the determined data to the third processor unit comprises: the first processor unit searching in a vicinity of the first terminal using wireless communication means; the first processor unit receiving a second identifier of the mobile telephone when it is in a detection field of the wireless communication means; and the first processor unit transmitting the second identifier to the remote authentication server, wherein the second processor unit identifies the mobile telephone to which the determined data is to be transmitted on the basis of the second identifier; g) the third processor unit obtaining said at least one item of sensitive text data; and h) the third processor unit triggering a display of said at least one item of sensitive text data.
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1. A processing method performed by a system comprising a first processor unit in a first terminal, a second processor unit in a remote authentication server, and a third processor unit in a mobile telephone, to enable a user to access sensitive text data from a secure electronic document, the method comprising: a) the first processor unit obtaining the secure electronic document; b) the first processor unit triggering a display of the secure electronic document on the first terminal; c) the user selecting at least one marker that is contained in said secure electronic document and that is viewable by the user on a display of the first terminal, wherein: each marker of the at least one marker comprises a first identifier and a machine-readable code, and the at least one marker is selected by said third processor unit under user control when the at least one marker displayed on the first terminal is viewed by a camera device in the mobile telephone controlled by the third processor unit; c′) sending, by the third processor unit, the at least one selected marker to the second processor unit; d) on the basis of said at least one received marker, determining, by said second processor unit, secure data from which it is possible to recover at least one item of sensitive text data, wherein said secure data is recovered from outside said secure electronic document; f) the second processor unit determining said at least one item of sensitive text data for viewing from said secure data and transmitting determined data to the third processor unit, wherein the second processor unit transmitting the determined data to the third processor unit comprises: the first processor unit searching in a vicinity of the first terminal using wireless communication means; the first processor unit receiving a second identifier of the mobile telephone when it is in a detection field of the wireless communication means; and the first processor unit transmitting the second identifier to the remote authentication server, wherein the second processor unit identifies the mobile telephone to which the determined data is to be transmitted on the basis of the second identifier; g) the third processor unit obtaining said at least one item of sensitive text data; and h) the third processor unit triggering a display of said at least one item of sensitive text data. 7. A method according to claim 1 , wherein the selection step c) is performed by means of a pointer that can be seen in the display of step b) on the first terminal, and that can be controlled by the user by means of an interface of the first terminal, said pointer being configured to change its visual appearance during said display when it enters a region of the secure electronic document that corresponds to said at least one marker.
| 0.676514 |
8,346,548 | 29 | 31 |
29. A computer readable medium encoded with a computer program as claimed in claim 23 , wherein the ranking step comprises identifying aligned phonemes which differ; allocating predetermined phoneme scores for each pair of differing aligned phonemes and summing the individual phoneme scores.
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29. A computer readable medium encoded with a computer program as claimed in claim 23 , wherein the ranking step comprises identifying aligned phonemes which differ; allocating predetermined phoneme scores for each pair of differing aligned phonemes and summing the individual phoneme scores. 31. A computer readable medium encoded with a computer program as claimed in claim 29 , wherein the phoneme scores are weighted such that phoneme scores arising from partial text predetermined as less relevant in the input text are lower than equivalent phoneme scores arising from other partial text in the input text.
| 0.5 |
9,584,696 | 7 | 9 |
7. Image processing circuitry for modifying bits of an input pixel data word, comprising: an embedded data engine configured to output at least one bit of embedded data; decatenation circuitry that is coupled to the embedded data engine and that is configured to separate the input pixel data word into first and second subsets of bits; arithmetic circuitry that modifies bits of the first subset of bits based on whether the at least one bit of embedded data is the same as at least one corresponding bit of the second subset of bits; and concatenation circuitry that is coupled to the decatenation circuitry and that is configured to produce an output data word including the at least one bit of embedded data.
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7. Image processing circuitry for modifying bits of an input pixel data word, comprising: an embedded data engine configured to output at least one bit of embedded data; decatenation circuitry that is coupled to the embedded data engine and that is configured to separate the input pixel data word into first and second subsets of bits; arithmetic circuitry that modifies bits of the first subset of bits based on whether the at least one bit of embedded data is the same as at least one corresponding bit of the second subset of bits; and concatenation circuitry that is coupled to the decatenation circuitry and that is configured to produce an output data word including the at least one bit of embedded data. 9. The image processing circuitry defined in claim 7 , further comprising: multiplexing circuitry coupled between the embedded data engine and the decatenation circuitry; and buffer circuitry coupled between the multiplexing circuitry and the decatenation circuitry.
| 0.713362 |
9,230,241 | 13 | 14 |
13. The non-transitory computer-readable medium of claim 12 , wherein selecting the subset of the one or more other client computing devices to invite to participate in the communication session comprises: determining which of the one or more other client computing devices are associated with the digital content item; and selecting the one or more other client computing devices are associated with the digital content item.
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13. The non-transitory computer-readable medium of claim 12 , wherein selecting the subset of the one or more other client computing devices to invite to participate in the communication session comprises: determining which of the one or more other client computing devices are associated with the digital content item; and selecting the one or more other client computing devices are associated with the digital content item. 14. The non-transitory computer-readable medium of claim 13 , wherein determining which of the one or more other client computing devices are associated with the digital content item comprises: determining whether the one or more other client computing devices have interacted with the digital content item; determining whether one or more users associated with the one or more other client computing devices, respectively, have a proximate geographic association with the digital content item, wherein the proximate geographic association comprises a distance less than a threshold distance between a location associated with the digital content item and a location associated with the one or more other client computing devices; determining whether the one or more users associated with the one or more other client computing devices have been tagged in association with the digital content item, wherein being tagged in association with the digital content item comprises one or more pieces of metadata that identify a user; and determining whether the one or more users of the one or more other client computing devices have specified an interest in a topic associated with the digital content item.
| 0.5 |
7,739,589 | 1 | 10 |
1. A method for extending markup supported by a browser comprising: identifying a Web browser that presents information written in a markup language, wherein said Web browser comprises a markup rendering processor that is a runtime processor that renders or interprets markup documents, and wherein said Web browser comprises a syntax processor that is a Document Type Definition (DTD) processor; identifying an extender comprising an extension module and linking instructions, wherein the extension module defines a new language tag that was not previously part of a markup language supported by the Web browser; loading the extender; and extending the markup language supported by the Web browser to include the new language tag, wherein the extending step further comprises: the syntax processor using the extender to dynamically produce an operational DTD based upon the linking instructions, wherein said operational DTD is used by the Web browser in parsing a markup document that includes the new language tag; wherein the identified extender further comprises a plurality of attributes, wherein said Web browser further comprises an extension loader for loading the extender at runtime, wherein the attributes of the extender define times at which the extension loader is to load the extender, wherein specifiable times via the attributes comprise at least two of “when the Web browser loads,” “when a markup document is parsed,” when a markup document is rendered,” and when specified Document Object Model (DOM) events occur.
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1. A method for extending markup supported by a browser comprising: identifying a Web browser that presents information written in a markup language, wherein said Web browser comprises a markup rendering processor that is a runtime processor that renders or interprets markup documents, and wherein said Web browser comprises a syntax processor that is a Document Type Definition (DTD) processor; identifying an extender comprising an extension module and linking instructions, wherein the extension module defines a new language tag that was not previously part of a markup language supported by the Web browser; loading the extender; and extending the markup language supported by the Web browser to include the new language tag, wherein the extending step further comprises: the syntax processor using the extender to dynamically produce an operational DTD based upon the linking instructions, wherein said operational DTD is used by the Web browser in parsing a markup document that includes the new language tag; wherein the identified extender further comprises a plurality of attributes, wherein said Web browser further comprises an extension loader for loading the extender at runtime, wherein the attributes of the extender define times at which the extension loader is to load the extender, wherein specifiable times via the attributes comprise at least two of “when the Web browser loads,” “when a markup document is parsed,” when a markup document is rendered,” and when specified Document Object Model (DOM) events occur. 10. The method of claim 1 , further comprising: hooking at least one extension point into the Web browser in response to loading the extender; and loading the extension module including code for implementing said extension, wherein said extension point points to code of the extension module.
| 0.643032 |
9,081,978 | 5 | 14 |
5. A system, comprising: a token mapping datastore storing token mapping data that associates a plurality of strings with corresponding tokens, the token mapping datastore included in a first computing environment associated with a first trust level; a whitelist token mapping datastore storing whitelist token mapping data that associates a subset of the plurality of strings with the corresponding tokens, the whitelist token mapping datastore included in a second computing environment associated with a second trust level; a first computing device in communication with the token mapping datastore, the first computing device configured to execute a first set of computer-readable instructions that cause the first computing device to: generate tokenized information that includes one or more tokens that correspond to one or more strings of the plurality of strings; send the tokenized information to be stored in the second computing environment; and a second computing device in communication with the first computing device and the whitelist token mapping datastore, the second computing device configured to execute a second set of computer-readable instructions that cause the second computing device to: receive a search request including one or more search terms; for the one or more search terms that are included in the whitelist token mapping data, retrieve the corresponding token from the whitelist token mapping datastore; for the one or more search terms that are not included in the whitelist token mapping data, send a request that the first computing device retrieve the corresponding token from the token mapping datastore; based at least in part on one or more tokens corresponding to the one or more search terms, perform a search for the tokenized information stored in the second computing environment; identify one or more tokens in the tokenized information that are included in the whitelist token mapping data; replace the identified one or more tokens with one or more corresponding strings from the whitelist token mapping data, to generate partly detokenized information; and provide the partly detokenized information in response to the search request.
