公开(公告)号：US20240177108A1

公开(公告)日：2024-05-30

申请号：US18072311

申请日：2022-11-30

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Youdan Xu ,  Krishna Kumar Selvam ,  Michael Chen ,  Radhika Anand ,  Rebecca Riso ,  Ajay Sampat

IPC: G06Q10/087 ,  G06Q30/0202

CPC classification number: G06Q10/087 ,  G06Q30/0202

Abstract: An online concierge system receives location information associated with pickers and actual orders associated with a geographical zone. A model trained to predict a likelihood an actual order associated with the zone will be available for servicing within a timeframe is accessed and applied to forecasted orders. Each picker is matched to an order for servicing by minimizing a value of a function that is based on a difference between a location associated with each picker matched to an actual order and an associated retailer location, a difference between the location associated with each picker matched to a forecasted order and an associated retailer location, and the predicted likelihood. Recommendations for accepting an actual order, moving to a retailer location associated with a forecasted order, or checking back later with the system are generated based on the matches and sent for display to a client device associated with each picker.

公开(公告)号：US20240202748A1

公开(公告)日：2024-06-20

申请号：US18066257

申请日：2022-12-14

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Radhika Anand ,  Ajay Pankaj Sampat ,  Caleb Grisell ,  Youdan Xu ,  Krishna Kumar Selvam ,  Bita Tadayon

IPC: G06Q30/0202

CPC classification number: G06Q30/0202

Abstract: Techniques for predicting a wait time for a shopper based on a location the shopper's client device are presented. A system identifies a shopper's current location and uses a machine learning model to predict a wait time until the shopper will receive one or more orders. The machine learning model is trained to use input features including a number of orders received during a current time period for fulfillment near the current location, a number of other shoppers available for fulfilling orders during the current time period near the current location, historical information about a presentation of a plurality of orders to a plurality of shoppers near the current location, and historical information about the shopper and the other nearby available shoppers. The system then sends the predicted wait time to the client device for presentation to the shopper.

公开(公告)号：US20240037588A1

公开(公告)日：2024-02-01

申请号：US17877758

申请日：2022-07-29

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Rockson Chang ,  Licheng Yin ,  Chen Zhang ,  Michael Chen ,  Aaron Dou ,  Radhika Anand ,  Nicholas Sturm ,  Ajay Sampat

IPC: G06Q30/02 ,  G06Q10/08 ,  G06Q10/06

CPC classification number: G06Q30/0205 ,  G06Q10/0836 ,  G06Q10/0635 ,  G06Q30/0222

Abstract: The present disclosure is directed to determining shopper-location pairs. In particular, the methods and systems of the present disclosure may identify a set of available shoppers associated with an online shopping concierge platform and located in a geographic area; identify a set of available warehouse locations associated with the online shopping concierge platform and located in the geographic area; and determine, based at least in part on the set of available shoppers, the set of available warehouse locations, and one or more machine learning (ML) models, a set of shopper-location pairs optimized based at least in part on time required by the set of available shoppers to travel from their respective current locations to one or more of the set of available warehouse locations.

公开(公告)号：US20220343395A1

公开(公告)日：2022-10-27

申请号：US17238217

申请日：2021-04-23

Applicant: Maplebear, Inc.(dba Instacart)

Inventor： Amy Luong ,  Michael Righi ,  Graham Adeson ,  Ross Stuart Williams ,  Aman Jain ,  Radhika Anand ,  Ganesh Krishnan

IPC: G06Q30/06

Abstract: For each retailer in the geographic region, an online system predicts a number of orders placed at the retailer and a capacity to fulfill orders during a forecast time period. The capacity of the retailer is predicted based on a number of pickers expected to be available to the retailer during the forecast time period. The online system determines demand for the services of a picker at the retailer based on a comparison of the predicted number of orders and the predicted capacity to fulfill those orders. The online system displays a user interactive map of the geographic region to the picker. The map displays a pin at the location of each retailer in the geographic region, which describes the categorization determined for the retailer. The picker selects a pin, which causes the user interactive map to display a notification characterizing the demand for services at the retailer.