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:
Jun. 02, 2026

Filed:

Aug. 25, 2023
Applicant:

Maplebear Inc., San Francisco, CA (US);

Inventors:

Levi Boxell, Brownsburg, IN (US);

Rustin Partow, San Francisco, CA (US);

Assignee:

Maplebear Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06Q 20/32 (2012.01); G06F 9/451 (2018.01); G06F 16/23 (2019.01); G06F 21/53 (2013.01); G06F 40/221 (2020.01); G06F 40/30 (2020.01); G06Q 10/04 (2023.01); G06Q 20/04 (2012.01); G06Q 20/10 (2012.01); G06Q 20/30 (2012.01); G06Q 20/34 (2012.01); G06Q 20/36 (2012.01); G06Q 20/38 (2012.01); G06Q 20/40 (2012.01); G06Q 40/06 (2012.01); G06T 7/10 (2017.01); H04L 9/32 (2006.01); H04L 29/06 (2006.01); H04W 12/06 (2021.01); H04W 12/08 (2021.01); G06F 21/31 (2013.01); G06F 21/32 (2013.01); G06F 21/45 (2013.01); G06F 21/57 (2013.01); G06F 40/284 (2020.01); G06Q 40/02 (2023.01); H04W 4/14 (2009.01); H04W 12/062 (2021.01); H04W 12/72 (2021.01); H04W 60/00 (2009.01);
U.S. Cl.
CPC ...
G06Q 10/04 (2013.01);
Abstract

A computing system automatically selects treatments for users by generating a propensity vector for a set of treatments and selecting a treatment based on the propensity vector. The propensity vector is determined based on one or more computer models that predict user actions responsive to the treatments and the propensity vector is determined based on the value of a treatment parameter. The treatment parameter is perturbed to determine an adjusted propensity vector. Treatments are applied and outcomes determined with the propensities determined by the current value of the treatment parameter, and counterfactuals for the adjusted treatment vector are determined to evaluate the effect of modifying the treatment parameter. When the perturbed treatment parameter value yields improved results in the counterfactual, the current value is modified to improve performance of the model as a whole without requiring retraining of underlying predictive models.


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