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US Patent 10013655 Artificial intelligence expert system for anomaly detection

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Is a
Patent
Patent
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Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
100136550
Patent Inventor Names
David Alan Clark0
Date of Patent
July 3, 2018
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Patent Application Number
150819910
Date Filed
March 28, 2016
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Patent Citations Received
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US Patent 10504174 System and method to search and verify borrower information using banking and investment account data and process to systematically share information with lenders and government sponsored agencies for underwriting and securitization phases of the lending cycle
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US Patent 10528407 Integrated statistical log data mining for mean time auto-resolution
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US Patent 10607284 System and method to search and verify borrower information using banking and investment account data and process to systematically share information with lenders and government sponsored agencies for underwriting and securitization phases of the lending cycle
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US Patent 11605126 Detecting fraud in credit applications
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US Patent 10956673 Method and system for identifying citations within regulatory content
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US Patent 11763321 Systems and methods for extracting requirements from regulatory content
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US Patent 11314922 System and method for generating regulatory content requirement descriptions
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US Patent 11232358 Task specific processing of regulatory content
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Patent Primary Examiner
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Tien C Nguyen
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Patent abstract

An artificial intelligence expert system detects an anomaly between an application and a publication provided by an applicant in a given class. The system calculates an application score then uses a decision tree to determine a publication score based on the presence or absence of tokens in the publication. An anomaly is detected when the ratio of the application score to the publication score is greater than a threshold value. The decision tree is trained using prior applications in the same class. The publication score of each leaf node of the decision tree is set equal to an average application score of the prior applications associated with the leaf node.

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