公开(公告)号：US20240020751A1

公开(公告)日：2024-01-18

申请号：US18473789

申请日：2023-09-25

Applicant: Maplebear Inc.

Inventor： Jagannath Putrevu ,  Zi Wang ,  Site Wang ,  Houtao Deng ,  Yijia Chen ,  Mingzhe Zhuang ,  Ji Chen ,  Deepak Tirumalasetty

IPC: G06Q30/0601 ,  G06Q10/0631 ,  G06Q10/087 ,  G06Q30/0283

CPC classification number: G06Q30/0635 ,  G06Q10/06315 ,  G06Q10/087 ,  G06Q30/0284 ,  G06Q10/06313

Abstract: An online concierge system receives two types of orders, one of which requires fulfillment in a specific time interval, while the other can be fulfilled anytime up to a specific time interval. A machine learning model, trained on historical data about available shoppers in discrete time intervals, is used to predict how many shoppers will be available to fulfill orders in each time interval. For each time interval, the system retrieves the relevant orders of both types and creates candidate groups including orders of both types. For each group, the system determines a fulfillment cost based on items in the orders. The candidate group with the lowest cost is selected, and the orders in the selected group are sent to devices of available shoppers in that interval, prompting the shoppers to view and fulfill the orders.

公开(公告)号：US11755987B2

公开(公告)日：2023-09-12

申请号：US17359486

申请日：2021-06-25

Applicant: Maplebear Inc.

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

IPC: G06Q10/0833 ,  G06Q10/087

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.

公开(公告)号：US20230196442A1

公开(公告)日：2023-06-22

申请号：US17556936

申请日：2021-12-20

Applicant: Maplebear Inc. (dba Instacart)

Inventor： Trace Levinson ,  Aman Jain ,  Ji Chen ,  Andrew Kephart

IPC: G06Q30/06

CPC classification number: G06Q30/0635

Abstract: An online concierge system allocates shoppers to different geographic regions at different times to fulfill orders received from users. The online concierge system uses different methods for adjusting allocation of shoppers to geographic regions, such as obtaining new shoppers or providing incentives to additional shoppers, based on estimated numbers of orders identifying different geographic regions. To account for costs to the online concierge system for allocating shoppers to geographic regions, the online concierge system trains multiple machine learned models to predict different efficiency metrics for methods for adjusting shopper allocation. Discrete samples are obtained from each efficiency metric, and samples that do not satisfy one or more constraints removed. From the remaining samples, a combination of samples for different methods for adjusting shopper allocation is selected to optimize a value to the online concierge system based on one or more criteria.

公开(公告)号：US20220358443A1

公开(公告)日：2022-11-10

申请号：US17308996

申请日：2021-05-05

Applicant: Maplebear, Inc.(dba Instacart)

Inventor： Houtao Deng ,  Ji Chen ,  Chris Sun ,  Yile Liu ,  Yijia Chen

IPC: G06Q10/06 ,  G06Q10/08

Abstract: An online concierge system allows users to order items within discrete time intervals later than a time when an order was received. Each order may require a different set of characteristics for fulfilment by shoppers. Because different shoppers may have different capabilities, it is most efficient to reserve shoppers with specialized characteristics for orders that require them. The online concierge system maintains a set of hierarchical structures for different characteristics of shoppers, with each level in a hierarchical structure having a value. To select a shopper to fulfill an order, the online concierge system scores identifies groups of shoppers having characteristics capable of fulfilling the order based on levels in the hierarchical structure for each characteristic of a group. A shopper from a group having a minimum score is selected to fulfill the order.

公开(公告)号：US20240070697A1

公开(公告)日：2024-02-29

申请号：US18503078

申请日：2023-11-06

Applicant: Maplebear Inc.

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

IPC: G06Q30/0201 ,  G06N5/04 ,  G06N20/00 ,  G06Q10/0631 ,  G06Q30/0204 ,  G06Q30/0601

CPC classification number: G06Q30/0206 ,  G06N5/04 ,  G06N20/00 ,  G06Q10/06312 ,  G06Q10/06313 ,  G06Q10/06315 ,  G06Q30/0205 ,  G06Q30/0635 ,  G06Q30/0641

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：US20240037467A1

公开(公告)日：2024-02-01

申请号：US18485554

申请日：2023-10-12

Applicant: Maplebear Inc.

Inventor： Houtao Deng ,  Ji Chen ,  Christopher Shey-Tau Sun ,  Yile Liu ,  Yijia Chen

IPC: G06Q10/0631 ,  G06Q10/0875

CPC classification number: G06Q10/063112 ,  G06Q10/0875 ,  G06Q10/06315

Abstract: An online concierge system maintains a data store with discrete time intervals for fulfilling orders and information on shoppers categorized in a tree structure based on their characteristics. When an order is received from a user's client device, the system employs a machine learning model trained on historical data to estimate the number of available shoppers and their corresponding levels in the tree structure. The order is tagged based on the items it contains, and these tags are matched to the levels of capable shoppers. Shoppers are grouped and scored based on their levels, and a group with the minimum score and capability to fulfill the order is selected. A shopper from this group is then dispatched for order fulfillment, leading to a decrement in the estimated number of shoppers in the selected group. This approach optimizes the allocation of shoppers to orders, ensuring efficient and timely fulfillment.

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：US20220292580A1

公开(公告)日：2022-09-15

申请号：US17202190

申请日：2021-03-15

Applicant: Maplebear, Inc.(dba Instacart)

Inventor： Jagannath Putrevu ,  Zi Wang ,  Site Wang ,  Houtao Deng ,  Yijia Chen ,  Mingzhe Zhuang ,  Ji Chen ,  Deepak Tirumalasetty

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

Abstract: An online concierge system allows users to order items within discrete time intervals later than a time when an order was received. The online concierge system allocates a specified percentage of an estimated number of shoppers during a discrete time interval to fulfilling orders received before the discrete time interval. An order may include a flag authorizing flexible fulfillment of the order along with a discrete time interval, which allows the order to be fulfilled earlier than the identified discrete time interval. The online concierge system generates groups of multiple orders authorizing flexible fulfillment and determines a cost for fulfilling different groups of orders. The online concierge system identifies a group of orders authorizing flexible fulfillment having a minimum cost for fulfillment by a shopper, allowing for more allocation of shoppers by enabling grouping of orders identifying different discrete time intervals.