US 12,333,461 B2
Iterative order availability for an online fulfillment system
Krishna Kumar Selvam, Cupertino, CA (US); Mouna Cheikhna, San Francisco, CA (US); Michael Chen, San Francisco, CA (US); Dylan Wang, Emeryville, CA (US); Joseph Cohen, New York, NY (US); Tahmid Shahriar, New York, NY (US); Graham Adeson, San Francisco, CA (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 Sep. 28, 2022, as Appl. No. 17/955,395.
Prior Publication US 2024/0104449 A1, Mar. 28, 2024
Int. Cl. G06Q 10/0631 (2023.01); G06Q 10/0639 (2023.01); G06Q 30/0601 (2023.01)
CPC G06Q 10/06311 (2013.01) [G06Q 10/06398 (2013.01); G06Q 30/0635 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for fulfilling orders by shoppers having choice to accept an order, the method comprising:
at a computer system comprising a processor and a computer-readable medium:
identifying a set of candidate shoppers for fulfilling a batch of one or more orders;
scoring each candidate shopper for fulfilling the batch, wherein the scoring comprises applying a machine learning model that is trained to predict a likelihood that a shopper would accept a batch of one or more orders if offered, wherein training the machine learning model comprises:
accessing a training dataset comprising a plurality of training items, each training items comprising a batch of orders, features associated with the batch of orders that reflect resource usage to fulfill the batch of order, and an outcome indicating whether a user received the batch of orders accepts the batch of orders;
applying the machine learning model to each training item to predict a likelihood that the user would accept the batch of orders in the respective training example;
updating the training dataset with recent previously delivery orders comprising outcomes of the recent previously delivery orders; and
updating the machine learning model based on the updated training dataset;
selecting a subset of the set of candidate shoppers based on the scores;
sending a notification to one or more devices associated with the subset of the candidate shoppers indicating that the batch is available to be accepted;
determining that the batch has not been accepted for fulfillment by a shopper within a threshold amount of time after sending the notification; and
responsive to the determination that the batch has not been accepted for fulfillment by a shopper within the threshold amount of time after sending the notification:
selecting an additional subset of the set of candidate shoppers based on the scores; and
sending an additional notification to one or more devices associated with the additional subset of candidate shoppers.