公开(公告)号：US20230049669A1

公开(公告)日：2023-02-16

申请号：US17403400

申请日：2021-08-16

Applicant: Maplebear Inc. (dba Instacart)

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

IPC: G06Q30/02 ,  G06N3/08 ,  G06Q10/06 ,  G06Q10/08 ,  G06Q30/06

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.

公开(公告)号：US20220414592A1

公开(公告)日：2022-12-29

申请号：US17359486

申请日：2021-06-25

Applicant: Maplebear Inc.(dba Instacart)

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

IPC: G06Q10/08

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.

公开(公告)号：US20230034221A1

公开(公告)日：2023-02-02

申请号：US17389281

申请日：2021-07-29

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Houtao Deng ,  Ji Chen ,  Zi Wang ,  Soren Zeliger ,  Ganesh Krishnan ,  Wa Yuan ,  Michael Scheibe

IPC: G06Q30/02 ,  G06Q30/06 ,  G06Q10/06 ,  G06N20/00 ,  G06N5/04

Abstract: An online concierge system allows users to order items within discrete time intervals later than a time when an order was received or for short-term fulfillment when the order was received. To account for a number of shoppers available to fulfill orders during different discrete time intervals and numbers of orders for fulfillment during different discrete time intervals, the online concierge system specifies a target rate for orders fulfilled later than a specified discrete time interval and a threshold from the target rate. A trained machine learning model periodically predicts a percentage of orders being fulfilled late, with an order associated with a predicted percentage when the order was received. The online concierge system increases a price of orders associated with predicted percentages greater than the threshold from the target rate. The increased price of an order is determined from a price elasticity curve and the predicted percentage.

公开(公告)号：US20240104458A1

公开(公告)日：2024-03-28

申请号：US17955407

申请日：2022-09-28

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Wa Yuan ,  Jae Cho ,  Yijia Chen ,  Houtao Deng ,  Soren Zeliger ,  Aman Jain ,  Jian Wang ,  Ji Chen

IPC: G06Q10/06 ,  G06N5/02 ,  G06Q30/06

CPC classification number: G06Q10/063116 ,  G06N5/022 ,  G06Q10/06393 ,  G06Q30/0637

Abstract: An online concierge system determines a quantity of a resource available in a timeslot to fulfill orders during the timeslot. The orders include immediate orders placed during the timeslot and scheduled orders that are scheduled for fulfillment during the timeslot. The online concierge system applies the quantity of the resource to a machine learning model to produce a predicted relationship between a value of a fulfillment metric and an allocation of the quantity of the resource reserved for immediate orders. The online concierge system determines, based on the predicted relationship, an expected optimal allocation of the quantity of the resource that maximizes the fulfillment metric. The online concierge system reserves the expected optimal allocation of the quantity of the resource for immediate orders.

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.

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.