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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.

    DATABASE SEARCH BASED ON MACHINE LEARNING BASED LANGUAGE MODELS
    2.
    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.

    TEXT-BASED REPRESENTATIONS OF LOCATION DATA FOR LARGE LANGUAGE MODEL-BASED ITEM IDENTIFICATION
    3.
    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
    4.
    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.

    Machine Learning Assisted Alerts for Item Picking
    5.
    发明申请
    Machine Learning Assisted Alerts for Item Picking 有权

    公开(公告)号：US20250139574A1

    公开(公告)日：2025-05-01

    申请号：US18498016

    申请日：2023-10-30

    Applicant: Maplebear Inc.

    Inventor： Shang Li ,  Ashish Sinha ,  Krishna Kumar Selvam ,  Qi Xi ,  Amirali Darvishzadeh ,  David Zandman ,  Christopher Billman

    IPC: G06Q10/087 ,  G06N5/022

    Abstract: A machine-learned predictive model is trained to predict potential for customer complaint. The model is part of an online concierge system. The online concierge system accesses a customer order that includes one or more items. The online concierge system determines input data for an item of the one or more items. The online concierge system determines a prediction value associated with potential for customer complaint for the item by applying the machine-learned prediction model to the input data. The online concierge system provides the prediction value to a picker client device associated with a picker who is assigned the item. The picker client device presents an alert to the picker based in part on the prediction value, and the alert includes a message that is customized to mitigate a cause of potential customer complaint for the item.

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