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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.

Patent No.:

US 12412662 B1

PDFFull Text
Date of Patent:
Sep. 09, 2025

Filed:

Sep. 13, 2023

Supervised machine learning-based modeling of sensitivities to potential disruptions

Applicant:

Fair Isaac Corporation, Minneapolis, MN (US);

Inventors:

Gerald Fahner, Austin, TX (US);

Brad Vancho, San Francisco, CA (US);

Assignee:

FAIR ISAAC CORPORATION, Minneapolis, MN (US);

Attorneys:

Mintz, Levin, Cohn, Ferris, Glovsky and Popeo, P.C.

F. Jason Far-hadian, Esq.

Primary Examiner:

Nga B Nguyen

Int. Cl.
CPC ...
G06Q 10/06 (2023.01); G06N 20/20 (2019.01); G06Q 10/0635 (2023.01); G06Q 10/0637 (2023.01); G06Q 30/02 (2023.01); G06Q 40/03 (2023.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01);
U.S. Cl.
CPC ...
G16H 50/20 (2018.01); G06N 20/20 (2019.01); G06Q 10/0635 (2013.01); G06Q 10/06375 (2013.01); G06Q 40/03 (2023.01); G16H 50/30 (2018.01);
Abstract

Computer-implemented systems, methods and products for modeling sensitivities to potential disruptions by observing performances of entities in a first sub-population and a second sub-population using a machine learning model comprising a set of predictors and a binary indicator variable associated with a first entity subjected to a first event associated with the first sub-population, the machine learning model trained to predict an expected performance for the first entity based on at least one of a known attribute associated with the first entity in relation to the first event and a value of the binary indicator variable associated with the first event.

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Fahner, Gerald. (2025). Supervised machine learning-based modeling of sensitivities to potential disruptions (U.S. Patent No. 12412662). U.S. Patent and Trademark Office. https://idiyas.com/patent/badge/12412662

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