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:
Feb. 03, 2026

Filed:

Nov. 28, 2023
Applicant:

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

Inventors:

Aakash Singh, Toronto, CA;

Levon Dolbakian, Berkeley, CA (US);

Hannah Gardner, Toronto, CA;

Kelvin Lau, Vancouver, CA;

Bryan Tran, Brooklyn, NY (US);

Fangzhou Wang, Oakland, CA (US);

Ajay Pankaj Sampat, San Francisco, CA (US);

Jaclyn Tandler, San Francisco, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 10/0631 (2023.01); G06Q 10/087 (2023.01); G06Q 30/0601 (2023.01);
U.S. Cl.
CPC ...
G06Q 10/063112 (2013.01); G06Q 10/087 (2013.01); G06Q 30/0635 (2013.01); G06Q 30/0641 (2013.01);
Abstract

An online concierge system selects picker expertise tags that showcase abilities or experiences of pickers that fulfill orders for the system. The online concierge system establishes a set of user-order cohorts based on characteristics of orders and users placing the orders. When an order is received, the online concierge system identifies a relevant user-order cohort and applies a trained model to predict, in the context of the user-order cohort, the performance of various candidate picker expertise tags applicable to the order. The trained model may be generated via a training and testing process in which different picker expertise tags are tested in the context of a user-order cohort, and performance metrics are observed to learn which picker expertise tags perform best in the context of a user-order cohort.


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