US 12,450,556 B2
Delivery time estimation using an attribute-based prediction of a difference between an arrival time and a delivery time for a delivery location
Sharath Rao Karikurve, Berkeley, CA (US); Ramasubramanian Balasubramanian, Jersey City, NJ (US); and Ashish Sinha, Jersey City, NJ (US)
Assigned to Maplebear Inc., San Francisco, CA (US)
Filed by Maplebear Inc., San Francisco, CA (US)
Filed on Mar. 30, 2023, as Appl. No. 18/129,021.
Prior Publication US 2024/0330846 A1, Oct. 3, 2024
Int. Cl. G06Q 10/0835 (2023.01); G06Q 10/087 (2023.01); G06Q 30/0203 (2023.01); G06N 20/00 (2019.01)
CPC G06Q 10/08355 (2013.01) [G06Q 10/087 (2013.01); G06Q 30/0203 (2013.01); G06N 20/00 (2019.01)] 19 Claims
OG exemplary drawing
 
1. A method comprising, at a computer system comprising a processor and a computer-readable medium:
receiving, from a client device associated with a user of an online concierge system, order data associated with an order placed with the online concierge system, wherein the order data describes a delivery location for the order;
receiving information describing a set of attributes associated with the delivery location;
accessing a machine learning model trained to predict a difference between an arrival time and a delivery time for the delivery location, wherein the machine learning model is trained by:
receiving a plurality of attributes associated with a plurality of delivery locations, wherein the plurality of delivery locations is associated with a plurality of orders,
receiving, for each order of the plurality of orders, a label indicating the difference between the arrival time and the delivery time, and
training the machine learning model based at least in part on the plurality of attributes associated with the plurality of delivery locations and the label for each order of the plurality of orders;
applying, by a time difference prediction module executed by a computer processor, the machine learning model to the set of attributes associated with the delivery location to predict the difference between the arrival time and the delivery time for the delivery location;
determining an actual difference between the arrival time and the delivery time for the order;
in response to a difference between the predicted difference and actual difference being at least a threshold difference, sending a prompt for attributes associated with the delivery location for display to the client device; and
training the machine learning model on attributes associated with the delivery location received from the client device in response to the prompt and a label indicating the actual difference between the arrival time and delivery time for the order.