Patent search ap:("Maplebear Inc.") AND inv:"Soren Zeliger" Page 2

11.

发明授权
Machine learning model trained to predict conversions for determining lost conversions caused by restrictions in available fulfillment windows or fulfillment cost 有权

公开(公告)号：US12008590B2

公开(公告)日：2024-06-11

申请号：US18149646

申请日：2023-01-03

Applicant: Maplebear Inc.

Inventor： Wa Yuan , Ganesh Krishnan , Qianyi Hu , Aishwarya Balachander , George Ruan , Soren Zeliger , Mike Freimer , Aman Jain

IPC: G06Q30/02 , G06N3/084 , G06Q10/0631 , G06Q10/087 , G06Q30/0201 , G06Q30/0202 , G06Q30/0601

CPC classification number: G06Q30/0202 , G06N3/084 , G06Q10/06312 , G06Q10/087 , G06Q30/0201 , G06Q30/0607 , G06Q30/0633

Abstract: An online concierge system trains a machine learning conversion model that predicts a probability of receiving an order from a user when the user accesses the online concierge system. The conversion model predicts the probability of receiving the order based on a set of input features that include price and availability information. For each access to the online concierge system, the online concierge system applies the conversion model to a current price and availability and to an optimal price availability. The online concierge system generates a metric as the difference between the two predicted probabilities of receiving an order.

12.

发明公开
DETERMINING ESTIMATED DELIVERY TIME OF ITEMS OBTAINED FROM A WAREHOUSE FOR USERS OF AN ONLINE CONCIERGE SYSTEM TO REDUCE PROBABILITIES OF DELIVERY AFTER THE ESTIMATED DELIVERY TIME 审中-公开

公开(公告)号：US20230394420A1

公开(公告)日：2023-12-07

申请号：US18235309

申请日：2023-08-17

Applicant: Maplebear Inc.

Inventor： Zi Wang , Ji Chen , Houtao Deng , Soren Zeliger , Yijia Chen

IPC: G06Q10/0833

CPC classification number: G06Q10/0833 , G06Q10/087

Abstract: An online concierge system displays an interface to a user identifying an estimated time of arrival for an order. To generate the estimated time of arrival for the order, the online concierge system trains a prediction engine to predict delivery time based on a predicted selection time for a shopper to select the order for fulfillment and predicted travel time for the shopper to deliver items of the order to a location identified by the order. The online concierge system generates a policy optimization model that computes an adjustment for the predicted delivery time. The adjustment is determined by solving a stochastic optimization problem with a constraint on a probability of the order being delivered after the estimated time of arrival. The predicted delivery time combined with the adjustment determines the estimated time of delivery displayed to the user to balance between minimizing late deliveries and wait times.

13.

发明公开
PREDICTING SHOPPER SUPPLY DURING A TIME INTERVAL BASED ON INTERACTIONS BY SHOPPERS WITH A SHOPPER ASSIGNMENT APPLICATION DURING EARLIER TIME INTERVALS 审中-公开

公开(公告)号：US20230351279A1

公开(公告)日：2023-11-02

申请号：US17731810

申请日：2022-04-28

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Soren Zeliger , Aman Jain , Zhaoyu Kou , Ji Chen , Trace Levinson , Ganesh Krishnan

IPC: G06Q10/06

CPC classification number: G06Q10/063116 , G06Q10/04

Abstract: An online concierge system assigns shoppers to fulfill orders from users. To allocate shoppers, the online concierge system predicts future supply and demand for the shoppers' services for different time windows. To forecast a supply of shoppers, the online concierge system trains a machine learning model that estimates future supply based on access to a shopper mobile application through which the shoppers obtain new assignments by shoppers. The online concierge system also forecasts future orders. The online concierge system estimates a supply gap in a future time period by selecting a target time to accept for shoppers to accept orders and determining a corresponding ratio of number of shoppers and number of orders. The online concierge system may adjust a number of shoppers allocated to the future time period to achieve the determined ratio number of shoppers and number of orders.

14.

发明公开
MACHINE LEARNING MODEL TRAINED TO PREDICT CONVERSIONS FOR DETERMINING LOST CONVERSIONS CAUSED BY RESTRICTIONS IN AVAILABLE FULFILLMENT WINDOWS OR FULFILLMENT COST 审中-公开

公开(公告)号：US20230153847A1

公开(公告)日：2023-05-18

申请号：US18149646

申请日：2023-01-03

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Wa Yuan , Ganesh Krishnan , Qianyi Hu , Aishwarya Balachander , George Ruan , Soren Zeliger , Mike Freimer , Aman Jain

IPC: G06Q30/0202 , G06N3/084 , G06Q30/0201 , G06Q30/0601 , G06Q10/087 , G06Q10/0631

CPC classification number: G06Q30/0202 , G06N3/084 , G06Q30/0201 , G06Q30/0633 , G06Q10/087 , G06Q30/0607 , G06Q10/06312

Abstract: An online concierge system trains a machine learning conversion model that predicts a probability of receiving an order from a user when the user accesses the online concierge system. The conversion model predicts the probability of receiving the order based on a set of input features that include price and availability information. For each access to the online concierge system, the online concierge system applies the conversion model to a current price and availability and to an optimal price availability. The online concierge system generates a metric as the difference between the two predicted probabilities of receiving an order.

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