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5. A system, comprising: a token mapping datastore storing token mapping data that associates a plurality of strings with corresponding tokens, the token mapping datastore included in a first computing environment associated with a first trust level; a whitelist token mapping datastore storing whitelist token mapping data that associates a subset of the plurality of strings with the corresponding tokens, the whitelist token mapping datastore included in a second computing environment associated with a second trust level; a first computing device in communication with the token mapping datastore, the first computing device configured to execute a first set of computer-readable instructions that cause the first computing device to: generate tokenized information that includes one or more tokens that correspond to one or more strings of the plurality of strings; send the tokenized information to be stored in the second computing environment; and a second computing device in communication with the first computing device and the whitelist token mapping datastore, the second computing device configured to execute a second set of computer-readable instructions that cause the second computing device to: receive a search request including one or more search terms; for the one or more search terms that are included in the whitelist token mapping data, retrieve the corresponding token from the whitelist token mapping datastore; for the one or more search terms that are not included in the whitelist token mapping data, send a request that the first computing device retrieve the corresponding token from the token mapping datastore; based at least in part on one or more tokens corresponding to the one or more search terms, perform a search for the tokenized information stored in the second computing environment; identify one or more tokens in the tokenized information that are included in the whitelist token mapping data; replace the identified one or more tokens with one or more corresponding strings from the whitelist token mapping data, to generate partly detokenized information; and provide the partly detokenized information in response to the search request. 14. The system of claim 5 , further comprising: the second computing device in communication with the first computing device and the whitelist token mapping datastore, the second computing device configured to execute a second set of computer-readable instructions that cause the second computing device to: send, to the first computing device, a request for one or more tokens corresponding to one or more strings, the one or more strings including one or more of: one or more hostnames of one or more computing devices in the first computing environment; one or more usernames of one or more users; or one or more common portions of message strings generated by one or more processes that execute in the first computing environment; receive, from the first computing device, the one or more tokens corresponding to the one or more strings; and populate the whitelist token mapping data with the one or more strings mapped to the one or more corresponding tokens.
| 0.5 |
7,685,088 | 3 | 4 |
3. The system for creating new concepts in existing ontologies according to claim 2 , wherein the normalizer comprises: a normalized description identifier for identifying whether a received new concept description is a normalized concept description and directly outputting the concept description identified as a normalized one; and a description normalizer connected with the normalized description identifier, for parsing the concept description which is identified as an un-normalized concept description by the normalized description identifier, transforming the un-normalized concept description into a normalized one, and then outputting the transformed normalized concept description.
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3. The system for creating new concepts in existing ontologies according to claim 2 , wherein the normalizer comprises: a normalized description identifier for identifying whether a received new concept description is a normalized concept description and directly outputting the concept description identified as a normalized one; and a description normalizer connected with the normalized description identifier, for parsing the concept description which is identified as an un-normalized concept description by the normalized description identifier, transforming the un-normalized concept description into a normalized one, and then outputting the transformed normalized concept description. 4. The system for creating new concepts in existing ontologies according to claim 3 , wherein the description normalizer comprises: a concept description partitioner for segmenting the received un-normalized concept description into description parts; a concept identifier connected with the concept description partitioner for identifying, for each segmented description part, kernel concepts in it; and a concept replacer connected with said concept identifier for, if the identified kernel concepts are not in a normalized format, replacing them with their corresponding normalized ones in the existing ontologies, wherein the replacement is executed based on a domain synonym set and sentence similarity algorithm.
| 0.5 |
9,922,328 | 15 | 16 |
15. The one or more non-transitory, machine-readable media of claim 14 , wherein the documentation attributes comprise a set of attributes pertaining to sequencing requirements.
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15. The one or more non-transitory, machine-readable media of claim 14 , wherein the documentation attributes comprise a set of attributes pertaining to sequencing requirements. 16. The one or more non-transitory, machine-readable media of claim 15 , wherein the instructions further cause the one or more processing devices to: process an indication of one or more user selections of one or more sequencing options; and generate the set of objects to facilitate setup of the accounting program based in part on the indication of the one or more user selections of one or more sequencing options.
| 0.5 |
8,416,240 | 1 | 8 |
1. A method, comprising: receiving, at a server, a search query, wherein the search query comprises a request for information about an object; determining a 3D model for the object based on the search query, wherein the 3D model comprises three-dimensional shape information about the object; generating and determining after the 3D model for the object has been determined, based on a plurality of stored images of the object, at least one applicable light field and at least one applicable viewing perspective, wherein in each of the stored images of the object, the object is lit by at least one respective light field and is imaged from a respective viewing perspective, wherein the plurality of stored images of the object are a variety of photographs or video frames of the object, wherein the at least one applicable light field is determined so as to substantially match lighting conditions of at least one of the stored images of the object, wherein the at least one applicable viewing perspective is determined so as to substantially match at least one viewing perspective of at least one of the stored images of the object; and transmitting, from the server, a search query result, wherein the search query result comprises the 3D model, the at least one applicable light field, and the at least one applicable viewing perspective.
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1. A method, comprising: receiving, at a server, a search query, wherein the search query comprises a request for information about an object; determining a 3D model for the object based on the search query, wherein the 3D model comprises three-dimensional shape information about the object; generating and determining after the 3D model for the object has been determined, based on a plurality of stored images of the object, at least one applicable light field and at least one applicable viewing perspective, wherein in each of the stored images of the object, the object is lit by at least one respective light field and is imaged from a respective viewing perspective, wherein the plurality of stored images of the object are a variety of photographs or video frames of the object, wherein the at least one applicable light field is determined so as to substantially match lighting conditions of at least one of the stored images of the object, wherein the at least one applicable viewing perspective is determined so as to substantially match at least one viewing perspective of at least one of the stored images of the object; and transmitting, from the server, a search query result, wherein the search query result comprises the 3D model, the at least one applicable light field, and the at least one applicable viewing perspective. 8. The method of claim 1 , wherein the at least one applicable light field comprises a specular reflection map.
| 0.923448 |
8,818,808 | 2 | 3 |
2. The method of claim 1 , further comprising: using the model, performing automatic speech recognition of the utterance data not having a corresponding manual transcription to produce a new set of automatically transcribed utterances; training a second model using the first manually transcribed data and the second manually transcribed data and the new set of automatically transcribed utterances; selecting another predetermined number of utterances not having a corresponding manual transcription; and manually transcribing the selected another predetermined number of utterances not having a corresponding manual transcription.
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2. The method of claim 1 , further comprising: using the model, performing automatic speech recognition of the utterance data not having a corresponding manual transcription to produce a new set of automatically transcribed utterances; training a second model using the first manually transcribed data and the second manually transcribed data and the new set of automatically transcribed utterances; selecting another predetermined number of utterances not having a corresponding manual transcription; and manually transcribing the selected another predetermined number of utterances not having a corresponding manual transcription. 3. The method of claim 2 , further comprising: determining confidence scores with respect to the new set of automatically transcribed utterances, wherein: the act of selecting a predetermined number of utterances not having a corresponding manual transcription is based on the confidence scores.
| 0.516393 |
8,346,534 | 1 | 19 |
1. A method for automatically generating one or more keywords from an electronic document, the method comprising the steps of: identifying candidate entries for the keywords by extracting all n-grams up to a specified length that do not cross sentence boundaries, reducing a size of a data set consisting of the extracted n-grams by applying one or more filters and balancing a distribution of positive and negative examples whenever the data set is derived from a training data set; constructing a feature vector for each candidate entry, wherein the feature vector comprises at least one feature selected from the group consisting of one or more discourse comprehension features, one or more part-of-speech pattern features, and one or more encyclopedic annotation features using a computer; assigning a numeric score to each candidate entry based on the feature vector for that candidate entry using a computer; and selecting a specified number of candidate entries to be retained as the keywords.
