Lyft, Inc.

Maplebear Inc.

Krishna Kumar Selvam

Dor Levi

Austin Broyles

Robert Anthony Farmer

Molly Angelica Ingles Lorenzo

Orit Balicer Tsur

Ajay Pankaj Sampat

Jody Metzenbaum Kelman

Nicholas Chamandy

Vincent Chih-jye Chang

Keshav Eva Klementine Puranmalka

Michael Chen

Christopher Billman

Radhika Anand

Dylan Wang

Youdan Xu

Keshav Puranmalka

Graham Adeson

Qi Xi

Vincent Chih-Jye Chang

Ashish Sinha

Caleb Grisell

Tahmid Shahriar

Mouna Cheikhna

Bita Tadayon

Shang Li

David Zandman

Amirali Darvishzadeh

Joseph Cohen

A machine-learned predictive model is trained to predict potential for customer complaint. The model is part of an online concierge system. The online concierge system accesses a customer order that includes one or more items. The online concierge system determines input data for an item of the one or more items. The online concierge system determines a prediction value associated with potential for customer complaint for the item by applying the machine-learned prediction model to the input data. The online concierge system provides the prediction value to a picker client device associated with a picker who is assigned the item. The picker client device presents an alert to the picker based in part on the prediction value, and the alert includes a message that is customized to mitigate a cause of potential customer complaint for the item.

Machine learning assisted alerts for item picking

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

The disclosed computer-implemented method may include matching transportation requests. By collecting and batching match requests over an extended period, a dynamic transportation matching system may identify more efficient matches (e.g., may match transportation requests with greater overlaps). In addition, by dynamically setting and/or extending the upper bound of time that a transportation request may remain batched with other transportation requests, the dynamic transportation matching system may account for contextual information thereby situationally improving matching efficiencies made possible with higher upper bounds while avoiding requestor dissatisfaction, lost conversions, or other inefficiencies that may result from upper bounds that are too high. Furthermore, by providing a notification interface for a transportation requestor device that notifies the requestor when to be ready, a transportation matching application may provide a positive requestor experience while allowing for longer match windows. Various other methods, systems, and computer-readable media are also disclosed.

Systems and methods for matching transportation requests over extended batching windows

Embodiments provide techniques, including systems and methods, for determining matches of requestors and providers based on a dynamic provider eligibility model. For example, a request matching model uses an estimated arrival time for a requestor and estimated travel times for available providers to a pickup location to determine eligible providers for matching to a ride request. The matching model determines those providers that are far enough away from the request location to allow the requestor time to arrive at the pickup location without matching providers that are too far away, causing delay for the requestor and lowering the efficiency of the system by taking provider system resources from other service areas and increasing provider downtime upon matching. Additionally, embodiments provide more efficient matching processing leading to fewer canceled matched requests, fewer requests for a successful match, and fewer system resources necessary to meet requestor demand.

Determining matches using dynamic provider eligibility model

Embodiments provide techniques for autonomous vehicle fleet modeling and simulation, such as within a dynamic transportation matching system utilizing one or more vehicle types such as non-autonomous vehicles and autonomous vehicles. An autonomous fleet simulation model may be generated based on real-world parameters of an autonomous vehicle fleet, and the parameters may be modified in a simulation in order to determine optimized values that may be applied to the real-world autonomous vehicle fleet.

Dynamic modeling and simulation of an autonomous vehicle fleet using real-time autonomous vehicle sensor input

Growing community of inventors

San Francisco, CA, United States of America

Krishna Kumar Selvam