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    WEAKLY SUPERVISED EXTRACTION OF ATTRIBUTES FROM UNSTRUCTURED DATA TO GENERATE TRAINING DATA FOR MACHINE LEARNING MODELS
    1.
    发明申请
    WEAKLY SUPERVISED EXTRACTION OF ATTRIBUTES FROM UNSTRUCTURED DATA TO GENERATE TRAINING DATA FOR MACHINE LEARNING MODELS 有权

    公开(公告)号：US20230058829A1

    公开(公告)日：2023-02-23

    申请号：US17407158

    申请日：2021-08-19

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Shih-Ting Lin ,  Jonathan Newman ,  Min Xie ,  Haixun Wang

    IPC: G06K9/62 ,  G06Q30/06

    Abstract: An online concierge system receives unstructured data describing items offered for purchase by various warehouses. To generate attributes for products from the unstructured data, the online concierge system extracts candidate values for attributes from the unstructured data through natural language processing. One or more users associate a subset candidate values with corresponding attributes, and the online concierge system clusters the remaining candidate values with the candidate values of the subset associated with attributes. One or more users provide input on the accuracy of the generated clusters. The candidate values are applied as labels to items by the online concierge system, which uses the labeled items as training data for an attribute extraction model to predict values for one or more attributes from unstructured data about an item.

    EXTRACTING ITEM ATTRIBUTES FROM ITEM DESCRIPTIONS USING LARGE LANGUAGE MACHINE-LEARNED MODELS
    6.
    发明申请
    EXTRACTING ITEM ATTRIBUTES FROM ITEM DESCRIPTIONS USING LARGE LANGUAGE MACHINE-LEARNED MODELS 有权

    公开(公告)号：US20240403947A1

    公开(公告)日：2024-12-05

    申请号：US18677588

    申请日：2024-05-29

    Applicant: Maplebear Inc.

    Inventor： Shih-Ting Lin

    IPC: G06Q30/0601

    Abstract: A system may obtain an item description associated with an item in an item catalog. The system may generate a prompt for input to a machine-learned language model, the prompt specifying at least the item description and a request to identify one or more attributes of the item. The system may provide the prompt to a model serving system for execution by the machine-learned language model. The system may receive from the machine-learned language model, an output including a list of attributes and respective values associated with the item based on the item description. The system may standardize the formatting of the list of attributes and may store the list of attributes and the respective values for the list of attributes in association with the item in the item catalog.

    ATTRIBUTE SCHEMA AUGMENTATION WITH RELATED CATEGORIES
    7.
    发明公开
    ATTRIBUTE SCHEMA AUGMENTATION WITH RELATED CATEGORIES 审中-公开

    公开(公告)号：US20240029132A1

    公开(公告)日：2024-01-25

    申请号：US17868572

    申请日：2022-07-19

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Shih-Ting Lin ,  Amirali Darvishzadeh ,  Min Xie ,  Haixun Wang

    IPC: G06Q30/06 ,  G06F40/20

    CPC classification number: G06Q30/0627 ,  G06F40/20 ,  G06N20/00

    Abstract: To improve attribute prediction for items, item categories are associated with a schema that is augmented with additional attributes and/or attribute labels. Items may be organized into categories and similar categories may be related to one another, for example in a taxonomy or other organizational structure. An attribute extraction model may be trained for each category based on an initial attribute schema for the respective category and the items of that category. The extraction model trained for one category may be used to identify additional attributes and/or attribute labels for the same or another, related category.

    USING LANGUAGE MODEL TO GENERATE RECIPE WITH REFINED CONTENT
    8.
    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
    9.
    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.

    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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