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    DISPLAYING A THREE-DIMENSIONAL IMAGE OF AN ITEM TO A USER OF AN ONLINE CONCIERGE SYSTEM DURING ORDER FULFILLMENT
    221.
    发明公开
    DISPLAYING A THREE-DIMENSIONAL IMAGE OF AN ITEM TO A USER OF AN ONLINE CONCIERGE SYSTEM DURING ORDER FULFILLMENT 审中-公开

    公开(公告)号：US20230419392A1

    公开(公告)日：2023-12-28

    申请号：US17846698

    申请日：2022-06-22

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Dylan Wang

    IPC: G06Q30/06 ,  G06Q10/08

    CPC classification number: G06Q30/0643 ,  G06Q30/0623 ,  G06Q10/087

    Abstract: An online concierge system receives multiple images of an item from a first client device associated with a shopper associated with the online concierge system, in which each of the images of the item is captured from a different angle and/or position and the item is included among an inventory of a warehouse associated with a retailer associated with the online concierge system. Based in part on the images of the item, the online concierge system generates a three-dimensional image of the item, in which the three-dimensional image of the item includes a dimension of the item and/or a color of the item. The online concierge system then sends the three-dimensional image of the item to a second client device associated with a customer of the online concierge system, in which a perspective of the three-dimensional image is modifiable within a display area of the second client device.

    MACHINE LEARNED MODEL FOR MANAGING FOUNDATIONAL ITEMS IN CONCIERGE SYSTEM
    222.
    发明公开
    MACHINE LEARNED MODEL FOR MANAGING FOUNDATIONAL ITEMS IN CONCIERGE SYSTEM 审中-公开

    公开(公告)号：US20230419381A1

    公开(公告)日：2023-12-28

    申请号：US17846887

    申请日：2022-06-22

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Leho Nigul ,  Shaun Navin Maharaj ,  Brent Scheibelhut

    IPC: G06Q30/06

    CPC classification number: G06Q30/0613

    Abstract: An online concierge system receives, from a client device comprising a customer mobile application, an order comprising a list of one or more items for delivery to a destination location from a warehouse. The customer mobile application comprises a user interface. The online concierge system identifies a set of item groupings from a database that match the list of one or more items. The online concierge system applies the order and the set of item groupings to a machine learning model to produce a set of foundational items. The online concierge system sends for display, to the client device, an updated user interface comprising a foundational items graphical element that visually distinguishes the set of foundational items from other items in the list of one or more items.

    GENERATING DATASTORE CHECKPOINTS
    223.
    发明公开
    GENERATING DATASTORE CHECKPOINTS 审中-公开

    公开(公告)号：US20230401186A1

    公开(公告)日：2023-12-14

    申请号：US17840454

    申请日：2022-06-14

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Jacob Jensen

    IPC: G06F16/22 ,  G06F16/2458

    CPC classification number: G06F16/22 ,  G06F16/2477

    Abstract: The present disclosure is directed to generating datastore checkpoints. In particular, the methods and systems of the present disclosure may generate, within a datastore, data representing multiple checkpoints. Each checkpoint of the checkpoints may correspond to a respective record of the datastore and may represent a common shared value for a field based at least in part on which the datastore is ordered. Based at least in part on the checkpoints, the datastore may be queried to produce one or more responsive records to one or more criteria of the query. Based at least in part on the responsive record(s), training data may be generated. The training data may be utilized for training one or more machine learning (ML) models configured to process input based at least in part on values for the field based at least in part on which the datastore is ordered.

    TREATMENT LIFT SCORE AGGREGATION FOR NEW TREATMENT TYPES
    224.
    发明公开
    TREATMENT LIFT SCORE AGGREGATION FOR NEW TREATMENT TYPES 审中-公开

    公开(公告)号：US20230368236A1

    公开(公告)日：2023-11-16

    申请号：US17744526

    申请日：2022-05-13

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Tilman Drerup ,  Anne Moxie ,  Sophia Li ,  Vibin Kundukulam ,  Jonathan Gu ,  Ashley Denney

    IPC: G06Q30/02 ,  G06Q30/06

    CPC classification number: G06Q30/0211 ,  G06Q30/0239 ,  G06Q30/0617

    Abstract: An online concierge system uses a new treatment engine to score users for applying treatments of a new treatment type. The new treatment engine uses treatment models to generate treatment lift scores for the user. The new treatment engine applies an aggregation function model to the treatment lift scores to generate an aggregated lift score for the user. If the aggregated lift score exceeds a threshold, the new treatment engine applies a treatment of the new treatment type to the user. The new treatment engine trains the aggregation function model based on training examples used to train the treatment models. For a training example associated with a particular treatment type, the new treatment engine uses a target lift score generated by the treatment model for the treatment type to evaluate the performance of the aggregation function model, and to update the aggregation function model accordingly.

    VALIDATION OF ITEM UPDATES USING MACHINE LEARNING TO SAMPLE DATA
    225.
    发明公开
    VALIDATION OF ITEM UPDATES USING MACHINE LEARNING TO SAMPLE DATA 审中-公开

    公开(公告)号：US20230359901A1

    公开(公告)日：2023-11-09

    申请号：US18306556

    申请日：2023-04-25

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Syed Wasi Hasan Rizvi ,  Charles Wesley

    IPC: G06N3/09

    CPC classification number: G06N3/09

    Abstract: An online system validates item updates using a machine-learning model to identify item updates that need independent review. The online system maintains an item database that has item entries for items on the online system. The online system receives item updates from an item update system and applies an error prediction model to the item updates to generate an error likelihood score for each item update. The online system samples a subset of the item updates based on the error likelihood scores and passes these sampled item updates to a human reviewer system. The human reviewer system labels each of the sampled item updates with an error label indicating whether the corresponding item update is actually erroneous. The online system determines whether to update the item database with the full set of received item updates based on the error labels.

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