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:
Oct. 23, 2018

Filed:

Apr. 19, 2018
Applicant:

Sift Science, Inc., San Francisco, CA (US);

Inventors:

Fred Sadaghiani, San Francisco, CA (US);

Alex Paino, San Francisco, CA (US);

Jacob Burnim, San Francisco, CA (US);

Keren Gu, San Francisco, CA (US);

Gary Lee, San Francisco, CA (US);

Noah Grant, San Francisco, CA (US);

Eugenia Ho, San Francisco, CA (US);

Doug Beeferman, San Francisco, CA (US);

Assignee:

Sift Science, Inc., San Francisco, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
H04L 29/06 (2006.01); G06Q 20/40 (2012.01); G06N 99/00 (2010.01); G06F 17/30 (2006.01); G06F 17/00 (2006.01); G06F 11/00 (2006.01); G06F 12/14 (2006.01); G06F 12/16 (2006.01); G06F 21/50 (2013.01);
U.S. Cl.
CPC ...
G06Q 20/4016 (2013.01); G06F 17/30598 (2013.01); G06N 99/005 (2013.01); G06F 21/50 (2013.01);
Abstract

Systems and methods include: implementing a first machine learning model to generate an output of a global digital threat score for an online activity based on an input of the collected digital event data; implementing a second machine learning model that generates a category inference of a category of digital fraud or a category of digital abuse from a plurality of digital fraud or digital abuse categories; selecting a third machine learning model from an ensemble of digital fraud or digital abuse machine learning models based on the category inference generated by the second machine learning model, wherein the ensemble of digital fraud or digital abuse machine learning models comprise a plurality of disparate digital fraud or digital abuse category-specific machine learning models; and implementing the selected third machine learning model to generate a digital fraud or digital abuse category-specific threat score based on the digital event data.


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