Maplebear Inc.

Ajay Pankaj Sampat

Michael Chen

Jaclyn Tandler

Youdan Xu

Krishna Kumar Selvam

Radhika Anand

Aoshi Li

Adrian Mclean

Caleb Grisell

Chen Zhang

Yuan Gao

Aman Jain

Darren Johnson

Konrad Gustav Miziolek

Graham Adeson

Dylan Wang

Mengyu Zhang

Nicholas Sturm

Brendan Evans Ashby

Aakash Singh

Bryan Daniel Bor

Emily Silberstein

Pak Hong Wong

Chia-Eng Chang

Van Nguyen

Bita Tadayon

Shuo Feng

Mouna Cheikhna

Sai Kannan Sampath

Roman Havran

Bryan Tran

Leo Kwan

Hannah Gardner

Marina Tanasyuk

Joseph Cohen

Matthew Donghyun Kim

Rucheng Xiao

Rockson Chang

Licheng Yin

Fangzhou Wang

Aaron Dou

Rafael Moreno Cesar

Nicholas William Sturm

Sawyer Bowman

Tahmid Shahriar

Ziwei Shi

An online system receives orders including items from customers and allocates the orders to pickers. A picker obtains items included in an order from a customer and delivers the items to the customer to fulfill the order. To provide encouragement for pickers fulfilling orders, the online system generates a highlight reel of accomplishments of a picker fulfilling orders. The online system generates prompts for a generative model, such as a large language model, based on stored information describing order fulfillment by a picker. Content, such as text, generated in response to a prompt by the generative model includes one or more portions of the stored information describing order fulfillment is included in the highlight reel. A template for the highlight reel is selected for the picker, with the template identifying content displayed to the picker and an order in which the content is displayed.

Generating content highlighting work experience using a generative model

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.

Location-based assignment of shopper-location pairs

An online concierge system schedules pickers (shoppers) to fulfill orders from users. During periods of peak demand, the system increases compensation to shoppers to encourage more to participate, thereby reducing missed orders. The system determines an optimal multiplier to increase compensation based on predictive models of supply and demand and then applying an optimization algorithm to search different hyperparameters that affect how the models generate the multipliers. The system selects the optimal multipliers for different time periods and locations. The system may further present the multipliers being offered during future time periods and enable users to activate reminder alerts for select periods. The offers may be presented in a ranked list using a model trained to infer likelihoods of the user accepting participation and/or setting a reminder notification.

User interface for presenting ranked surge pricing opportunities for pickers in an online concierge system

An online concierge system allows customers to place orders to be fulfilled by pickers. An order includes an amount of compensation a customer provides to a picker when the order is fulfilled. A customer may modify the amount of compensation provided to a picker, so some customers may initially specify a large amount of compensation to entice a picker to fulfill an order and then reduce the amount of compensation when the order is fulfilled. To prevent penalizing pickers who fulfilled an order without a problem, the online concierge system trains a model to determine a probability that a reduction in compensation to a picker was unrelated to a problem with order fulfillment. The online concierge system may perform one or more remedial actions for a picker based on the probability determined by the model.

Detection and remediation of improper value modification using machine learning

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.

Machine learning model for predicting wait times to receive orders at different locations

An online concierge system iteratively makes a batch of one or more orders available to an increasing number of shoppers to choose to fulfill. Each shopper may choose to accept or reject a batch for fulfillment. To improve batch acceptance and matching between batches and shoppers, the batches are scored with respect to expected resource costs, likelihood of acceptance by the shopper, and/or other quality metrics to iteratively offer the batch to an increasing number of shoppers (prioritizing the scoring factors) until a shopper accepts. The number of shoppers notified of the batch and the frequency that additional shoppers are selected may vary based on characteristics of the batch and likelihood the batch will be accepted by a shopper.

Iterative order availability for an online fulfillment system

An online system receives information describing an order placed by a user of the online system and a set of contextual features associated with servicing the order. The online system also retrieves a set of user features associated with the user. The online system accesses a machine learning model trained to predict a tip amount the user is likely to provide for servicing the order and applies the machine learning model to a set of inputs, in which the set of inputs includes the information describing the order, the set of user features, and the set of contextual features. The online system then determines a suggested tip amount for servicing the order based on the predicted tip amount.

Using a predictive model to identify context features causing above-average tip amount

Display panel of a programmed computer system with a graphical user interface

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.

Software platform to manage shoppers to fulfill orders for items received by an online concierge system

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San Francisco, CA, United States of America

Ajay Pankaj Sampat