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    MACHINE LEARNING BASED RESOURCE ALLOCATION OPTIMIZATION
    6.
    发明公开
    MACHINE LEARNING BASED RESOURCE ALLOCATION OPTIMIZATION 审中-公开

    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.

    SELECTIVELY PROVIDING MACHINE LEARNING MODEL-BASED SERVICES
    7.
    发明公开
    SELECTIVELY PROVIDING MACHINE LEARNING MODEL-BASED SERVICES 审中-公开

    公开(公告)号：US20240070605A1

    公开(公告)日：2024-02-29

    申请号：US17897045

    申请日：2022-08-26

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Shuai Wang ,  Zi Wang ,  Ganesh Krishnan ,  Houtao Deng ,  Aman Jain ,  Jian Wang

    IPC: G06Q10/08 ,  G06N5/02 ,  G06Q10/06 ,  G06Q30/06

    CPC classification number: G06Q10/0838 ,  G06N5/022 ,  G06Q10/06393 ,  G06Q30/0617

    Abstract: An online concierge system provides arrival prediction services for a user placing an order to be retrieved by a shopper. An order may have a predicted arrival time predicted by a model that may err under some conditions. To reduce the likelihood of providing the predicted arrival time (and related services) when the arrival time may be incorrect, the prediction model and related services are throttled (e.g., selectively provided) based on one or more predicted delivery metrics, which may include a time to accept the order by a shopper and a predicted portion of late orders that will be delivered past the respective predicted arrival times. The predicted delivery metrics are compared with thresholds and the result of the comparison used to selectively provide, or not provide, the predicted delivery services.

    SELECTING METHODS TO ALLOCATE SHOPPERS FOR ORDER FULFILLMENT IN GEOGRAPHIC REGIONS BY AN ONLINE CONCIERGE SYSTEM BASED ON MACHINE-LEARNED EFFICIENCIES FOR DIFFERENT ALLOCATION METHODS
    8.
    发明公开
    SELECTING METHODS TO ALLOCATE SHOPPERS FOR ORDER FULFILLMENT IN GEOGRAPHIC REGIONS BY AN ONLINE CONCIERGE SYSTEM BASED ON MACHINE-LEARNED EFFICIENCIES FOR DIFFERENT ALLOCATION METHODS 审中-公开

    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.

    USER INTERFACE FOR SHOWING AVAILABILITY OF ORDERS FOR CONCIERGE SHOPPING SERVICE
    9.
    发明申请
    USER INTERFACE FOR SHOWING AVAILABILITY OF ORDERS FOR CONCIERGE SHOPPING SERVICE 有权

    公开(公告)号：US20220343395A1

    公开(公告)日：2022-10-27

    申请号：US17238217

    申请日：2021-04-23

    Applicant: Maplebear, Inc.(dba Instacart)

    Inventor： Amy Luong ,  Michael Righi ,  Graham Adeson ,  Ross Stuart Williams ,  Aman Jain ,  Radhika Anand ,  Ganesh Krishnan

    IPC: G06Q30/06

    Abstract: For each retailer in the geographic region, an online system predicts a number of orders placed at the retailer and a capacity to fulfill orders during a forecast time period. The capacity of the retailer is predicted based on a number of pickers expected to be available to the retailer during the forecast time period. The online system determines demand for the services of a picker at the retailer based on a comparison of the predicted number of orders and the predicted capacity to fulfill those orders. The online system displays a user interactive map of the geographic region to the picker. The map displays a pin at the location of each retailer in the geographic region, which describes the categorization determined for the retailer. The picker selects a pin, which causes the user interactive map to display a notification characterizing the demand for services at the retailer.

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