Maplebear Inc.

Bryan Daniel Bor

Konrad Gustav Miziolek

Ajay Pankaj Sampat

Darren Johnson

Nicholas William Sturm

A user treatment engine uses user data describing characteristics of a user to evaluate a set of treatments that the user treatment engine may apply to the user. The user treatment engine generates treatment cost predictions for the treatments and generates treatment scores for the set of treatments based on the treatment cost predictions for the treatments and the user data for the user. The user treatment engine selects and applies a treatment from the set of treatments based on the generated treatment scores. The user treatment engine determines a reward to the online concierge system for the application of the treatment to the user and updates treatment selection parameters for the applied treatment based on the determined reward.

Treatment selection and application using machine learning to predict cost of treatment where information about cost of treatment is unknown

An online concierge system receives orders from users that include items from one or more warehouses. The online concierge system identifies the orders to shoppers, who select one or more orders to fulfill. The online concierge system uses models to estimate orders likely to be received at different times and shoppers likely to be available to fulfill orders at different times. Responsive to greater than a threshold difference between estimated orders and estimated shoppers during a time interval, the online concierge system selects one or more incentives for shoppers to select orders during the time interval to entice shoppers to select orders during the time interval. An interface displayed to the shoppers by the online concierge system may present a map of warehouses and their estimated number of orders and allow shoppers to identify incentives offered for fulfilling orders at different warehouses during the time interval.

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Pittsburg, CA, United States of America

Bryan Daniel Bor