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US Patent 10115108 Rendering transaction data to identify fraud detection rule strength

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Patent
Patent
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Patent attributes

Patent Applicant
0
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
101151080
Patent Inventor Names
Anatoly Gendelev0
Kineret Raviv0
Alex Zaslavsky0
Alma Zohar0
Eyal Kolman0
Date of Patent
October 30, 2018
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Patent Application Number
150836850
Date Filed
March 29, 2016
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Patent Citations Received
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US Patent 11334726 Applied artificial intelligence technology for using natural language processing to train a natural language generation system with respect to date and number textual features
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US Patent 11475076 Interactive and conversational data exploration
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US Patent 11341338 Applied artificial intelligence technology for interactively using narrative analytics to focus and control visualizations of data
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US Patent 11341330 Applied artificial intelligence technology for adaptive natural language understanding with term discovery
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US Patent 11399045 Detecting fraudulent logins
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US Patent 11935073 System and methods for general ledger flagging
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US Patent 11989519 Applied artificial intelligence technology for using natural language processing and concept expression templates to train a natural language generation system
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US Patent 12001807 Applied artificial intelligence technology for narrative generation using an invocable analysis service
0
...
Patent Primary Examiner
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Olabode Akintola
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Patent abstract

Techniques of identifying fraud detection rule strength involve varying the rendering of a graph from transaction data. Along these lines, a rules server computer provides a general graph from a group of transaction entries defining a group of fraudulent and authentic transactions on an electronic display. A user defines selection criteria that the rules server computer applies to the group of transaction entries to generate a subgroup of transaction entries. From the subgroup of transaction entries, the rules server computer provides a focused graph on the electronic display from the subgroup of transaction entries defining a subgroup of the group of fraudulent and authentic transactions. A ratio of the number of fraudulent transactions to the number of authentic transactions represented in the focused graph identifies the strength of the selection criteria for use in a fraud detection rule.

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