Delivery time estimation using an attribute-based prediction of a difference between an arrival time and a delivery time for a delivery location

Patent metadata

Field	Value
Publication number	US-12450556-B2
Application number	US-202318129021-A
Country	US
Kind code	B2
Filing date	Mar 30, 2023
Priority date	Mar 30, 2023
Publication date	Oct 21, 2025
Grant date	Oct 21, 2025

Abstract

An online concierge system receives, from a client device associated with a user of the online concierge system, order data associated with an order placed with the online concierge system, in which the order data describes a delivery location for the order. The online concierge system receives information describing a set of attributes associated with the delivery location and accesses a machine learning model trained to predict a difference between an arrival time and a delivery time for the delivery location. The online concierge system applies the 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 and determines an estimated delivery time for the order based at least in part on the predicted difference. The online concierge system sends the estimated delivery time for the order for display to the client device.

First claim

What is claimed is:

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.

2. The method of claim 1 , wherein the set of attributes associated with the delivery location comprises one or more of: a number of steps associated with the delivery location, a number of elevators associated with the delivery location, a number of units associated with the delivery location, a number of floors included in a building associated with the delivery location, a floor number associated with the delivery location, one or more dimensions of the building associated with the delivery location, a gate associated with the delivery location, a call box associated with the delivery location, a security desk associated with the delivery location, or an access code associated with the delivery location.

3. The method of claim 1 , wherein the information describing the set of attributes associated with the delivery location is received from one or more of: a customer client device associated with a customer of the online concierge system, a picker client device associated with a picker associated with the online concierge system, or a third-party system.

4. The method of claim 1 , further comprising:
accessing an additional machine learning model trained to predict a likelihood that an attribute is associated with the delivery location, wherein the additional machine learning model is trained by:
receiving information describing a plurality of features associated with an additional plurality of delivery locations,
receiving, for each additional delivery location of the plurality of additional delivery locations, an additional label indicating whether the attribute is associated with a corresponding additional delivery location, and
training the additional machine learning model based at least in part on the plurality of features associated with the additional plurality of delivery locations and the additional label for each additional delivery location of the plurality of additional delivery locations;

applying the additional machine learning model to a set of features associated with the delivery location to predict the likelihood that the attribute is associated with the delivery location; and
determining one or more attributes of the set of attributes associated with the delivery location based at least in part on the predicted likelihood that the attribute is associated with the delivery location.

5. The method of claim 4 , wherein the set of features associated with the delivery location comprises one or more of: an address associated with the delivery location, a floorplan associated with the delivery location, one or more parking spots associated with the delivery location, a geographical location associated with the delivery location, one or more buildings associated with the delivery location, or one or more dimensions of the one or more buildings associated with the delivery location.

6. The method of claim 1 , further comprising:
generating a request to fulfill the order based at least in part on the predicted difference between the arrival time and the delivery time for the delivery location, wherein the request comprises one or more of: an additional service fee and the information describing the set of attributes associated with the delivery location; and
sending the request to fulfill the order to a picker client device associated with a picker associated with the online concierge system.

7. The method of claim 1 , further comprising:
generating a prompt for the user to perform an action based at least in part on the predicted difference between the arrival time and the delivery time for the delivery location; and
sending the prompt to the client device responsive to receiving a notification that a picker servicing the order has arrived at the delivery location.

8. The method of claim 1 , wherein receiving the information describing the set of attributes associated with the delivery location comprises:
sending a survey associated with the delivery location to one or more of: a customer client device associated with a customer of the online concierge system and a picker client device associated with a picker associated with the online concierge system; and
receiving a response to the survey associated with the delivery location, wherein the response to the survey identifies one or more of the set of attributes associated with the delivery location.

9. The method of claim 1 , wherein the information describing the set of attributes associated with the delivery location is included in one or more of: customer data provided by the user, order data associated with one or more previous orders placed with the online concierge system, or one or more messages associated with one or more previous orders placed with the online concierge system, wherein the one or more messages were sent between a customer client device and a picker client device.

10. A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:
receive, 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;
receive information describing a set of attributes associated with the delivery location;
access 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 plurali

Assignees

• Maplebear Inc

Inventors

• Rao Karikurve Sharath
• Balasubramanian Ramasubramanian
• Sinha Ashish

Classifications

• G06Q10/087

  Inventory or stock management, e.g. order filling, procurement or balancing against orders · CPC title

• G06Q30/0203

  Market surveys; Market polls · CPC title

• G06N20/00

  Machine learning · CPC title

• G06Q10/08355 Primary

  Routing methods · CPC title