US 12,423,724 B2
Location-based assignment of shopper-location pairs
Rockson Chang, Somerville, MA (US); Licheng Yin, Burlingame, CA (US); Chen Zhang, San Jose, CA (US); Michael Chen, San Francisco, CA (US); Aaron Dou, Santa Clara, CA (US); Radhika Anand, Sunnyvale, CA (US); Nicholas Sturm, Denver, CO (US); and Ajay Pankaj Sampat, San Francisco, CA (US)
Assigned to Maplebear Inc., San Francisco, CA (US)
Filed by Maplebear Inc., San Francisco, CA (US)
Filed on Jul. 29, 2022, as Appl. No. 17/877,758.
Prior Publication US 2024/0037588 A1, Feb. 1, 2024
Int. Cl. G06Q 30/0204 (2023.01); G06Q 10/0635 (2023.01); G06Q 10/0836 (2023.01); G06Q 30/0207 (2023.01)
CPC G06Q 30/0205 (2013.01) [G06Q 10/0635 (2013.01); G06Q 10/0836 (2013.01); G06Q 30/0222 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
identifying, from one or more computing devices associated with a set of available shoppers, a location at a first timestamp of each available shopper in a geographic area;
identifying a set of available warehouse locations associated with an online system and located in the geographic area;
applying a demand forecast prediction model to the set of available warehouse locations to determine a forecasted demand for an upcoming period of time following the first timestamp for each of the set of available warehouse locations, wherein the demand forecast prediction model is trained by:
collecting a first set of training examples comprising demand over a first set of periods of time,
training the demand forecast prediction model with the first set of training examples,
applying the demand forecast prediction model to a second set of periods of time to predict demand for the second set of periods of time,
generating a second set of training examples comprising the predicted demands for the second set of periods of time, and
retraining the demand forecast prediction model with the second set of training examples;
determining, based at least in part on the set of available shoppers, the set of available warehouse locations, the forecasted demand for each of the set of available warehouse locations, and one or more machine learning (ML) models, a set of shopper-location pairs optimized based at least in part on time required by the set of available shoppers to travel from their respective current locations to one or more of the set of available warehouse locations;
generating, based at least in part on the set of shopper-location pairs, a user interface for displaying communications of at least one of dispatching, instructing, incentivizing, or encouraging one or more of the available shoppers to relocate from their respective current locations to the one or more of the set of available warehouse locations; and
transmitting, prior to the upcoming period of time for transmitting orders to the available shoppers and to one or more of the computing devices associated with a portion of the available shoppers, the user interface for presentation of the communications on the one or more computing devices associated with the portion of the available shoppers.