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    Systems and methods for contract based offer generation
    172.
    发明授权
    Systems and methods for contract based offer generation 有权

    公开(公告)号：US12254482B2

    公开(公告)日：2025-03-18

    申请号：US18159249

    申请日：2023-01-25

    Applicant: MAPLEBEAR INC.

    Inventor： Jacob Solotaroff ,  Jamie Rapperport

    IPC: G06Q30/02 ,  G06Q30/0201 ,  G06Q30/0211 ,  G06Q30/0251

    Abstract: Systems and methods for a contract-based offer generator is provided. A contract for a promotional offer on a product is received. Data is extracted from the contract. An offer band is accessed, and a plurality of test offers are selected from the offer bank by scoring each offer in the offer bank against the extracted data. The promotional offer and the selected plurality of test offers are deployed in a plurality of retail locations. This is done by maximizing orthogonality between the following variables: store sales, store out of stock rates, number of relevant SKUs carried in each store, temporal effects, discount depth, buy quantity and offer structure.

    ANOMALY DETECTION AND USER ATTRIBUTION USING MACHINE-LEARNING LARGE LANGUAGE MODELS
    173.
    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.

    SELECTING RECOMMENDATIONS BASED ON MACHINE LEARNING PREDICTION OF USER SENSITIVITY TO RELEVANCE OF RECOMMENDATIONS TO SEARCH RESULTS
    174.
    发明申请
    SELECTING RECOMMENDATIONS BASED ON MACHINE LEARNING PREDICTION OF USER SENSITIVITY TO RELEVANCE OF RECOMMENDATIONS TO SEARCH RESULTS 有权

    公开(公告)号：US20250078133A1

    公开(公告)日：2025-03-06

    申请号：US18240157

    申请日：2023-08-30

    Applicant: Maplebear Inc.

    Inventor： Brian Lin ,  Angadh Singh ,  Sharath Rao Karikurve

    IPC: G06Q30/0601 ,  G06Q30/0201

    Abstract: Content items are presented to users based on sensitivity scores indicating sensitivity levels of users to relevance of content items to queries. A system receives a query from a target user, retrieves a set of search results responsive to the query, and retrieves a set of content items, each of which has a relevance score to the query. The system applies a machine learning model to user data of the target user to output a sensitivity score, indicating a sensitivity level of the target user to relevance of content item to the query. The system then selects one or more content items based on the sensitivity score and the relevance scores of the content items, incorporates the selected content items into the search results, and sends the search results with the selected content items for display to the target user.

    MACHINE-LEARNED MODEL FOR OPTMIZING SELECTION SEQUENCE FOR ITEMS IN A WAREHOUSE
    175.
    发明申请
    MACHINE-LEARNED MODEL FOR OPTMIZING SELECTION SEQUENCE FOR ITEMS IN A WAREHOUSE 有权

    公开(公告)号：US20250078025A1

    公开(公告)日：2025-03-06

    申请号：US18952836

    申请日：2024-11-19

    Applicant: Maplebear Inc.

    Inventor： Jeremy Stanley ,  Montana Low ,  Nima Zahedi

    IPC: 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.

    GENERATING TRAINING DATA FOR A NUTRITIONAL REPLACEMENT MACHINE-LEARNING MODEL
    178.
    发明申请
    GENERATING TRAINING DATA FOR A NUTRITIONAL REPLACEMENT MACHINE-LEARNING MODEL 有权

    公开(公告)号：US20250069723A1

    公开(公告)日：2025-02-27

    申请号：US18455498

    申请日：2023-08-24

    Applicant: Maplebear Inc.

    Inventor： Bhavya Gulati ,  Chakshu Ahuja ,  Girija Narlikar ,  Karuna Ahuja ,  Radhika Goel

    IPC: G16H20/60 ,  G16H50/20

    Abstract: The online concierge system accesses item data for a target item and item data for a candidate item. The online concierge system generates a replacement score based on the accessed item data and generates a nutrition score based on the item data for the candidate item. The online concierge system generates a nutrition replacement score based on the replacement score and the nutrition score and stores a training example based on the item data and the nutrition replacement score. The training example may include the item data for the target item and the candidate item and a label based on the nutrition replacement score.

    Shopping cart self-tracking in an indoor environment
    180.
    发明授权
    Shopping cart self-tracking in an indoor environment 有权

    公开(公告)号：US12227219B2

    公开(公告)日：2025-02-18

    申请号：US17873526

    申请日：2022-07-26

    Applicant: Maplebear Inc.

    Inventor： Lin Gao ,  Yilin Huang ,  Shiyuan Yang ,  Xiaofei Zhou ,  Kaiyang Chu ,  Sikun Zhu

    IPC: H04W4/00 ,  B62B3/14 ,  B62B5/00 ,  G01C21/00 ,  G01C21/16 ,  G01S5/00 ,  G07C5/04 ,  H04W4/029

    Abstract: A shopping cart's tracking system determines a first baseline location of the shopping cart at a first timestamp with a wireless device located on the shopping cart detecting one or more external wireless devices (e.g., RFID tags) in the indoor environment. The shopping cart's tracking system receives wheel motion data from one or more wheel sensors coupled to one or more wheels of the shopping cart, wherein the wheel motion data describes rotation of the one or more wheels. The shopping cart's tracking system calculates a translation traveled by the shopping cart from the first baseline location based on the wheel motion data. The shopping cart's tracking system determines an estimated location of the shopping cart at a second timestamp based on the first baseline location and the translation. With the estimated location, the shopping cart can update a map with the estimated location of the shopping cart.

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