中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

37 records (took 0.026 seconds)

    DETECTING KEY ITEMS USING LARGE LANGUAGE MACHINE-LEARNED MODELS
    3.
    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.

    INFERRING CATEGORIES IN A PRODUCT TAXONOMY USING A REPLACEMENT MODEL
    5.
    发明申请
    INFERRING CATEGORIES IN A PRODUCT TAXONOMY USING A REPLACEMENT MODEL 有权

    公开(公告)号：US20220292567A1

    公开(公告)日：2022-09-15

    申请号：US17196855

    申请日：2021-03-09

    Applicant: Maplebear, Inc. (dba Instacart)

    Inventor： Shishir Kumar Prasad ,  Sharath Rao Karikurve ,  Abhay Pawar

    IPC: G06Q30/06 ,  G06Q10/08 ,  G06F16/28 ,  G06F16/2457 ,  G06N20/00

    Abstract: An online concierge system accesses a hierarchical taxonomy of products each labeled with a category of the hierarchical taxonomy. The online concierge system receives, from an inventory database, an unlabeled product, which not included in the hierarchical taxonomy. The online concierge system inputs the unlabeled product to a replacement model. The replacement model is trained to output, for each of one or more labeled products from the hierarchical taxonomy, a likelihood that a user would select the labeled product as a replacement for an input product. The online concierge system selects a labeled product from the one or more labeled products based on the likelihoods. The online concierge system adds the unlabeled product to a category of the hierarchical taxonomy based on the selected labeled product.

    USING LANGUAGE MODEL TO GENERATE RECIPE WITH REFINED CONTENT
    6.
    发明申请
    USING LANGUAGE MODEL TO GENERATE RECIPE WITH REFINED CONTENT 有权

    公开(公告)号：US20250086395A1

    公开(公告)日：2025-03-13

    申请号：US18244098

    申请日：2023-09-08

    Applicant: Maplebear Inc.

    Inventor： Prithvishankar Srinivasan ,  Saurav Manchanda ,  Shih-Ting Lin ,  Shishir Kumar Prasad ,  Riddhima Sejpal ,  Luis Manrique ,  Min Xie

    IPC: G06F40/30

    Abstract: Embodiments relate to utilizing a language model to automatically generate a novel recipe with refined content, which can be offered to a user of an online system. The online system generates a first prompt for input into a large language model (LLM), the first prompt including a plurality of task requests for generating initial content of a recipe. The online system requests the LLM to generate, based on the first prompt input into the LLM, the initial content of the recipe. The online system generates a second prompt for input into the LLM, the second prompt including the initial content of the recipe and contextual information about the recipe. The online system requests the LLM to generate, based on the second prompt input into the LLM, refined content of the recipe. The online system stores the recipe with the refined content in a database of the online system.

    USING UNSUPERVISED CLUSTERING AND LANGUAGE MODEL TO NORMALIZE ATTRIBUTE TUPLES OF ITEMS IN A DATABASE
    7.
    发明申请
    USING UNSUPERVISED CLUSTERING AND LANGUAGE MODEL TO NORMALIZE ATTRIBUTE TUPLES OF ITEMS IN A DATABASE 有权

    公开(公告)号：US20250005279A1

    公开(公告)日：2025-01-02

    申请号：US18215505

    申请日：2023-06-28

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Shih-Ting Lin ,  Prithvishankar Srinivasan ,  Saurav Manchanda ,  Shishir Kumar Prasad ,  Min Xie

    IPC: G06F40/247 ,  G06F16/21 ,  G06F16/215 ,  G06F16/28

    Abstract: A computer system uses clustering and a large language model (LLM) to normalize attribute tuples for items stored in a database of an online system. The online system collects attribute tuples, each attribute tuple comprising an attribute type and an attribute value for an item. The online system initially clusters the attribute tuples into a first plurality of clusters. The online system generates prompts for input into the LLM, each prompt including a subset of attribute tuples grouped into a respective cluster of the first plurality. Based on the prompts, the LLM generates a second plurality of clusters, each cluster including one or more attribute tuples that have a common attribute type and a common attribute value. The online system maps each attribute tuple to a respective normalized attribute tuple associated with each cluster. The online system rewrites each attribute tuple in the database to a corresponding normalized attribute tuple.

    AUTOMATICALLY GENERATING BASKETS OF ITEMS TO BE RECOMMENDED TO USERS OF AN ONLINE SYSTEM
    8.
    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.

    GENERATING ITEM REPLACEMENTS USING MACHINE LEARNING BASED LANGUAGE MODELS
    9.
    发明公开
    GENERATING ITEM REPLACEMENTS USING MACHINE LEARNING BASED LANGUAGE MODELS 审中-公开

    公开(公告)号：US20240362696A1

    公开(公告)日：2024-10-31

    申请号：US18643890

    申请日：2024-04-23

    Applicant: Maplebear Inc.

    Inventor： Shishir Kumar Prasad ,  Ahsaas Bajaj

    IPC: G06Q30/0601 ,  G06F40/40 ,  G06V30/10

    CPC classification number: G06Q30/0629 ,  G06F40/40 ,  G06V30/10

    Abstract: An online system uses a machine learning based language model, for example, a large language model (LLM) to identify replacement items for an item that may not be available at a store. The online system receives a request for an item and determines that the requested item is not available. The online system identifies a replacement item. If the online system determines that the replacement item has a replacement score below a threshold value indicating a low quality of replacement for the requested item, it uses a machine learning based language model, for example, a large language model to generate an explanation for why the replacement item has a replacement score below the threshold value. The online system sends the explanation to a client device.

Patent Agency Ranking