The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 04, 2020

Filed:

Jan. 29, 2019
Applicant:

Pointpredictive Inc., San Diego, CA (US);

Inventors:

Frank J. McKenna, La Jolla, CA (US);

Timothy J. Grace, Encinitas, CA (US);

Gregory Gancarz, San Diego, CA (US);

Michael J. Kennedy, Encinitas, CA (US);

Assignee:

PointPredictive Inc., San Diego, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 40/00 (2012.01); G06Q 40/02 (2012.01); G06N 20/00 (2019.01); G06F 21/57 (2013.01); G06N 20/20 (2019.01); G06N 7/00 (2006.01); G06F 3/01 (2006.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G10L 15/22 (2006.01);
U.S. Cl.
CPC ...
G06Q 40/025 (2013.01); G06F 21/577 (2013.01); G06N 7/005 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06F 3/013 (2013.01); G06F 2221/034 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G10L 15/22 (2013.01); G10L 2015/223 (2013.01);
Abstract

The present disclosure relates generally to a multi-layer fraud identification and risk analysis system. For example, the system may receive application data from a first borrower user, apply application data to one or more machine learning (ML) models, and receive a first score based at least in part upon output of the ML model that is associated with the first borrower user. The system may aggregate scores associated with multiple borrower users to a cumulative dealer user level. The aggregated first scores associated with the dealer user, as well as other corresponding application data, may be provided as input to a second ML model. Output from the second ML model may be associated with the dealer user as a second score.


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