中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

8 records (took 0.023 seconds)

    REPLACING AN UNAVAILABLE ITEM IN AN ORDER USING A TRAINED OUTCOME PREDICTION MODEL
    5.
    发明申请
    REPLACING AN UNAVAILABLE ITEM IN AN ORDER USING A TRAINED OUTCOME PREDICTION MODEL 有权

    公开(公告)号：US20250124485A1

    公开(公告)日：2025-04-17

    申请号：US18485797

    申请日：2023-10-12

    Applicant: Maplebear Inc.

    Inventor： Benjamin Knight ,  Saumitra Maheshwari ,  Jennie Braunstein ,  Darren Johnson ,  Kenneth Jason Sanchez ,  Christopher Billman

    IPC: G06Q30/0601 ,  G06N20/00

    Abstract: An online system receives orders from users and dispatches pickers to fulfill the orders by obtaining ordered items at a retailer. If an ordered item cannot be found by a picker, the picker may refund the item or attempt to find a replacement item. While obtaining a replacement item may increase revenue to the online system, it can also cause a bad outcome for user experience (e.g., an unacceptable replacement item, a refund request of the replacement item, etc.). To balance these interests, the online system trains a model to predict an outcome metric comprising a likelihood of a bad outcome from replacing an item or an expected amount of profit to the online system from a replacement item. The online system compares the outcome metric to a threshold to determine whether to promote or dissuade the picker from replacing a not-found item.

    DETERMINING EFFICIENT ROUTES IN A COMPLEX SPACE USING HIERARCHICAL INFORMATION AND SPARSE DATA
    8.
    发明公开
    DETERMINING EFFICIENT ROUTES IN A COMPLEX SPACE USING HIERARCHICAL INFORMATION AND SPARSE DATA 审中-公开

    公开(公告)号：US20240005269A1

    公开(公告)日：2024-01-04

    申请号：US17855793

    申请日：2022-07-01

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Benjamin Knight ,  Qi Xi ,  Saumitra Maheshwari ,  Salmaan Ayaz

    IPC: G06Q10/08 ,  G06Q30/06

    CPC classification number: G06Q10/087 ,  G06Q30/0633

    Abstract: An online system performs a method. The method comprises obtaining historical pick data for items located in a warehouse, including data for each of the items picked and pick times between each of the items picked, and determining a taxonomy of items offered by the warehouse. The taxonomy identifies a plurality of product categories structured in a hierarchy, wherein each level of the hierarchy corresponds to a particular level of granularity of product data. The method further comprises applying the historical pick data to a machine learning model to generate pairwise relations between product categories at each level of the taxonomy and generating sequences of product categories based on the pairwise relations. An order for items offered by the warehouse is received and compared to the sequences for each level to generate a pick sequence for picking the items efficiently, which is outputted by the system to a mobile application.

Patent Agency Ranking