KNOWLEDGE DISCOVERY AGENT SYSTEM
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Accused Products
Abstract
A system and method for processing information in unstructured or structured form, comprising a computer running in a distributed network with one or more data agents. Associations of natural language artifacts may be learned from natural language artifacts in unstructured data sources, and semantic and syntactic relationships may be learned in structured data sources, using grouping based on a criteria of shared features that are dynamically determined without the use of a priori classifications, by employing conditional probability constraints.
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Citations
20 Claims
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1-10. -10. (canceled)
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11. A system, comprising:
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at least one processor in a distributed computer network; at least one data storage device in the distributed computer network, in communication with the at least one processor, and configured to store computer-executable instructions and program data; and at least one data agent in the distributed computer network and in communication with the at least one processor and configured to perform specific functions in response to instructions from the at least one processor; wherein the at least one data agent and at least one processor are configured to perform functions that include creating a co-occurrence matrix of features from at least one unstructured data source in a fixed or variable length window trailing or leading a focus word or symbol that includes natural language processing artifacts, and wherein the created co-occurrence matrix of features is configured as a directed or undirected graph representation of the features and is configured for use with at least one machine learning function, the at least one machine learning function including learning semantic associations between words using, at least in part, calculations of similarity of the words based on usage of the words in context over time. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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19. A computer-implemented method, comprising:
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performing, by at least one data agent and at least one processor in a distributed computer network, functions that include creating a co-occurrence matrix of features from at least one unstructured data source in a fixed or variable length window trailing or leading a focus word or symbol that includes natural language processing artifacts, wherein the created co-occurrence matrix of features is configured as a directed or undirected graph representation of the features and is configured for use with at least one machine learning function, the at least one machine learning function including learning semantic associations between words using, at least in part, calculations of similarity of the words based on usage of the words in context over time.
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20. A non-transitory computer-readable medium storing instructions which, when executed by at least one processor in a distributed computer network, cause at least one computer to perform functions that include:
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performing, by at least one data agent in the distributed computer network and the at least one processor, functions that include creating a co-occurrence matrix of features from at least one unstructured data source in a fixed or variable length window trailing or leading a focus word or symbol that includes natural language processing artifacts, wherein the created co-occurrence matrix of features is configured as a directed or undirected graph representation of the features and is configured for use with at least one machine learning function, the at least one machine learning function including learning semantic associations between words using, at least in part, calculations of similarity of the words based on usage of the words in context over time.
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Specification