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1. A method for automatically generating one or more keywords from an electronic document, the method comprising the steps of: identifying candidate entries for the keywords by extracting all n-grams up to a specified length that do not cross sentence boundaries, reducing a size of a data set consisting of the extracted n-grams by applying one or more filters and balancing a distribution of positive and negative examples whenever the data set is derived from a training data set; constructing a feature vector for each candidate entry, wherein the feature vector comprises at least one feature selected from the group consisting of one or more discourse comprehension features, one or more part-of-speech pattern features, and one or more encyclopedic annotation features using a computer; assigning a numeric score to each candidate entry based on the feature vector for that candidate entry using a computer; and selecting a specified number of candidate entries to be retained as the keywords. 19. The method as recited in claim 1 , wherein one of the one or more part-of-speech pattern features is derived by the steps of: providing a candidate phrase from a list of candidate entries; labeling each word in the candidate phrase with a tag indicating part-of-speech to form a tag pattern; using the training data set to calculate a number of distinct phrases C(pattern) having this tag pattern; using the training data set to calculate a number of phrases C(pattern, positive) having this tag pattern and also having a positive label indicating presence in a manually-constructed collection of keywords associated with the training data set; and calculating a probability of the tag pattern being positive as P(pattern)=C(pattern, positive)/C(pattern), whereby the probability may be used as the part-of-speech pattern feature.
| 0.708188 |
8,131,540 | 1 | 13 |
1. A method in a computer system for performing a relationship search of a corpus of documents, each document having at least one sentence, comprising: receiving a relationship search query that designates a desired grammatical relationship between a first entity and at least one of a second entity or an action; transforming the search query into a Boolean expression; under control of the computer system, automatically determining a set of data objects that match the Boolean expression using a keyword-style search of a data structure that indexes terms of the documents in a memory of the computer system by including, for at least some of a plurality of terms, grammatical relationship information that specifies that the corresponding term is a subject, object, or modifier of another term, and including for at least one of the plurality of terms having the included grammatical relationship information, semantic information that specifies an entity type that identifies the term as a type of person, location, or thing; when the received relationship search query designates a desired grammatical relationship between the first entity and any action, returning an indication of a plurality of matching objects in the corpus that encompass the first entity along with an indication of the corresponding action encompassed by the matching objects; and otherwise, returning an indication of a plurality of matching objects in the corpus that encompass the desired grammatical relationship.
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1. A method in a computer system for performing a relationship search of a corpus of documents, each document having at least one sentence, comprising: receiving a relationship search query that designates a desired grammatical relationship between a first entity and at least one of a second entity or an action; transforming the search query into a Boolean expression; under control of the computer system, automatically determining a set of data objects that match the Boolean expression using a keyword-style search of a data structure that indexes terms of the documents in a memory of the computer system by including, for at least some of a plurality of terms, grammatical relationship information that specifies that the corresponding term is a subject, object, or modifier of another term, and including for at least one of the plurality of terms having the included grammatical relationship information, semantic information that specifies an entity type that identifies the term as a type of person, location, or thing; when the received relationship search query designates a desired grammatical relationship between the first entity and any action, returning an indication of a plurality of matching objects in the corpus that encompass the first entity along with an indication of the corresponding action encompassed by the matching objects; and otherwise, returning an indication of a plurality of matching objects in the corpus that encompass the desired grammatical relationship. 13. The method of claim 1 wherein the relationship search query includes a designation of at least one entity type or a path specification in a classification system.
| 0.746177 |
8,312,042 | 14 | 26 |
14. A computer-implemented method comprising: determining, by one or more servers, whether a textual representation of a first spoken phrase includes one or more keywords that are associated with automatic dialing; in response to determining that the textual representation of the first spoken phrase does not include the one or more keywords, submitting, by the one or more servers, the textual representation of the first spoken phrase to a first search engine; determining, by the one or more servers, whether a textual representation of a second spoken phrase includes the one or more keywords that are associated with automatic dialing; in response to determining that the textual representation of the second spoken phrase includes the one or more keywords, submitting, by the one or more servers, the textual representation of the second spoken phrase to a second search engine, the second search engine being a geographic search engine; receiving, by the one or more servers, one or more responsive search results from the geographic search engine; selecting, by the one or more servers, contact information associated with a particular search result of the one or more responsive search results; and providing, by the one or more servers and for receipt by a mobile device, an instruction to automatically initiate communication using the selected contact information.
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14. A computer-implemented method comprising: determining, by one or more servers, whether a textual representation of a first spoken phrase includes one or more keywords that are associated with automatic dialing; in response to determining that the textual representation of the first spoken phrase does not include the one or more keywords, submitting, by the one or more servers, the textual representation of the first spoken phrase to a first search engine; determining, by the one or more servers, whether a textual representation of a second spoken phrase includes the one or more keywords that are associated with automatic dialing; in response to determining that the textual representation of the second spoken phrase includes the one or more keywords, submitting, by the one or more servers, the textual representation of the second spoken phrase to a second search engine, the second search engine being a geographic search engine; receiving, by the one or more servers, one or more responsive search results from the geographic search engine; selecting, by the one or more servers, contact information associated with a particular search result of the one or more responsive search results; and providing, by the one or more servers and for receipt by a mobile device, an instruction to automatically initiate communication using the selected contact information. 26. The method of claim 14 , wherein selecting the contact information associated with the particular search result comprises selecting one of the responsive search results that is not a highest ranked of the responsive search results.
| 0.742325 |
9,672,209 | 1 | 5 |
1. A computer program product comprising a non-transitory computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: automatically track language based user activities occurring via the computing device to at least one other computing device via a communication connection; automatically analyze the language based user activities to determine a relative priority ordering of one or more languages used by a user performing the user activities; automatically generate a sequence of preferred language translation substitutions for outputting messages based on results of the analysis, wherein the sequence comprises two or more preferred language translation substitutions for outputting the messages; automatically apply the sequence to a received message from a process associated with the computing device to generate a translated message using one of the preferred language translation substitutions in the sequence of preferred language translation substitutions, wherein the sequence is automatically applied to the received message from the process by overriding a user defined sequence of preferred translation substitutions and giving priority to the automatically sequence and wherein the computer readable program to automatically apply the sequence to the received message further causes the computing device to: traverse, in a sequence order, the preferred language translation substitutions of the sequence; for each preferred language translation substitution in the sequence order: determine whether a source of the received message has a translation catalog file corresponding to the first preferred language translation substitution in the sequence order, wherein each translation catalog file comprises a translation in an associated language; responsive to a failure of the source of the received message having the translation catalog file corresponding to the first preferred language translation substitution in the sequence order, determine whether the source of the received message has a translation catalog file corresponding to the next preferred language translation substitution in the sequence order; and responsive to the source of the received message having the translation catalog file corresponding to the next preferred language translation substitution in the sequence order, select the next preferred language translation substitution as a preferred language translation substitution to use in generating the translated message; and generate the translated message using the selected preferred language translation substitution to translate the received message into a different language from a language in which the received message is received; and output the translated message via an output device of the computing device.
