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    INFERRING ATTRIBUTION OF TARGET ACTIONS FOR RECIPE SUGGESTIONS
    131.
    发明公开
    INFERRING ATTRIBUTION OF TARGET ACTIONS FOR RECIPE SUGGESTIONS 审中-公开

    公开(公告)号：US20230377020A1

    公开(公告)日：2023-11-23

    申请号：US17751521

    申请日：2022-05-23

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Nathan Bauer

    IPC: G06Q30/06 ,  G06Q20/20

    CPC classification number: G06Q30/0631 ,  G06Q30/0641 ,  G06Q20/20

    Abstract: An automated checkout system automatically establishes sessions between users and shopping carts by correlating action events with distances of the user's client device to the shopping cart. The automated checkout system determines the client device's distance from the shopping cart at timestamps when an action event occurs with respect to the shopping cart. If the distances and the action events are correlated, the system establishes a session between the user and the shopping cart. Additionally, the automated checkout system attributes target actions to recipe suggestions. The automated checkout system displays a recipe suggestion to a user on a display of a shopping cart, and identifies an item added to the shopping cart. If the added item matches an item in the set of recipes, the automated checkout system applies an attribution model that determines whether to attribute a target action that relates to the item with the recipe suggestion.

    OPTICAL SCANNING USING RECEIPT IMAGERY FOR AUTOMATED TAX RECONCILIATION
    133.
    发明公开
    OPTICAL SCANNING USING RECEIPT IMAGERY FOR AUTOMATED TAX RECONCILIATION 审中-公开

    公开(公告)号：US20230316350A1

    公开(公告)日：2023-10-05

    申请号：US17853619

    申请日：2022-06-29

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Benjamin Knight ,  Benjamin Peyrot ,  Djordje Gluhovic ,  Rohit Turumella ,  Alice Han

    IPC: G06Q30/04 ,  G06Q20/20 ,  G06Q40/00

    CPC classification number: G06Q30/04 ,  G06Q20/207 ,  G06Q40/10

    Abstract: An online concierge system requests an image of a receipt of an order from a picker after the picker fulfills the order at a store. The online concierge system performs image processing on the image of the receipt and uses machine learning and optical character recognition to determine a tax amount paid for the order and a confidence score associated with the tax amount. The online concierge system may use the machine learning model for segmenting extracted text in the image of the receipt into tokens. The online concierge system may then determine at least one token associated with a tax item and the tax amount associated with the tax item. The online concierge system communicates the tax amount to the store for reimbursement based on the tax amount and the confidence score.

    Machine-learned model for optimizing selection sequence for items in a warehouse
    134.
    发明授权
    Machine-learned model for optimizing selection sequence for items in a warehouse 有权

    公开(公告)号：US11775926B2

    公开(公告)日：2023-10-03

    申请号：US15882934

    申请日：2018-01-29

    Applicant: Maplebear, Inc.

    Inventor： Jeremy Stanley ,  Montana Low ,  Nima Zahedi

    IPC: G06Q10/087 ,  G06N3/08 ,  G06N3/04 ,  G06N20/20 ,  G06N5/01

    CPC classification number: G06Q10/087 ,  G06N3/04 ,  G06N3/08 ,  G06N5/01 ,  G06N20/20

    Abstract: An online shopping concierge system sorts a list of items to be picked in a warehouse by receiving data identifying a warehouse and items to be picked by a picker in the warehouse. The system retrieves a machine-learned model that predicts a next item of a picking sequence of items. The model was trained, using machine-learning, based on sets of data that each include a list of picked items, an identification of a warehouse from which the items were picked, and a sequence in which the items were picked. The system identifies an item to pick first and a plurality of remaining items. The system predicts, using the model, a next item to be picked based on the remaining items, the first item, and the warehouse. The system transmits data identifying the first item and the predicted next item to be picked to the picker in the warehouse.

    DOMAIN-ADAPTIVE CONTENT SUGGESTION FOR AN ONLINE CONCIERGE SYSTEM
    139.
    发明公开
    DOMAIN-ADAPTIVE CONTENT SUGGESTION FOR AN ONLINE CONCIERGE SYSTEM 审中-公开

    公开(公告)号：US20230186361A1

    公开(公告)日：2023-06-15

    申请号：US17550960

    申请日：2021-12-14

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Saurav Manchanda ,  Ramasubramanian Balasubramanian

    IPC: G06Q30/06 ,  G06Q30/02

    CPC classification number: G06Q30/0619 ,  G06Q30/0282 ,  G06Q30/0641

    Abstract: An online concierge system uses a domain-adaptive suggestion module to score products that may be presented to a user as suggestions in response to a user’s search query. The domain-adaptive suggestion module receives data that is relevant to scoring products as suggestions in response to a search query. The domain-adaptive suggestion module uses one or more domain-neutral representation models to generate a domain-neutral representation of the received data. The domain-neutral representation is a featurized representation of the received data that can be used by machine-learning models in the search domain or the suggestion domain. The domain-adaptive suggestion module then scores products by applying one or more machine-learning models to domain-neutral representations generated based on those products. By using domain-neutral representations, the domain-adaptive suggestion module can be trained based on training examples from a similar prediction task in a different domain.

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