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    Automation engine using a hyperparameter learning model to optimize sub-systems of an online system
    1.
    发明授权
    Automation engine using a hyperparameter learning model to optimize sub-systems of an online system 有权

    公开(公告)号：US12271939B2

    公开(公告)日：2025-04-08

    申请号：US17855788

    申请日：2022-06-30

    Applicant: Maplebear Inc.

    Inventor： Sonali Deepak Chhabria ,  Xiangyu Wang ,  Aman Jain ,  Ganesh Krishnan ,  Trace Levinson ,  Jian Wang

    IPC: G06Q30/0601 ,  G01C21/34 ,  G06Q10/0631 ,  G06Q10/0637 ,  G06Q10/087

    Abstract: An online concierge system includes a marketplace automation engine for setting various control parameters affecting marketplace operation. The marketplace automation engine applies a hyperparameter learning model to the marketplace state data to predict a set of hyperparameters affecting a set of respective parameterized control decision models. The hyperparameter learning model is trained on historical marketplace state data and a configured outcome objective for the online concierge system. The marketplace automation engine independently applies the set of parameterized control decision models to the marketplace state data using the hyperparameters to generate a respective set of control parameters affecting marketplace operation of the online concierge system. The marketplace automation engine applies the respective set of control parameters to operation of the online concierge system.

    Using Computer Models to Predict Delivery Times for an Order During Creation of the Order
    3.
    发明申请
    Using Computer Models to Predict Delivery Times for an Order During Creation of the Order 有权

    公开(公告)号：US20250104003A1

    公开(公告)日：2025-03-27

    申请号：US18475766

    申请日：2023-09-27

    Applicant: Maplebear Inc.

    Inventor： Shuai Wang ,  Zi Wang ,  Liang Chen ,  Aman Jain ,  Xiangyu Wang ,  Jian Wang

    IPC: G06Q10/0834 ,  G06Q30/0601

    Abstract: One or more trained computer models are used to determine, at different stages of an order, an estimated time range for delivery of the order at an online system. The online system retrieves a set of candidate ranges of delivery times for the order. The online system applies the one or more computer models trained to predict a value of a metric for each candidate range in the set of candidate ranges, based on one or more features associated with the order. The online system selects a range of delivery times for the order from the set of candidate ranges, based on the predicted value of the metric for each candidate range. The online system causes a device of the user to display a user interface with the selected range of delivery times for the order.

    Generating a range of estimated fulfillment times for an order based on characteristics of an order
    4.
    发明授权
    Generating a range of estimated fulfillment times for an order based on characteristics of an order 有权

    公开(公告)号：US12033109B1

    公开(公告)日：2024-07-09

    申请号：US18114858

    申请日：2023-02-27

    Applicant: Maplebear Inc.

    Inventor： Shuai Wang ,  Zi Wang ,  Liang Chen ,  Houtao Deng ,  Xiangyu Wang ,  Aman Jain ,  Jian Wang

    IPC: G06Q10/0835 ,  G06Q10/0833

    CPC classification number: G06Q10/0835 ,  G06Q10/0833

    Abstract: An online concierge system delivers items from retailers to customers. The online concierge predicts a range of times during which an order may be fulfilled for presentation to a user. The online concierge system uses a trained maximum time prediction model to determine a maximum time for order fulfillment based on an order. A trained minimum time prediction model determines a minimum time for order fulfillment from the order and the maximum time. The minimum time may account for one or more rules (e.g., a percentage of orders fulfilled before the minimum time, a desired rate of selection of a range including the minimum time). A range bounded by the maximum time and the minimum time is transmitted to a customer to enable the customer to select a time interval for order fulfillment.

    USER INTERFACE ENABLING ORDER FULFILLMENT OPTIONS BASED ON PREDICTED FULFILLMENT TIMES FROM A TRAINED MODEL
    5.
    发明公开
    USER INTERFACE ENABLING ORDER FULFILLMENT OPTIONS BASED ON PREDICTED FULFILLMENT TIMES FROM A TRAINED MODEL 审中-公开

    公开(公告)号：US20240330852A1

    公开(公告)日：2024-10-03

    申请号：US18616724

    申请日：2024-03-26

    Applicant: Maplebear Inc.

    Inventor： Yueyi Sun ,  Zi Wang ,  Houtao Deng ,  Aman Jain ,  Jian Wang

    IPC: G06Q10/087 ,  G06Q10/04 ,  G06Q10/083

    CPC classification number: G06Q10/087 ,  G06Q10/04 ,  G06Q10/083

    Abstract: An online concierge system receives an order from a user including items to obtain from a retailer for delivery to a location. A picker selects the order and obtains items from the retailer. The user selects a time interval during which items from the order are delivered to the location. To prevent the user from selecting a time interval for fulfillment the online concierge system prevents the user from selecting a time interval when a picker may be unable to obtain the items from the retailer before a closing time of the retailer. The online concierge system evaluates time intervals by subtracting a travel time for the picker travelling from the retailer to the location from a predicted fulfillment time for the order. This prevents the time for delivering items after being obtained from affecting whether a time interval may be selected.

    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.

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