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    DETECTING KEY ITEMS USING LARGE LANGUAGE MACHINE-LEARNED MODELS
    1.
    发明公开
    DETECTING KEY ITEMS USING LARGE LANGUAGE MACHINE-LEARNED MODELS 审中-公开

    公开(公告)号：US20240296385A1

    公开(公告)日：2024-09-05

    申请号：US18592961

    申请日：2024-03-01

    Applicant: Maplebear Inc.

    Inventor： Shishir Kumar Prasad ,  Shrikar Archak

    IPC: G06N20/00 ,  G06N5/04

    CPC classification number: G06N20/00 ,  G06N5/04

    Abstract: An online system performs inference in conjunction with a machine-learned language model to determine one or more key items in an order. The system generates a prompt for input to a machine-learned language model. The prompt may specify at least the list of ordered items in the order and a request to infer one or more key items in the order. The system provides the prompt to a model serving system for execution by the machine-learned language model for execution. The system parses the response from the model serving system to extract a subset of items as the one or more key items of the order. The system generates an interface presenting the order of the list of items and one or more indications on the interface that indicate the subset of items are key items of the order.

    Determining recommended items for a shopping list
    2.
    发明授权
    Determining recommended items for a shopping list 有权

    公开(公告)号：US11195222B2

    公开(公告)日：2021-12-07

    申请号：US16725503

    申请日：2019-12-23

    Applicant: Maplebear, Inc.

    Inventor： Shrikar Archak ,  Sharath Rao

    IPC: G06Q30/00 ,  G06Q30/06 ,  G06F16/248 ,  G06F16/2457

    Abstract: In an online concierge system, a customer adds items to an online shopping cart. The online concierge system determines key ingredients from the items in the online shopping cart by mapping the items to generic items and removing non-ingredient items and staple items. The online concierge system retrieves recipes including at least one of the key ingredients. The online concierge system determines complementary ingredients based on the other ingredients in the recipes and calculates co-occurrence scores for the complementary ingredients. Using the co-occurrence scores, the online concierge system ranks the complementary ingredients and sends for display a subset of the complementary ingredients as recommended items.

    AUTOMATICALLY GENERATING BASKETS OF ITEMS TO BE RECOMMENDED TO USERS OF AN ONLINE SYSTEM
    4.
    发明申请
    AUTOMATICALLY GENERATING BASKETS OF ITEMS TO BE RECOMMENDED TO USERS OF AN ONLINE SYSTEM 有权

    公开(公告)号：US20240394771A1

    公开(公告)日：2024-11-28

    申请号：US18202768

    申请日：2023-05-26

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Shrikar Archak ,  Shishir Kumar Prasad

    IPC: G06Q30/0601 ,  G06Q30/08

    Abstract: Embodiments relate to automatically generating a basket of items to be recommended to a user of an online system. The online system communicates a basket opportunity to a group of retailers, wherein the basket opportunity defines a plurality of item categories each associated with a respective item to be included in a basket. The online system receives, from each retailer in response to the basket opportunity, a respective bid of a plurality of bids for the basket opportunity. The online system applies a computer model to each bid to determine a score for each bid and selects a winning bid for the user based on determined scores for the bids. For each item category, the online system populates the basket with a respective item from a catalog of a retailer that is associated with the winning bid. The online system then presents the basket with items to the user.

    INTEGRATING FEATURED PRODUCT RECOMMENDATIONS IN APPLICATIONS WITH MACHINE-LEARNED LARGE LANGUAGE MODELS (LLMS)
    7.
    发明申请
    INTEGRATING FEATURED PRODUCT RECOMMENDATIONS IN APPLICATIONS WITH MACHINE-LEARNED LARGE LANGUAGE MODELS (LLMS) 有权

    公开(公告)号：US20250156926A1

    公开(公告)日：2025-05-15

    申请号：US18943691

    申请日：2024-11-11

    Applicant: Maplebear Inc.