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1. A computer program product comprising a non-transitory computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: automatically track language based user activities occurring via the computing device to at least one other computing device via a communication connection; automatically analyze the language based user activities to determine a relative priority ordering of one or more languages used by a user performing the user activities; automatically generate a sequence of preferred language translation substitutions for outputting messages based on results of the analysis, wherein the sequence comprises two or more preferred language translation substitutions for outputting the messages; automatically apply the sequence to a received message from a process associated with the computing device to generate a translated message using one of the preferred language translation substitutions in the sequence of preferred language translation substitutions, wherein the sequence is automatically applied to the received message from the process by overriding a user defined sequence of preferred translation substitutions and giving priority to the automatically sequence and wherein the computer readable program to automatically apply the sequence to the received message further causes the computing device to: traverse, in a sequence order, the preferred language translation substitutions of the sequence; for each preferred language translation substitution in the sequence order: determine whether a source of the received message has a translation catalog file corresponding to the first preferred language translation substitution in the sequence order, wherein each translation catalog file comprises a translation in an associated language; responsive to a failure of the source of the received message having the translation catalog file corresponding to the first preferred language translation substitution in the sequence order, determine whether the source of the received message has a translation catalog file corresponding to the next preferred language translation substitution in the sequence order; and responsive to the source of the received message having the translation catalog file corresponding to the next preferred language translation substitution in the sequence order, select the next preferred language translation substitution as a preferred language translation substitution to use in generating the translated message; and generate the translated message using the selected preferred language translation substitution to translate the received message into a different language from a language in which the received message is received; and output the translated message via an output device of the computing device. 5. The computer program product of claim 1 , wherein the computer readable program causes the computing device to generate the translated message by utilizing the translation catalog file associated with the source of the received message which matches the selected preferred language translation substitution.
| 0.799223 |
9,438,419 | 11 | 15 |
11. One or more tangible non-transitory computer-readable media having computer-executable instructions for performing a method of running a software program on a computing device, the computing device operating under an operating system, the method including issuing instructions from the software program for a computer processor to generate a probabilistic password cracking system for cracking a targeted password for a secured user account associated with a user, the instructions comprising: receiving a plurality of known password strings, said plurality of known password strings formed of at least one category selected from the group consisting of alpha strings, digits, and special characters; deriving one or more base structures from said plurality of known password strings, whereby one base structure may include more than one password string from said plurality of known password strings; detecting relevant patterns from said plurality of known password strings, wherein said relevant patterns include an A-word, an R-word, an R-pattern, an M-word, and an A-pattern; automatically assigning a set of probability values to each relevant pattern of said relevant patterns and to each base structure of said one or more base structures based on a probability value of each alpha string, each digit, or each special character in said each base structure; creating a probabilistic context free grammar based on said set of probability values assigned to said each relevant pattern to said each base structure; receiving one or more input dictionaries containing a plurality of sequences of alpha characters; generating password guess strings in decreasing estimated probability via said probabilistic context-free grammar by utilizing said plurality of sequences of alpha characters; accessing a login interface to the secured user account; and applying said password guess strings from said computer processor sequentially to said login interface, whereby authentication of the user can be achieved.
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11. One or more tangible non-transitory computer-readable media having computer-executable instructions for performing a method of running a software program on a computing device, the computing device operating under an operating system, the method including issuing instructions from the software program for a computer processor to generate a probabilistic password cracking system for cracking a targeted password for a secured user account associated with a user, the instructions comprising: receiving a plurality of known password strings, said plurality of known password strings formed of at least one category selected from the group consisting of alpha strings, digits, and special characters; deriving one or more base structures from said plurality of known password strings, whereby one base structure may include more than one password string from said plurality of known password strings; detecting relevant patterns from said plurality of known password strings, wherein said relevant patterns include an A-word, an R-word, an R-pattern, an M-word, and an A-pattern; automatically assigning a set of probability values to each relevant pattern of said relevant patterns and to each base structure of said one or more base structures based on a probability value of each alpha string, each digit, or each special character in said each base structure; creating a probabilistic context free grammar based on said set of probability values assigned to said each relevant pattern to said each base structure; receiving one or more input dictionaries containing a plurality of sequences of alpha characters; generating password guess strings in decreasing estimated probability via said probabilistic context-free grammar by utilizing said plurality of sequences of alpha characters; accessing a login interface to the secured user account; and applying said password guess strings from said computer processor sequentially to said login interface, whereby authentication of the user can be achieved. 15. One or more tangible non-transitory computer-readable media as in claim 11 , further comprising: said step of creating said probabilistic context free grammar further including deriving substructures from said alpha strings.
| 0.853093 |
8,832,132 | 20 | 28 |
20. A computer-implemented method of providing a search result set relevant to a search query, the method comprising: hosting a social network comprising a plurality of communities, each community having a plurality of users; storing a user profile for each user including a first user and a second user, the user profile identifying the user's membership in at least one of the communities; receiving the search query directed to a content index that is independent of the social network from the first user who is a member of at least one of the communities; determining personalization information including a first search phrase and a second search phrase, the first search phrase being determined from the first user's user profile and data from the second user's user profile, the second user's user profile having a degree of separation from the first user's user profile in the social network, and the second search phrase being based at least in part on the first user's membership in the at least one of the communities and a level of personalization selected by the first user for personalizing the search query received from the first user, wherein the level of personalization indicates a total number of the communities from which the second search phrase is retrieved; combining the search query received from the first user and the first search phrase and the second search phrase to form a personalized search query; and searching the content index using the personalized search query to produce the search result set comprising documents relevant to the personalized search query.
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20. A computer-implemented method of providing a search result set relevant to a search query, the method comprising: hosting a social network comprising a plurality of communities, each community having a plurality of users; storing a user profile for each user including a first user and a second user, the user profile identifying the user's membership in at least one of the communities; receiving the search query directed to a content index that is independent of the social network from the first user who is a member of at least one of the communities; determining personalization information including a first search phrase and a second search phrase, the first search phrase being determined from the first user's user profile and data from the second user's user profile, the second user's user profile having a degree of separation from the first user's user profile in the social network, and the second search phrase being based at least in part on the first user's membership in the at least one of the communities and a level of personalization selected by the first user for personalizing the search query received from the first user, wherein the level of personalization indicates a total number of the communities from which the second search phrase is retrieved; combining the search query received from the first user and the first search phrase and the second search phrase to form a personalized search query; and searching the content index using the personalized search query to produce the search result set comprising documents relevant to the personalized search query. 28. The method of claim 20 , further comprising ranking the search result set based on the personalization information.
| 0.746809 |
8,589,411 | 4 | 5 |
4. A non-transitory computer storage medium encoded with instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: maintaining an index that indexes source code files, the index including, for each source code file, a respective popularity score, a file name of the source code file, and data describing one or more tags associated with the source code file, wherein the data describing tag comprises a tag name, a tag definition, a description of the source code file, and one or more code lines associated with the tag; determining the respective popularity score stored in the index for each source code file based on a quantity of references to each source code file in source code of other source code files included in the index; receiving a search query that includes a source code tag and data specifying a programming language; identifying, using the index, search results that satisfy the search query, each search result referencing a respective source code file; ranking the identified search results based on respective popularity scores of the referenced source code files, wherein search results that reference source code files that are not associated with the specified programming language are ranked lower than other search results; and providing the ranked search results in response to the search query.
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4. A non-transitory computer storage medium encoded with instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: maintaining an index that indexes source code files, the index including, for each source code file, a respective popularity score, a file name of the source code file, and data describing one or more tags associated with the source code file, wherein the data describing tag comprises a tag name, a tag definition, a description of the source code file, and one or more code lines associated with the tag; determining the respective popularity score stored in the index for each source code file based on a quantity of references to each source code file in source code of other source code files included in the index; receiving a search query that includes a source code tag and data specifying a programming language; identifying, using the index, search results that satisfy the search query, each search result referencing a respective source code file; ranking the identified search results based on respective popularity scores of the referenced source code files, wherein search results that reference source code files that are not associated with the specified programming language are ranked lower than other search results; and providing the ranked search results in response to the search query. 5. The computer storage medium of claim 4 , wherein a reference to a particular source code file in source code of a second source code file is one of an import statement or an include statement in the source code of the second source code file.
| 0.5 |
9,070,047 | 9 | 10 |
9. A labeling system comprising: one or more processors; a graph definer executed by the one or more processors and configured to define a factor graph in processor-readable memory, the factor graph including a factor graph model replicated for each variable node of a set of variable nodes, each variable node being informed by one or more factor types in the factor graph model, each factor type being implemented as a single decision tree, at least one factor type defining the relationship among more than two variable nodes; and a graph trainer executed by the one or more processors and configured to train structure and parameterization of each decision tree using training data having a plurality of datasets, each dataset having elements of at least one labeled property, the training executing an objective function that determines the parameters of each decision tree.
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9. A labeling system comprising: one or more processors; a graph definer executed by the one or more processors and configured to define a factor graph in processor-readable memory, the factor graph including a factor graph model replicated for each variable node of a set of variable nodes, each variable node being informed by one or more factor types in the factor graph model, each factor type being implemented as a single decision tree, at least one factor type defining the relationship among more than two variable nodes; and a graph trainer executed by the one or more processors and configured to train structure and parameterization of each decision tree using training data having a plurality of datasets, each dataset having elements of at least one labeled property, the training executing an objective function that determines the parameters of each decision tree. 10. The labeling system of claim 9 further comprising: a labeling engine configured to execute the factor graph on an dataset having elements of at least one unlabeled properties to determine a label for the at least one unlabeled property for each element.
| 0.5 |
9,161,007 | 15 | 17 |
15. At least one computer storage device storing computer-executable instructions that, based on execution by at least one computing device that includes at least one processor and memory, configure the at least one computing device to perform actions comprising: receiving, by the at least one computing device, a selection of a theme script; generating, by the at least one computing device a story, where the generated story comprises at least a portion of user assets organized according to the theme script, and where the generating comprises: applying a plurality of rules from the theme script to the user assets, selecting at least the portion of the user assets according to the theme script and meta data of the user assets, automatically generating an introduction section according to the theme script at a beginning of the generated story, where the introduction section comprises a title generated according to the meta data of the user assets in response to the meta data being sufficient for generating the title, and where the introduction section comprises a generic title in response to the meta data being insufficient for generating the title, and automatically generating a conclusion section according to the theme script at an end of the generated story.
