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. 30, 2024

Filed:

May. 02, 2023
Applicant:

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

Inventors:

David Sheehan, Santa Monica, CA (US);

Siavash Yasini, Altadena, CA (US);

Bingjia Wang, Glendale, CA (US);

Yunyan Zhang, El Monte, CA (US);

Qiumeng Yu, Los Alamos, NM (US);

Ruochen Zha, Pasadena, CA (US);

Adam Kleinman, Santa Monica, CA (US);

Sean Javad Kamkar, Burbank, CA (US);

Lingzhi Du, El Monte, CA (US);

Saar Yalov, Los Angeles, CA (US);

Jerome Louis Budzik, Altadena, CA (US);

Assignee:

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

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/20 (2019.01); G06Q 40/03 (2023.01);
U.S. Cl.
CPC ...
G06N 20/20 (2019.01); G06Q 40/03 (2023.01);
Abstract

This application describes systems and methods for generating machine learning models (MLMs). An exemplary method includes obtaining a sample and user input data characterizing a product or service. A subset of the data is selected from the sample based on sampling the sample according to the user input data. An MLM is trained by applying the data subset as training input to the MLM, thereby providing a trained MLM to emulate a customer selection process unique to the product or service. A user interface (UI) configured to receive other user input data and cause the trained MLM to execute on the other user input data, thereby testing the trained MLM, is presented. A summary of results from the execution of the trained MLM is generated and presented in the UI. The summary of results indicates a contribution to the trained MLM of each of a plurality of features.


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