US 12,002,084 B2
Online shopping system and method for selecting a warehouse for inventory based on predicted availability and predicted replacement machine learning models
Shishir Kumar Prasad, Fremont, CA (US); Sharath Rao Karikurve, Berkeley, CA (US); and Diego Goyret, Los Gatos, CA (US)
Assigned to Maplebear Inc., San Francisco, CA (US)
Filed by Maplebear Inc., San Francisco, CA (US)
Filed on Jul. 6, 2023, as Appl. No. 18/218,967.
Application 18/218,967 is a continuation of application No. 17/230,816, filed on Apr. 14, 2021, granted, now 11,734,749.
Prior Publication US 2023/0351480 A1, Nov. 2, 2023
Int. Cl. G06Q 30/0601 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06Q 10/087 (2023.01); G06Q 30/0201 (2023.01); G06Q 30/0204 (2023.01)
CPC G06Q 30/0639 (2013.01) [G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06Q 10/087 (2013.01); G06Q 30/0201 (2013.01); G06Q 30/0205 (2013.01); G06Q 30/0619 (2013.01); G06Q 30/0629 (2013.01); G06Q 30/0633 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, performed at a computer system comprising at least one processor and memory, comprising:
receiving, at an online concierge system, a request to specify an order from a device associated with a user over a network;
identifying, by the online concierge system, a plurality of candidate warehouse locations from which the order can be fulfilled;
retrieving, by the online concierge system, a set of orders previously received from the user;
selecting, by the online concierge system, a set of items from the set of orders previously received by the online concierge system from the user;
for each candidate warehouse location of the plurality of candidate warehouse locations, determining, by the online concierge system, an availability score by:
accessing a machine learning item availability model that is trained to predict whether a target item is available at a target warehouse location by:
obtaining a plurality of training examples, each training example including a label indicating whether the target item was previously found at the target warehouse location and a plurality of input features for the target item and the target warehouse location,
updating the item availability model by applying the item availability model to each training example, comparing a result of the applying to the label of the training example, and modifying parameters of the item availability model based on the comparing,
obtaining a set of additional training examples for the item availability model, and
retraining the item availability model with the set of additional training examples;
determining a predicted availability of each of the selected set of items by applying the item availability model to the plurality of input features for the selected item and the candidate warehouse location; and
generating the availability score for the candidate warehouse location from the predicted availabilities of the selected set of items;
selecting, by the online concierge system, a warehouse location from the plurality of candidate warehouse locations based on the availability scores of each of the plurality of warehouse locations; and
in response to the request to specify an order, presenting, by the online concierge system, an inventory of items available from the selected warehouse location to the device associated with the user.