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15. At least one computer storage device storing computer-executable instructions that, based on execution by at least one computing device that includes at least one processor and memory, configure the at least one computing device to perform actions comprising: receiving, by the at least one computing device, a selection of a theme script; generating, by the at least one computing device a story, where the generated story comprises at least a portion of user assets organized according to the theme script, and where the generating comprises: applying a plurality of rules from the theme script to the user assets, selecting at least the portion of the user assets according to the theme script and meta data of the user assets, automatically generating an introduction section according to the theme script at a beginning of the generated story, where the introduction section comprises a title generated according to the meta data of the user assets in response to the meta data being sufficient for generating the title, and where the introduction section comprises a generic title in response to the meta data being insufficient for generating the title, and automatically generating a conclusion section according to the theme script at an end of the generated story. 17. The at least one computer storage device of claim 15 where the plurality of rules define the story to be generated by the method, and where the plurality of rules are configured for specifying processing of the portion of the user assets including filtering, arranging, and transforming of the portion of the user assets.
| 0.5 |
8,433,708 | 3 | 5 |
3. The computer readable medium of claim 1 further comprising a step of: attaching the data structure to the formatted document, wherein, when a portion of text is copied from the formatted document, a corresponding internal citation is included with the copied portion.
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3. The computer readable medium of claim 1 further comprising a step of: attaching the data structure to the formatted document, wherein, when a portion of text is copied from the formatted document, a corresponding internal citation is included with the copied portion. 5. The computer readable medium of claim 3 wherein the attaching step yields a searchable annotated formatted document.
| 0.709756 |
8,904,269 | 5 | 6 |
5. The system of claim 1 , wherein the multimedia engine is further enabled to generate the slide show using presentation parameters extracted from the separate variables file.
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5. The system of claim 1 , wherein the multimedia engine is further enabled to generate the slide show using presentation parameters extracted from the separate variables file. 6. The system of claim 5 , wherein the presentation parameters comprise slide transition times and slide display times.
| 0.5 |
8,255,380 | 14 | 15 |
14. Computer apparatus as claimed in claim 11 wherein the certain objects are topics.
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14. Computer apparatus as claimed in claim 11 wherein the certain objects are topics. 15. Computer apparatus as claimed in claim 14 wherein the hierarchy is an ontology of the topics.
| 0.5 |
9,582,483 | 13 | 14 |
13. A non-transitory computer storage medium storing instructions readable by a computerized device, said computerized device executing said instructions to perform a method comprising: inputting a plurality of different input documents; receiving user input to identify a similarity standard that determines how closely elements of said different input documents must match in order to be considered repeated elements; automatically identifying said repeated elements and unique elements within each of said different input documents based on said similarity standard; automatically generating one or more templates using said repeated elements and unique elements within said different input documents, said templates being generated to have said repeated elements and dynamic objects corresponding to said unique elements, said repeated elements being similar for all documents that are represented by a given template, said unique elements having at least one difference between said documents that are represented by said given template, and said dynamic objects comprising a placeholder for said unique elements within said templates; automatically storing variable data for each of said dynamic objects from said unique elements; outputting said templates and said variable data; receiving user acceptance and refusal of said repeated elements and said dynamic objects in said templates; automatically learning patterns of acceptable template objects and refused template objects based on said user acceptance and refusal; and automatically revising said identifying of said repeated elements and said unique elements and said generating of said templates for additional documents, based on using said patterns of acceptable template objects and refused template objects to alter said similarity standard.
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13. A non-transitory computer storage medium storing instructions readable by a computerized device, said computerized device executing said instructions to perform a method comprising: inputting a plurality of different input documents; receiving user input to identify a similarity standard that determines how closely elements of said different input documents must match in order to be considered repeated elements; automatically identifying said repeated elements and unique elements within each of said different input documents based on said similarity standard; automatically generating one or more templates using said repeated elements and unique elements within said different input documents, said templates being generated to have said repeated elements and dynamic objects corresponding to said unique elements, said repeated elements being similar for all documents that are represented by a given template, said unique elements having at least one difference between said documents that are represented by said given template, and said dynamic objects comprising a placeholder for said unique elements within said templates; automatically storing variable data for each of said dynamic objects from said unique elements; outputting said templates and said variable data; receiving user acceptance and refusal of said repeated elements and said dynamic objects in said templates; automatically learning patterns of acceptable template objects and refused template objects based on said user acceptance and refusal; and automatically revising said identifying of said repeated elements and said unique elements and said generating of said templates for additional documents, based on using said patterns of acceptable template objects and refused template objects to alter said similarity standard. 14. The non-transitory computer storage medium according to claim 13 , said learning of said patterns of acceptable template objects and refused template objects comprising learning at least one of: acceptable data lengths for said unique elements; acceptable locations for said unique elements; and acceptable data types for said unique elements.
| 0.5 |
9,165,028 | 3 | 4 |
3. The method of claim 1 , further comprising: determining, for at least a first modification of the modifications, a query pattern of the first modification; wherein the popularity measure for the first modification includes a query pattern popularity measure indicative of the popularity of the query pattern of the first modification.
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3. The method of claim 1 , further comprising: determining, for at least a first modification of the modifications, a query pattern of the first modification; wherein the popularity measure for the first modification includes a query pattern popularity measure indicative of the popularity of the query pattern of the first modification. 4. The method of claim 3 , wherein determining the query pattern of the first modification includes: determining a category of an n-gram in the first modification; and substituting the n-gram with an identifier of the category.
| 0.5 |
8,706,478 | 10 | 14 |
10. Apparatus for transforming a natural language request for modifying a set of subscriptions for a publish/subscribe topic string, the apparatus comprising: at least one processing device operable to (A) receive a natural language request for modifying a set of subscriptions for one or more topics in a publish/subscribe topic hierarchy, the natural language request comprising a topic in the hierarchy and a predetermined natural language element; (B) transform the natural language request into a publish/subscribe topic string, wherein the predetermined natural language element is transformed into a publish/subscribe symbol, the symbol identifying a hierarchical relationship between the topic in the natural language request and one or more other topics in the topic hierarchy; and (C) modify one or more subscriptions to include one or more topics having the identified hierarchical relationship to the topic in the natural language request based on the transformed topic string.
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10. Apparatus for transforming a natural language request for modifying a set of subscriptions for a publish/subscribe topic string, the apparatus comprising: at least one processing device operable to (A) receive a natural language request for modifying a set of subscriptions for one or more topics in a publish/subscribe topic hierarchy, the natural language request comprising a topic in the hierarchy and a predetermined natural language element; (B) transform the natural language request into a publish/subscribe topic string, wherein the predetermined natural language element is transformed into a publish/subscribe symbol, the symbol identifying a hierarchical relationship between the topic in the natural language request and one or more other topics in the topic hierarchy; and (C) modify one or more subscriptions to include one or more topics having the identified hierarchical relationship to the topic in the natural language request based on the transformed topic string. 14. Apparatus according to claim 10 , where the at least one processing device is further operable such that, in response to the identification of one of a set of predetermined elements in the natural language request in relation to one or more topics, contextual data is used to identify a given topic for use in the topic string.
| 0.5 |
9,020,207 | 1 | 8 |
1. A biometric authentication system comprising: a biometric enrollment system configured to determine, for each person in a group of multiple, different people, a similarity score that represents a similarity between a biometric image of at least a portion of the corresponding person from the group of multiple, different people and a reference image and sort biometric data that includes all of the determined similarity scores using the determined similarity scores, wherein the reference image is used in determining all of the similarity scores included in the biometric data; a data storage system configured to maintain, for the group of multiple, different people, biometric data that includes all of the sorted similarity scores; and a biometric verification system that includes at least one processor, the biometric verification system being configured to: access a particular biometric image of at least a portion of a particular person; access the reference image used in computing all of the similarity scores maintained in the data storage system; compute a particular similarity score that represents similarity between the accessed particular biometric image and the reference image; search, using the computed particular similarity score, the sorted similarity scores included in the biometric data for the group of multiple, different people; determine whether the data storage system includes data for the particular person in the biometric data based on the search of the sorted similarity scores included in the biometric data using the computed particular similarity score; and output a result based on the determination of whether the data storage system includes data for the particular person in the biometric data.
