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:
Jul. 01, 2025

Filed:

Feb. 01, 2024
Applicant:

Included Health, Inc.;

Inventors:

Nathaniel Freese, San Francisco, CA (US);

Meera Rao, San Francisco, CA (US);

Rick Wolf, San Francisco, CA (US);

Peyton Rose, San Francisco, CA (US);

Stephen Martin, San Francisco, CA (US);

Sameer Soi, San Francisco, CA (US);

Zachary Taylor, San Francisco, CA (US);

Ye Wang, San Francisco, CA (US);

Assignee:

INCLUDED HEALTH, INC., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06F 16/242 (2019.01); G06F 16/2458 (2019.01); G06F 18/2113 (2023.01); G06F 18/214 (2023.01); G06Q 30/0282 (2023.01);
U.S. Cl.
CPC ...
G06F 18/2113 (2023.01); G06F 16/2443 (2019.01); G06F 16/2465 (2019.01); G06F 18/2155 (2023.01); G06N 20/00 (2019.01); G06Q 30/0282 (2013.01);
Abstract

Methods, systems, and computer-readable media for generating a statistically covaried machine learning model for performance measurement of service providers. The method receives a configuration file that includes one or more parameters associated with a plurality of individuals and parses it to generate and executing the database query on input data to generate sets of tabulated data of individuals of the plurality of individuals. The method next determines one or more measures of service providers listed in the configuration file using two or more tabulated data of individuals from the sets of tabulated data of individuals. The method finally generates a covaried machine learning model by training a machine learning model by statistically covarying measures and using them as training data.


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