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:
Apr. 01, 2025

Filed:

Nov. 07, 2023
Applicant:

Zestfinance, Inc., Burbank, CA (US);

Inventors:

Douglas C. Merrill, Burbank, CA (US);

Armen Donigian, Burbank, CA (US);

Eran Dvir, Burbank, CA (US);

Sean Javad Kamkar, Burbank, CA (US);

Evan George Kriminger, Burbank, CA (US);

Vishwaesh Rajiv, Burbank, CA (US);

Michael Edward Ruberry, Burbank, CA (US);

Ozan Sayin, Burbank, CA (US);

Yachen Yan, Burbank, CA (US);

Derek Wilcox, Burbank, CA (US);

John Candido, Burbank, CA (US);

Benjamin Anthony Solecki, Burbank, CA (US);

Jiahuan He, Burbank, CA (US);

Jerome Louis Budzik, Burbank, CA (US);

John J. Beahan, Jr., Burbank, CA (US);

John Wickens Lamb Merrill, Burbank, CA (US);

Esfandiar Alizadeh, Burbank, CA (US);

Liubo Li, Burbank, CA (US);

Carlos Alberta Huertas Villegas, Burbank, CA (US);

Feng Li, Burbank, CA (US);

Randolph Paul Sinnott, Jr., Burbank, CA (US);

Assignee:

ZestFinance, Inc., Burbank, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 5/022 (2023.01); G06F 16/908 (2019.01); G06F 18/2115 (2023.01); G06F 40/44 (2020.01); G06N 5/045 (2023.01); G06N 20/00 (2019.01); G06Q 10/0633 (2023.01);
U.S. Cl.
CPC ...
G06N 5/022 (2013.01); G06F 16/908 (2019.01); G06F 18/2115 (2023.01); G06F 40/44 (2020.01); G06N 5/045 (2013.01); G06N 20/00 (2019.01); G06Q 10/0633 (2013.01);
Abstract

Systems and methods for generating and processing modeling workflows are disclosed. In some examples, a reference distribution of scores generated by a model is determined. The reference distribution of scores is recorded in a structured database. One or more unexpected scores are detected during execution of the model. To detect the one or more unexpected scores, a production distribution of scores is compared with the reference distribution of scores recorded in the structured database. The production distribution of scores is generated by the model for a production input data set. An alert is then provided to an external system, when an alert condition is determined to be satisfied based on the comparison. The alert indicates detection of the one or more unexpected scores.


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