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1. A biometric authentication system comprising: a biometric enrollment system configured to determine, for each person in a group of multiple, different people, a similarity score that represents a similarity between a biometric image of at least a portion of the corresponding person from the group of multiple, different people and a reference image and sort biometric data that includes all of the determined similarity scores using the determined similarity scores, wherein the reference image is used in determining all of the similarity scores included in the biometric data; a data storage system configured to maintain, for the group of multiple, different people, biometric data that includes all of the sorted similarity scores; and a biometric verification system that includes at least one processor, the biometric verification system being configured to: access a particular biometric image of at least a portion of a particular person; access the reference image used in computing all of the similarity scores maintained in the data storage system; compute a particular similarity score that represents similarity between the accessed particular biometric image and the reference image; search, using the computed particular similarity score, the sorted similarity scores included in the biometric data for the group of multiple, different people; determine whether the data storage system includes data for the particular person in the biometric data based on the search of the sorted similarity scores included in the biometric data using the computed particular similarity score; and output a result based on the determination of whether the data storage system includes data for the particular person in the biometric data. 8. The biometric authentication system of claim 1 , wherein the biometric verification system is configured to compute the particular similarity score that represents similarity between the accessed particular biometric image and the reference image by using an image similarity process to generate a similarity measure between the accessed particular biometric image and the reference image and normalizing the similarity measure to a similarity score between zero and one.
| 0.829128 |
8,493,229 | 1 | 8 |
1. A product container, the container comprising: a body having an opening associated with an interior portion; a product disposed within the interior portion; an enclosure member positioned proximate to the opening and movable between a first position in which the product is retained in the interior portion and a second position in which the product can exit the interior portion via the opening; a sensor operably coupled to at least one of the body and the enclosure member; and an audible warning system carried by the container and operably coupled to the sensor, wherein the audible warning system is configured to audibly output a spoken warning regarding the product in response to receiving an indication from the sensor associated with movement of the enclosure member from the first position toward the second position.
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1. A product container, the container comprising: a body having an opening associated with an interior portion; a product disposed within the interior portion; an enclosure member positioned proximate to the opening and movable between a first position in which the product is retained in the interior portion and a second position in which the product can exit the interior portion via the opening; a sensor operably coupled to at least one of the body and the enclosure member; and an audible warning system carried by the container and operably coupled to the sensor, wherein the audible warning system is configured to audibly output a spoken warning regarding the product in response to receiving an indication from the sensor associated with movement of the enclosure member from the first position toward the second position. 8. The container of claim 1 wherein the sensor is a proximity sensor, and the indication from the sensor is associated with an object moving within a predetermined area surrounding the sensor.
| 0.794433 |
8,140,981 | 8 | 11 |
8. A computer system for handling message threads, the computer system comprising: one or more processors, one or more computer-readable memories and one or more computer-readable tangible storage devices; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to display a primary dialog window, wherein the primary dialog window includes a first ongoing conversation pane and a first outgoing message pane; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to display, in the primary dialog window, a first discussion between a first party and a second party during a chat session between the first party and the second party; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to, during the chat session, receive an indication, from the first party, of a new thread, wherein the new thread is a subset of the chat session, and wherein the new thread is a second discussion between the first party and the second party; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to, after receiving the indication, display a secondary dialog window, wherein the secondary dialog window includes a second ongoing conversation pane and a second outgoing message pane; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to display responses to first outgoing messages sent from the first outgoing message pane in the first ongoing conversation pane; and program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to display responses to second outgoing messages sent from the second outgoing message pane in the second ongoing conversation pane.
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8. A computer system for handling message threads, the computer system comprising: one or more processors, one or more computer-readable memories and one or more computer-readable tangible storage devices; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to display a primary dialog window, wherein the primary dialog window includes a first ongoing conversation pane and a first outgoing message pane; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to display, in the primary dialog window, a first discussion between a first party and a second party during a chat session between the first party and the second party; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to, during the chat session, receive an indication, from the first party, of a new thread, wherein the new thread is a subset of the chat session, and wherein the new thread is a second discussion between the first party and the second party; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to, after receiving the indication, display a secondary dialog window, wherein the secondary dialog window includes a second ongoing conversation pane and a second outgoing message pane; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to display responses to first outgoing messages sent from the first outgoing message pane in the first ongoing conversation pane; and program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to display responses to second outgoing messages sent from the second outgoing message pane in the second ongoing conversation pane. 11. The computer system according to claim 8 further comprising: program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to display a new thread button in the primary dialog window; and program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to receive a selection of the new thread button, wherein the indication is the selection of the new thread button.
| 0.653153 |
7,598,942 | 50 | 66 |
50. The method of claim 43 , wherein controlling the component comprises controlling a three-space object through three translational degrees of freedom and three rotational degrees of freedom.
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50. The method of claim 43 , wherein controlling the component comprises controlling a three-space object through three translational degrees of freedom and three rotational degrees of freedom. 66. The method of claim 50 , comprising controlling movement of the three-space object along a z-axis using forward-backward movement of the first appendage.
| 0.702652 |
8,214,208 | 7 | 8 |
7. An apparatus, comprising: a storage device configured to store a plurality of portable voice profiles; and a computer-based system coupled to the storage device, the computer-based system including (1) a speech recognition engine configured to convert a voice signal to text, the speech recognition engine including speaker identification logic configured to analyze the voice signal to identify the speaker and then dynamically select a particular portable voice profile associated with the identified speaker, in real-time, from the plurality of portable voice profiles; (2) a local group manager configured to manage access privileges to a local user's portable voice profile according to connections between a local user of the apparatus and other members of a group to which the local user belongs and instructions of the local user; and (3) a local voice profile manager configured to receive the plurality of portable voice profiles, each portable voice profile associated with a speaker and including speaker-dependent data accessible to a plurality of speech recognition engines through an interface, the speaker-dependent data to enhance an accuracy with which each speech recognition engine in the plurality of speech recognition engines recognizes spoken words in a voice signal from a speaker associated with a portable voice profile, wherein at least one of the plurality of portable voice profiles includes data derived from use with a variety of speech recognition engines.
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7. An apparatus, comprising: a storage device configured to store a plurality of portable voice profiles; and a computer-based system coupled to the storage device, the computer-based system including (1) a speech recognition engine configured to convert a voice signal to text, the speech recognition engine including speaker identification logic configured to analyze the voice signal to identify the speaker and then dynamically select a particular portable voice profile associated with the identified speaker, in real-time, from the plurality of portable voice profiles; (2) a local group manager configured to manage access privileges to a local user's portable voice profile according to connections between a local user of the apparatus and other members of a group to which the local user belongs and instructions of the local user; and (3) a local voice profile manager configured to receive the plurality of portable voice profiles, each portable voice profile associated with a speaker and including speaker-dependent data accessible to a plurality of speech recognition engines through an interface, the speaker-dependent data to enhance an accuracy with which each speech recognition engine in the plurality of speech recognition engines recognizes spoken words in a voice signal from a speaker associated with a portable voice profile, wherein at least one of the plurality of portable voice profiles includes data derived from use with a variety of speech recognition engines. 8. The apparatus of claim 7 , wherein the local group manager is further configured to facilitate establishing the local user of the apparatus as a member of the group, and to facilitate establishing connections with other users as members of the group.
| 0.748008 |
8,046,329 | 6 | 9 |
6. A computer system comprising: an application server operating on a first host; a backup application configured to create one or more new target database images for each of a group of backup datasets, wherein the backup datasets include data from one or more source databases associated with the application server, wherein at least one of the one or more source databases is hosted on a remote source host separate from the first host; and a client registered with the application server and installed on a backup host, wherein the client is configured to: store a seed document in each of the one or more new target database images, wherein said seed documents are Lotus® replication notes and said replication request is a Lotus® replication request; and modify each seed document to specify which portions of a backup dataset in said group are to be stored in each corresponding new target database image; wherein the backup application receives a backup request; wherein in response to determining the backup request corresponds to an incremental backup request: the backup application is configured to send a replication request to said client, the replication request including a formula that indicates which data should be replicated; the client is configured to activate each seed document in said one or more new target database images, wherein to activate each seed document the client conveys a filter to each said seed document; utilize each said seed document to send a replication request based upon parameters of the filter to a given server responsive to the seed document being activated; and the given server is configured to replicate a filtered portion of each database for which a replication request is received, thereby updating said one or more new target database images, wherein archive logging is not used for replication.