    Inventor： Haixun Wang ,  Shrikar Archak ,  Tejaswi Tenneti

    IPC: G06Q30/0601 ,  G06F16/9535 ,  G06F16/955 ,  G06Q30/08

    Abstract: An online system receives a user request from a client device through the interface, identifies one or more featured products based on the query, and generates a prompt for input to a machine-learned generative language model. The prompt specifies both the user's request and a request to suggest the featured products in association with a response to the user request. This prompt is fed into a machine-learned language model via a model serving system for execution. The online system receives a response generated by the model, generates a query response based on the response generated by the model, and transmits instructions to the client device to display the query response. The online system collects data on user interactions with the uses the collected data to fine-tune the machine-learned generative language model.

    GENERATING EXPLANATIONS FOR ATYPICAL REPLACEMENTS USING LARGE LANGUAGE MACHINE-LEARNED MODELS
    8.
    发明申请
    GENERATING EXPLANATIONS FOR ATYPICAL REPLACEMENTS USING LARGE LANGUAGE MACHINE-LEARNED MODELS 有权

    公开(公告)号：US20250139106A1

    公开(公告)日：2025-05-01

    申请号：US18933807

    申请日：2024-10-31

    Applicant: Maplebear Inc.

    Inventor： Sharath Rao Karikurve ,  Shrikar Archak ,  Shishir Kumar Prasad

    IPC: G06F16/2457 ,  G06F16/248

    Abstract: An online system performs an atypical replacement recommendation task in conjunction with a model serving system or the interface system to make recommendations to a user for replacing a target item with an atypical replacement item. The online system receives a search query from a user and identifies a target item based on the search query. The online system identifies a set of candidate items for replacing the target item. The online system may select one or more atypical replacement items in the set of candidate items, and generate an explanation for each atypical replacement item. The explanation provides a reason for using the atypical replacement item to replace the target item. The online system provides the atypical replacement items and the corresponding explanations as a response to the search query.

    Inferring User Brand Sensitivity Using a Machine Learning Model
    9.
    发明公开
    Inferring User Brand Sensitivity Using a Machine Learning Model 审中-公开

    公开(公告)号：US20240249333A1

    公开(公告)日：2024-07-25

    申请号：US18100739

    申请日：2023-01-24

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Sharath Rao Karikurve ,  Ramasubramanian Balasubramanian ,  Shrikar Archak

    IPC: G06Q30/0601 ,  G06N20/00

    CPC classification number: G06Q30/0631 ,  G06N20/00

    Abstract: An online concierge system may receive, from a customer, a selection of an item that is associated with a first brand. The online concierge system may extract features associated with the customer and features associated with the item. The online concierge system may input the extracted features to a machine learning model that is trained to predict a degree of association between the customer and the first brand associated with the item. The online concierge system may identify candidate alternatives for replacing the item. The candidate alternatives may include a first alternative that is associated with the first brand and a second alternative that is associated with a second brand different from the first brand. The online concierge system may select, based on the degree of association between the customer and the first brand, one or more candidate alternatives to be presented to the customer to replace the item.

    Generating Sponsored Content Pages Using Large Language Machine-Learned Models
    10.
    发明申请
    Generating Sponsored Content Pages Using Large Language Machine-Learned Models 有权

    公开(公告)号：US20250124498A1

    公开(公告)日：2025-04-17

    申请号：US18917136

    申请日：2024-10-16

    Applicant: Maplebear Inc.

    Inventor： Prithvishankar Srinivasan ,  Shishir Kumar Prasad ,  Min Xie ,  Shrikar Archak ,  Shih-Ting Lin ,  Haixun Wang

    IPC: G06Q30/08 ,  G06Q30/0601

    Abstract: An online system presents a sponsored content page to a user in conjunction with a model serving system. The online system accesses a content page for a food item and identifies one or more sponsorship opportunities at the content page. The online system identifies one or more candidate sponsors for each sponsorship opportunity. The online system selects a bidding sponsor for the sponsorship opportunity from the one or more candidate sponsors and a candidate item associated with the bidding sponsor as a sponsored item. The online system provides a content page, a description of the sponsored item, and a request to generate a sponsored content page for the sponsorship opportunity to a model serving system. The online system receives a sponsored content page generated by a machine-learning language model at the model serving system and presents the sponsored content page to a user.

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