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    TEXT-BASED REPRESENTATIONS OF LOCATION DATA FOR LARGE LANGUAGE MODEL-BASED ITEM IDENTIFICATION
    1.
    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
    2.
    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.

    WAREHOUSE ITEM ASSORTMENT COMPARISON AND DISPLAY CUSTOMIZATION
    3.
    发明公开
    WAREHOUSE ITEM ASSORTMENT COMPARISON AND DISPLAY CUSTOMIZATION 审中-公开

    公开(公告)号：US20240362580A1

    公开(公告)日：2024-10-31

    申请号：US18141394

    申请日：2023-04-29

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Kenneth Jason Sanchez ,  Haochen Luo ,  Rishab Saraf ,  Eric Hermann ,  Dario Fidanza

    IPC: G06Q10/087 ,  G06Q30/0202

    CPC classification number: G06Q10/087 ,  G06Q30/0202

    Abstract: An online system evaluates different item assortments for a physical warehouse having limited capacity to stock items. Each item assortment is stocked at the physical warehouse in proportion to an assortment split weight. The items at the warehouse are available for users to order, for example to be gathered by a picker and physically delivered to users near the warehouse. Rather than display all items actually stocked at the physical warehouse to all users, the different item assortments are displayed to different users. Users may order items from the assigned item assortment and, because both item assortments are actually stocked at the physical warehouse, orders from either item assortment may be successfully fulfilled for delivery. The different user interfaces thus permit evaluation of the preferred item assortment by users while maintaining expected delivery capability and while using the same storage capacity of the physical warehouse.

    DATABASE SEARCH BASED ON MACHINE LEARNING BASED LANGUAGE MODELS
    4.
    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.

    INTERACTION PREDICTION FOR INVENTORY ASSORTMENT WITH NEARBY LOCATION FEATURES
    5.
    发明公开
    INTERACTION PREDICTION FOR INVENTORY ASSORTMENT WITH NEARBY LOCATION FEATURES 审中-公开

    公开(公告)号：US20240362579A1

    公开(公告)日：2024-10-31

    申请号：US18141393

    申请日：2023-04-29

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Haochen Luo ,  Kenneth Jason Sanchez ,  Eric Hermann

    IPC: G06Q10/087

    CPC classification number: G06Q10/087

    Abstract: An inventory interaction model predicts user interactions with items of a location for a physical warehouse included with other warehouses in a region. The location is described with features that include the nearby locations and the respective user interactions with the respective item assortments, so that the item interactions for the evaluated location incorporate location-location effects in model predictions. To effectively train the model in the absence of prior interaction data for a location, training examples are generated from existing locations and user interaction data of item assortments by selecting a portion of the locations for the training examples and including nearby location interaction data, labeling the training example output with item interactions for the location. The trained model is then applied for an item assortment at a location by describing nearby locations in evaluating candidate locations and item assortments.

    PREDICTIVE PICKING OF ITEMS FOR PREPOPULATING A SHOPPING CART FOR A SHOPPER
    6.
    发明公开
    PREDICTIVE PICKING OF ITEMS FOR PREPOPULATING A SHOPPING CART FOR A SHOPPER 审中-公开

    公开(公告)号：US20240331015A1

    公开(公告)日：2024-10-03

    申请号：US18129464

    申请日：2023-03-31

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Kenneth Jason Sanchez ,  Eric Hermann

    IPC: G06Q30/0601 ,  G06Q30/02

    CPC classification number: G06Q30/0635 ,  G06Q30/0281 ,  G06Q30/0639

    Abstract: An online concierge system facilitates creation of shopping lists of items for ordering from a physical retail store and at least partial self-service fulfillment of orders by the customer. To support fulfillment by the customer, the online concierge system may intelligently select one or more items of the order to be picked by a third-party picker and prepopulated to a shopping cart reserved for the customer in advance of the customer arriving at the retail location. The items for prepopulating may be selected based on various factors that optimize prepopulation decisions on an item-by-item basis in accordance with various machine learning models. The online concierge system may furthermore facilitate procurement of the remaining items by the customer through a customer client device that may track item procurement and/or provide guidance for locating items.

    REPLACING AN UNAVAILABLE ITEM IN AN ORDER USING A TRAINED OUTCOME PREDICTION MODEL
    7.
    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.

    TRAINED COMPUTER MODELS FOR AUTOMATIC SUGGESTION OF ALTERNATIVE ITEMS IN AN ORDER
    9.
    发明申请
    TRAINED COMPUTER MODELS FOR AUTOMATIC SUGGESTION OF ALTERNATIVE ITEMS IN AN ORDER 有权

    公开(公告)号：US20250124486A1

    公开(公告)日：2025-04-17

    申请号：US18488811

    申请日：2023-10-17

    Applicant: Maplebear Inc.

    Inventor： Alexis Weill ,  Kenneth Jason Sanchez

    IPC: G06Q30/0601 ,  G06F40/40

    Abstract: Embodiments relate to automatic determination of an alternative item for an item of original set of items included into an order of a user of an online system. The online system accesses a first computer model trained to identify a set of candidate replacement items for the item and selects a subset of the candidate replacement items from the identified set based on a constraint that each candidate replacement item in the subset has a smaller monetary value than the item. The online system accesses a second computer model trained to select a candidate replacement item from the subset based on a predicted likelihood of conversion by the user for each candidate replacement item. The online system causes a device of the user to display a user interface with the selected candidate replacement item for inclusion into the order instead of the item from the original set of items.

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