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6. A computer system comprising: an application server operating on a first host; a backup application configured to create one or more new target database images for each of a group of backup datasets, wherein the backup datasets include data from one or more source databases associated with the application server, wherein at least one of the one or more source databases is hosted on a remote source host separate from the first host; and a client registered with the application server and installed on a backup host, wherein the client is configured to: store a seed document in each of the one or more new target database images, wherein said seed documents are Lotus® replication notes and said replication request is a Lotus® replication request; and modify each seed document to specify which portions of a backup dataset in said group are to be stored in each corresponding new target database image; wherein the backup application receives a backup request; wherein in response to determining the backup request corresponds to an incremental backup request: the backup application is configured to send a replication request to said client, the replication request including a formula that indicates which data should be replicated; the client is configured to activate each seed document in said one or more new target database images, wherein to activate each seed document the client conveys a filter to each said seed document; utilize each said seed document to send a replication request based upon parameters of the filter to a given server responsive to the seed document being activated; and the given server is configured to replicate a filtered portion of each database for which a replication request is received, thereby updating said one or more new target database images, wherein archive logging is not used for replication. 9. The system of claim 6 , wherein to update each new target database image includes the backup server synchronizing the new target database images with data from the source databases excluding at least a portion of the data specified in the information used to modify the seed document.
| 0.558462 |
8,175,380 | 9 | 10 |
9. The method as claimed in claim 7 , wherein the step of determining if text exists comprises: setting a center block with a predetermined size centering on a moving block movable by a user in the input image; and determining that the text exists when a number of edges acquired within the set center block exceeds a threshold value.
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9. The method as claimed in claim 7 , wherein the step of determining if text exists comprises: setting a center block with a predetermined size centering on a moving block movable by a user in the input image; and determining that the text exists when a number of edges acquired within the set center block exceeds a threshold value. 10. The method as claimed in claim 9 , wherein the step of determining the text-background color surrounding the text comprises: setting the moving block with the predetermined size around the center block; seeking a histogram and edges by scanning of the moving block; and determining the text-background color using a color value and the number of edges, calculated through analysis of the histogram.
| 0.5 |
8,434,134 | 1 | 17 |
1. A computer-implemented method comprising: receiving, at a computer system, a user request to access an electronic document collection that integrates a plurality of electronic sub-documents that are each of one of a plurality of defined document types; retrieving information that is associated with the document collection and identifies a first sub-document of a first type of the plurality of sub-documents using a first non-address identifier that does not provide a storage location for the first sub-document; retrieving information that is associated with the document collection and identifies a second sub-document of a second type of the plurality of sub-documents using a second non-address identifier that does not provide a storage location for the second sub-document; identifying a first software application that is configured to provide access to the first sub-document; identifying a second software application that is configured to provide access to the second sub-document; initiating a connection with a server that causes execution of the identified first software application and that, using the first non-address identifier, provides access to the first sub document; initiating a connection with a server that causes execution of the identified second software application and that, using the second non-address identifier, provides access to the second sub-document; integrating access to the first sub-document into the document collection ; and integrating access to the second sub-document into the document collection; wherein at least a portion of data in the first sub-document is dynamically linked to the second sub-document using the second non-address identifier; wherein the first software application interprets the first sub-document, identifies the dynamic link and requests data from the second sub-document using the second non-address identifier; wherein the second software application receives a request for data from the second sub-document, interprets the second sub-document, and provides information relating to the requested data.
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1. A computer-implemented method comprising: receiving, at a computer system, a user request to access an electronic document collection that integrates a plurality of electronic sub-documents that are each of one of a plurality of defined document types; retrieving information that is associated with the document collection and identifies a first sub-document of a first type of the plurality of sub-documents using a first non-address identifier that does not provide a storage location for the first sub-document; retrieving information that is associated with the document collection and identifies a second sub-document of a second type of the plurality of sub-documents using a second non-address identifier that does not provide a storage location for the second sub-document; identifying a first software application that is configured to provide access to the first sub-document; identifying a second software application that is configured to provide access to the second sub-document; initiating a connection with a server that causes execution of the identified first software application and that, using the first non-address identifier, provides access to the first sub document; initiating a connection with a server that causes execution of the identified second software application and that, using the second non-address identifier, provides access to the second sub-document; integrating access to the first sub-document into the document collection ; and integrating access to the second sub-document into the document collection; wherein at least a portion of data in the first sub-document is dynamically linked to the second sub-document using the second non-address identifier; wherein the first software application interprets the first sub-document, identifies the dynamic link and requests data from the second sub-document using the second non-address identifier; wherein the second software application receives a request for data from the second sub-document, interprets the second sub-document, and provides information relating to the requested data. 17. The computer-implemented method of claim 1 , wherein the computer system comprises a hosted public server system.
| 0.911364 |
8,145,488 | 9 | 12 |
9. The one or more computer storage devices of claim 8 , wherein the piecewise function is a spline function, and the plurality of connected segments correspond to a plurality of spline segments.
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9. The one or more computer storage devices of claim 8 , wherein the piecewise function is a spline function, and the plurality of connected segments correspond to a plurality of spline segments. 12. The one or more computer storage devices of claim 9 , wherein the spline function references a set of spline parameters that are shared by plural spline functions in a class.
| 0.763926 |
10,027,346 | 14 | 23 |
14. A method for compressing an input block of characters using a hardware data compressor, the method comprising: scanning, by a first hardware engine, an input block of characters and responsively producing a stream of tokens, the stream of tokens comprising replacement back pointers to matched strings of characters of the input block and non-replaced characters of the input block; receiving, by a second hardware engine, the stream of tokens from the first hardware engine; maintaining, by the second hardware engine, the sorted list by frequency of occurrence concurrently with said producing the stream of tokens by the first hardware engine.
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14. A method for compressing an input block of characters using a hardware data compressor, the method comprising: scanning, by a first hardware engine, an input block of characters and responsively producing a stream of tokens, the stream of tokens comprising replacement back pointers to matched strings of characters of the input block and non-replaced characters of the input block; receiving, by a second hardware engine, the stream of tokens from the first hardware engine; maintaining, by the second hardware engine, the sorted list by frequency of occurrence concurrently with said producing the stream of tokens by the first hardware engine. 23. The method of claim 14 , further comprising: constructing, by a third hardware engine, a Huffman code table using the sorted list of symbols.
| 0.824029 |
7,487,166 | 1 | 10 |
1. A method for mapping an XML (eXtensible Markup Language) data structure to an ontology, wherein the method is encoded in program instructions that are recorded on and executed by at least one of a computing device and a co-processor device, the method comprising: providing an XML document with a corresponding XML schema definition; a) mapping XML schema declarations and definitions to ontology schema definitions by: mapping XML element and attribute declarations to ontology property definition; and mapping XML complexType definitions to ontology class definitions; a1) validating the XML document by the XML schema to generate a PSVI (post schema validation info set) annotation of the XML document; b) using the PSVI annotation from the validation of the XML document by the XML schema for mapping XML nodes with known PSVI-type to ontology instances by: mapping PSVI complexType annotations to ontology class instances; mapping element and attribute nodes to ontology property instances; and mapping XML nodes with a known PSVI-type in the following way to ontology instances: b1) for each XML element node with a complexType annotation an instance of a class of the ontology is generated, the class of this instance is the class to which this complexType definition is mapped; b2) mapping an XML simpleType element node to a datatype property instance of the ontology, the domain class instance of the datatype property instance being the instance which is generated for the PSVI type annotation where the element node is used; b3) mapping an XML simpleContent element node with an attribute to an object property instance of the ontology, the object property having a domain class instance and a range class instance, the domain class instance of the object property instance being the instance which is generated for the type annotation where the element is used, the range class instance of the object property instance being the class instance which is generated for the PSVI type annotation of the XML simpleContent element node; b4) mapping an XML complexContent element node to an object property instance of the ontology, the object property having a domain class instance and a range class instance, the domain class instance of the object property instance being the instance which is generated for the PSVI type annotation where the element is used, and the range class instance of the object property instance being the class instance which is generated for the PSVI type annotation of the XML complexContent element node; b5) mapping an XML simpleType attribute node to a datatype property instance of the ontology, the domain class instance of the datatype property instance being the instance which is generated for the PSVI type annotation where the attribute node is used; c) defining predefined ontology schema definitions by defining a predefined ontology class definition and a predefined ontology datatype property definition and a predefined object property definition; d) mapping XML nodes without PSVI-type annotation to ontology instances and to ontology definitions by mapping XML element and attribute nodes to ontology property instances of the predefined ontology property definitions and ontology class instances of the predefined ontology class definitions; e) wherein this mapping is performed dynamically while evaluating a query inquiring data from the XML document.
