Using computer model to determine availability of service option for delivery of order placed with online system

Patent metadata

Field	Value
Publication number	US-12475411-B2
Application number	US-202318210976-A
Country	US
Kind code	B2
Filing date	Jun 16, 2023
Priority date	Jun 16, 2023
Publication date	Nov 18, 2025
Grant date	Nov 18, 2025

Abstract

Embodiments relate to determining an availability of a service option for delivery of an order placed with an online system. The online system receives an order placed with the online system. The online system accesses a computer model trained to predict a value of metric for an order placed with the online system. The online system applies the computer model to predict the value of the metric for the order. The online system determines which service option of a plurality of service options of the online system is available for delivery of the order, based at least in part on the predicted value of the metric and a threshold. The online system causes the device of the user to display an availability of the determined service option for delivery of the order.

First claim

What is claimed is:

1 . A method comprising, at a computer system comprising a processor and a computer-readable medium:
receiving, via a network and from a device associated with a user of an online system, order data including information about a list of items in an order placed by the user via a user interface of the device associated with the user, information about a delivery location for the order, and a timeframe during which items from the list should be delivered to the delivery location; receiving, via the network, supply data including information about a current level of available supply at the online system for servicing orders; responsive to receiving the order data, accessing a machine-learning model of the online system, wherein the machine-learning model includes a multilayer neural network and is trained to predict a time to accept servicing of the order by the online system, training of the machine-learning model comprises:
generating a set of training examples including data related to a collection of past orders,
obtaining a label for each training example from the set of training examples,
applying a set of parameters of the machine-learning model to the set of training examples to generate an output for each training example from the set of training examples, the set of parameters including weights and biases that are applied at each neuron of the multilayer neural network,
comparing the output for each training example to the label using a loss function to generate a score for each training example, and
updating, through a back-propagation process, the set of parameters of the machine-learning model using the score for each training example;
applying the machine-learning model to the order data and the supply data to generate a value of a metric for the order that is indicative of the time to accept servicing of the order; comparing the value of the metric to a first threshold value; determining, based on a result of comparing the value of the metric to the first threshold value, a service option of a plurality of service options for servicing the order; causing the device associated with the user to display the user interface with an availability of the service option for servicing the order; generating, over a time period, training order data including information about a plurality of values of the metric indicative of a plurality of times to accept servicing of a set of orders, wherein the plurality of times to accept are generated by the machine-learning model, and the plurality of times to accept include the time to accept; collecting, over the time period, information about levels of available supply at the online system for servicing the set of orders; and re-training the machine-learning model by updating, through the back-propagation process, the set of parameters of the machine-learning model using the training order data and the information about levels of available supply.

2 . The method of claim 1 , further comprising:
causing the device associated with the user to display an availability of a first of the plurality of service options having a first delivery time associated with the order, when the value of the metric is greater than the first threshold value; and causing the device associated with the user to display an availability of a second of the plurality of service options having a second delivery time associated with the order that is less than the first delivery time, when the value of the metric is less than or equal to the first threshold value.

3 . The method of claim 2 , further comprising:
receiving, via the network and from the device associated with the user, second order data including information about a second list of items in a second order placed by the user via the user interface of the device associated with the user; receiving, via the network, second supply data including information about a current updated level of available supply at the online system for servicing orders; responsive to receiving the second order data, applying the machine-learning model to the second order data and the second supply data to generate a second value of the metric for the second order that is indicative of a second time to accept servicing of the second order; causing the device associated with the user to display an availability of the first service option having the first delivery time associated with the second order, when the second value of the metric is greater than a second threshold value that is less than the first threshold value; and causing the device associated with the user to display an availability of a third of the plurality of service options having a third delivery time associated with the second order that is less than the second delivery time, when the second value of the metric is less than or equal to the second threshold value.

4 . The method of claim 3 , further comprising:
causing the device associated with the user to display the availability of the first service option having the first delivery time associated with the second order, when the second value of the metric is greater than a smaller of the first threshold value and the second threshold value increased by a second parameter; and causing the device associated with the user to display the availability of the third service option having the third delivery time associated with the second order, when the second value of the metric is greater than or equal to the second threshold value decreased by a first parameter and the second value of the metric is less than or equal to a smaller of the first threshold value and the second threshold value increased by the second parameter.

5 . The method of claim 3 , further comprising:
receiving, via the network, information about a plurality of orders placed with the online system; applying the machine-learning model to the information about the plurality of orders and the second supply data to generate a value of the metric for each of the plurality of orders that is indicative of a respective time to accept each of the plurality of orders; and causing a device associated with a corresponding user of the online system to display the availability of the third service option having the third delivery time associated with each of the plurality of orders, based on the value of the metric for each of the plurality of orders being smaller than the second threshold value decreased by a defined parameter.

6 . The method of claim 1 , further comprising:
causing the device associated with the user to display an availability of a first of the plurality of service options having a first delivery time associated with the order, when the value of the metric is greater than the first threshold value increased by a second parameter; and causing the device associated with the user to display an availability of a second of the plurality of service options having a second delivery time associated with the order that is less than the first delivery time, when the value of the metric is greater than or equal to a larger of a second threshold value and the first threshold value decreased by a first parameter and the value of the metric is less than or equal to the first threshold value increased by the second parameter, wherein the second threshold value is less than the first threshold value.

7 . The method of claim 6 , further comprising:
receiving, via the network, information about a plurality of orders placed with the online system; applying the machine-learning model to the information about the plurality of orders and the supply data to generate a value of the metric for each of the plurality of orders that is indicative of a respective time to accept servicing of each of the p

Assignees

• Maplebear Inc

Inventors

• Wang Zi
• Deng Houtao
• Wang Xiangyu
• Krishnan Ganesh
• Jain Aman

Classifications

• G06Q10/083 Primary

  Shipping · CPC title

• G06Q30/0601

  Electronic shopping [e-shopping] · CPC title

• G06Q10/04 Primary

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title