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    REPLACING AN UNAVAILABLE ITEM IN AN ORDER USING A TRAINED OUTCOME PREDICTION MODEL
    1.
    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.

    OPTICAL SCANNING USING RECEIPT IMAGERY FOR AUTOMATED TAX RECONCILIATION
    4.
    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.

    TEXT-BASED REPRESENTATIONS OF LOCATION DATA FOR LARGE LANGUAGE MODEL-BASED ITEM IDENTIFICATION
    6.
    发明申请
    TEXT-BASED REPRESENTATIONS OF LOCATION DATA FOR LARGE LANGUAGE MODEL-BASED ITEM IDENTIFICATION 有权

    公开(公告)号：US20250124238A1

    公开(公告)日：2025-04-17

    申请号：US18912395

    申请日：2024-10-10

    Applicant: Maplebear Inc.

    Inventor： Benjamin Knight ,  Kenneth Jason Sanchez ,  Matthew Negrin ,  Licheng Yin ,  Christopher Billman ,  Rebecca Riso

    IPC: G06F40/40 ,  G06F40/205

    Abstract: An online system generates text-based representations of various types of data for processing using a large language model. The online system extracts location data from a map of a source location and converts the location data into a text-based representation of the location data. The online system receives a set of item identifiers from a client device of a user and generates an LLM prompt based on the set of item identifiers and the text-based representations of the location data. The online system receives a response from the LLM and parses the response for a text-based description of related items. The online system maps the text-based description of the related items to item identifiers and transmits a notification to the client device that includes item data associated with the related items.

    ANOMALY DETECTION AND USER ATTRIBUTION USING MACHINE-LEARNING LARGE LANGUAGE MODELS
    7.
    发明申请
    ANOMALY DETECTION AND USER ATTRIBUTION USING MACHINE-LEARNING LARGE LANGUAGE MODELS 有权

    公开(公告)号：US20250086435A1

    公开(公告)日：2025-03-13

    申请号：US18885294

    申请日：2024-09-13

    Applicant: Maplebear Inc.

    Inventor： Benjamin Knight ,  Kenneth Jason Sanchez ,  Christopher Billman ,  Rebecca Riso ,  Matthew Negrin ,  Licheng Yin

    IPC: G06N3/0455 ,  G06N3/09 ,  G06Q10/087

    Abstract: An online system detects an anomaly associated with an item selection made by a picker for fulfilling an order of a user of an online system. The system generates a prompt for execution by a machine-learned model trained as a large language model. The prompt comprises a chat log between the picker and the user. The system provides the prompt to the machine-learned model for execution. The system receives, as output from the machine-learned model and based on the chat log, a description indicating whether the anomaly is attributable to the user. The system determines, based on the output from the machine-learned model, that the item selection is not attributable to the user. Responsive to determining that the item selection is not attributable to the user, the system provides a notification to a client device of the user to confirm whether the item selection is approved by the user.

    DATABASE SEARCH BASED ON MACHINE LEARNING BASED LANGUAGE MODELS
    10.
    发明申请
    DATABASE SEARCH BASED ON MACHINE LEARNING BASED LANGUAGE MODELS 有权

    公开(公告)号：US20250147954A1

    公开(公告)日：2025-05-08

    申请号：US18936854

    申请日：2024-11-04

    Applicant: Maplebear Inc.

    Inventor： Christopher Billman ,  Benjamin Knight ,  Kenneth Jason Sanchez ,  Matthew Negrin ,  Licheng Yin ,  Rebecca Riso

    IPC: G06F16/242 ,  G06F16/2455

    Abstract: An online system receives information describing a set of items requested by a user and an indication via a chat interface that a particular item needs replacement. The online system generates one or more prompts configured to request a machine learned language model to identify the particular item that needs replacement and to identify one or more replacement items for the particular item. The online system receives a set of item identifiers from the machine learned language model and selects a replacement item from a database based on the set of item identifiers. The online system may also receive an order and a communication history associated with a user including a message with a request to modify the a. The online uses the machine-learning language model to map the request type to the set of API requests for updating the order to reflect the request from the user.

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