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1. A method for mapping an XML (eXtensible Markup Language) data structure to an ontology, wherein the method is encoded in program instructions that are recorded on and executed by at least one of a computing device and a co-processor device, the method comprising: providing an XML document with a corresponding XML schema definition; a) mapping XML schema declarations and definitions to ontology schema definitions by: mapping XML element and attribute declarations to ontology property definition; and mapping XML complexType definitions to ontology class definitions; a1) validating the XML document by the XML schema to generate a PSVI (post schema validation info set) annotation of the XML document; b) using the PSVI annotation from the validation of the XML document by the XML schema for mapping XML nodes with known PSVI-type to ontology instances by: mapping PSVI complexType annotations to ontology class instances; mapping element and attribute nodes to ontology property instances; and mapping XML nodes with a known PSVI-type in the following way to ontology instances: b1) for each XML element node with a complexType annotation an instance of a class of the ontology is generated, the class of this instance is the class to which this complexType definition is mapped; b2) mapping an XML simpleType element node to a datatype property instance of the ontology, the domain class instance of the datatype property instance being the instance which is generated for the PSVI type annotation where the element node is used; b3) mapping an XML simpleContent element node with an attribute to an object property instance of the ontology, the object property having a domain class instance and a range class instance, the domain class instance of the object property instance being the instance which is generated for the type annotation where the element is used, the range class instance of the object property instance being the class instance which is generated for the PSVI type annotation of the XML simpleContent element node; b4) mapping an XML complexContent element node to an object property instance of the ontology, the object property having a domain class instance and a range class instance, the domain class instance of the object property instance being the instance which is generated for the PSVI type annotation where the element is used, and the range class instance of the object property instance being the class instance which is generated for the PSVI type annotation of the XML complexContent element node; b5) mapping an XML simpleType attribute node to a datatype property instance of the ontology, the domain class instance of the datatype property instance being the instance which is generated for the PSVI type annotation where the attribute node is used; c) defining predefined ontology schema definitions by defining a predefined ontology class definition and a predefined ontology datatype property definition and a predefined object property definition; d) mapping XML nodes without PSVI-type annotation to ontology instances and to ontology definitions by mapping XML element and attribute nodes to ontology property instances of the predefined ontology property definitions and ontology class instances of the predefined ontology class definitions; e) wherein this mapping is performed dynamically while evaluating a query inquiring data from the XML document. 10. A computer loadable data structure, that is adapted to perform the method according to the method claim 1 while the data structure is being executed on a computer.
| 0.947152 |
9,787,637 | 1 | 2 |
1. A computing apparatus defining a first location in a network and comprising: at least one memory comprising instructions; and at least one computing processor configured for executing the instructions, wherein the instructions cause the at least one computing processor to perform the operations of: establishing, using computing instructions comprised in the at least one memory, a web browsing session with the network; selecting, using a traffic routing software unit comprised in the at least one computing processor, a secure web container through which web content is to be accessed during the web browsing session, wherein the network comprises two or more secure web containers, and wherein a first secure web container, of the two or more secure web containers, is located in a first secure web container location of the network and a second secure web container, of the two or more secure web containers, is located in a second secure web container location of the network; selecting, using the traffic routing software unit, an egress node through which the web content is to be routed during the web browsing session, wherein the network comprises two or more egress nodes, and wherein a first egress node, of the two or more egress nodes, is located in a first egress node location of the network and a second egress node, of the two or more egress nodes, is located in a second egress node location of the network; and providing, via a communication connection between at least one of the two or more secure web containers and a user device defining a second location in the network, a user of the user device with secure access to the web content, wherein the web content is rendered or accessed at the secure web container, and wherein providing the user of the user device with the secure access to the web content comprises manipulating, at the secure web container, one or more elements of a browser fingerprint presented to or accessed by a web server associated with the web content such that a characteristic of the user device comprised in the manipulated browser fingerprint and a third location presented to or accessed by the web server is different from an actual characteristic of the user device and the second location, respectively.
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1. A computing apparatus defining a first location in a network and comprising: at least one memory comprising instructions; and at least one computing processor configured for executing the instructions, wherein the instructions cause the at least one computing processor to perform the operations of: establishing, using computing instructions comprised in the at least one memory, a web browsing session with the network; selecting, using a traffic routing software unit comprised in the at least one computing processor, a secure web container through which web content is to be accessed during the web browsing session, wherein the network comprises two or more secure web containers, and wherein a first secure web container, of the two or more secure web containers, is located in a first secure web container location of the network and a second secure web container, of the two or more secure web containers, is located in a second secure web container location of the network; selecting, using the traffic routing software unit, an egress node through which the web content is to be routed during the web browsing session, wherein the network comprises two or more egress nodes, and wherein a first egress node, of the two or more egress nodes, is located in a first egress node location of the network and a second egress node, of the two or more egress nodes, is located in a second egress node location of the network; and providing, via a communication connection between at least one of the two or more secure web containers and a user device defining a second location in the network, a user of the user device with secure access to the web content, wherein the web content is rendered or accessed at the secure web container, and wherein providing the user of the user device with the secure access to the web content comprises manipulating, at the secure web container, one or more elements of a browser fingerprint presented to or accessed by a web server associated with the web content such that a characteristic of the user device comprised in the manipulated browser fingerprint and a third location presented to or accessed by the web server is different from an actual characteristic of the user device and the second location, respectively. 2. The computing apparatus of claim 1 , wherein the instructions further cause the at least one computing processor to perform the operations of: generating, using a content management software unit of the at least one computing processor, a request to receive, at the secure web container, second web content from a second web server; and transmitting, using the traffic routing software unit, the request to the secure web container, wherein the request is transmitted to the web server from the secure web container via the network by routing the request through the egress node.
| 0.5 |
7,761,287 | 3 | 4 |
3. The computing device of claim 1 wherein the sequences and probabilities of the opinion data store are associated with a positive opinion or negative opinion.
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3. The computing device of claim 1 wherein the sequences and probabilities of the opinion data store are associated with a positive opinion or negative opinion. 4. The computing device of claim 3 wherein the infer opinion component calculates a combined probability that the target words express a positive opinion and a combined probability that the target words express a negative opinion and selects as the opinion for the target words the opinion associated with the higher combined probability.
| 0.5 |
7,676,517 | 12 | 16 |
12. A computer-implemented method of processing a query, the method comprising: employing a processor to execute code instructions stored in a computer readable medium, the code instructions when executed by the processor implement the following acts: receiving a query character into a query input box of a client application; searching an Internet-based index service in realtime that returns search results based on the received query character; suggesting additional query characters in a realtime look-ahead manner, the additional query characters are presented to a user in association with the received query character in response to receiving the search results; facilitating the inclusion of one or more of the additional query characters and one or more of impacting, refining, and filtering additional query data resulting from the one or more additional query characters; automatically generating one or more rules to adjust the realtime look-ahead injection of the additional query characters into the query input box of the client application based on user interaction, wherein the realtime look-ahead injection of the additional query characters is more automated when the user interaction indicates the user is more skillful and less automated when the user is indicated to be more novice; and employing a probabilistic or statistical-based analysis, or a combination thereof, to prognose or infer an action that a user desires to be automatically performed.
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12. A computer-implemented method of processing a query, the method comprising: employing a processor to execute code instructions stored in a computer readable medium, the code instructions when executed by the processor implement the following acts: receiving a query character into a query input box of a client application; searching an Internet-based index service in realtime that returns search results based on the received query character; suggesting additional query characters in a realtime look-ahead manner, the additional query characters are presented to a user in association with the received query character in response to receiving the search results; facilitating the inclusion of one or more of the additional query characters and one or more of impacting, refining, and filtering additional query data resulting from the one or more additional query characters; automatically generating one or more rules to adjust the realtime look-ahead injection of the additional query characters into the query input box of the client application based on user interaction, wherein the realtime look-ahead injection of the additional query characters is more automated when the user interaction indicates the user is more skillful and less automated when the user is indicated to be more novice; and employing a probabilistic or statistical-based analysis, or a combination thereof, to prognose or infer an action that a user desires to be automatically performed. 16. The method of claim 12 , further comprising: suggesting a correct spelling of URL query data and correcting URL query data based on URL search results; and suggesting a URL to the user based on the URL search results.
| 0.550813 |
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