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US Patent 10467631 Ranking and tracking suspicious procurement entities

OverviewStructured DataIssuesContributors

Contents

Is a
Patent
Patent

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10467631
Patent Inventor Names
Markus Ettl0
Anthony T. Mazzatti0
Bruce C. Graves0
Gopikrishna Maniachari0
Rajesh Kumar Ravi0
Amit Dhurandhar0
Date of Patent
November 5, 2019
Patent Application Number
15094361
Date Filed
April 8, 2016
Patent Citations Received
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US Patent 12063226 Graph-based multi-staged attack detection in the context of an attack framework
0
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US Patent 11416713 Distributed predictive analytics data set
0
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US Patent 11423143 Anomaly detection based on processes executed within a network
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US Patent 11431741 Detecting unmanaged and unauthorized assets in an information technology network with a recurrent neural network that identifies anomalously-named assets
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US Patent 11449870 Fraud detection rule optimization
0
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US Patent 11853400 Distributed machine learning engine
0
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US Patent 11954688 Apparatus for fraud detection rule optimization
0
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US Patent 11956253 Ranking cybersecurity alerts from multiple sources using machine learning
0
...
Patent Primary Examiner
‌
Fahd A Obeid
Patent abstract

An apparatus, method and computer program product for identifying fraud in transaction data. The method includes: receiving invoice data comprising a vendor, a requestor and events, receiving public data and private data sources, computing a vendor risk score using the public and private data sources matching the vendor of the invoice data, computing a requestor risk score using the public data sources and the private data sources matching the requestor of the invoice data, computing an active invoice score using the vendor risk score and the requestor risk score and when the active invoice score is greater than a predetermined amount, blocking the invoice data. In one embodiment, computing a vendor risk score comprises obtaining a weight and a confidence for the event, calculating an event vendor risk score using the weight times the confidence and combining the event vendor risk scores for all of the events.

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