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    Machine learned models for search and recommendations
    1.
    发明授权
    Machine learned models for search and recommendations 有权

    公开(公告)号：US12287819B2

    公开(公告)日：2025-04-29

    申请号：US18415551

    申请日：2024-01-17

    Applicant: Maplebear Inc.

    Inventor： Haixun Wang ,  Taesik Na ,  Li Tan ,  Jian Li ,  Xiao Xiao

    IPC: G06F16/33 ,  G06F16/334 ,  G06F16/338 ,  G06N20/00

    Abstract: A system may generate a prompt based in part on a search query from a customer client device. The prompt instructs a machine learned model to provide item predictions. And the model was trained by: converting structured data describing items of an online catalog to annotated text data (unstructured data), generating training examples based in part on the annotated text data, and training the model using the training examples. The system may receive item predictions generated by the prompt being applied to the machine learned model, the item predictions may have corresponding item identifiers. The item predictions are processed to identify a recommended item from the item predictions. The processing includes determining item information for the recommended item using an item identifier associated with the recommended item. The item information is provided to the customer client device.

    PROVIDING AND DISPLAYING SEARCH RESULTS IN RESPONSE TO A QUERY
    3.
    发明公开
    PROVIDING AND DISPLAYING SEARCH RESULTS IN RESPONSE TO A QUERY 审中-公开

    公开(公告)号：US20240249335A1

    公开(公告)日：2024-07-25

    申请号：US18159357

    申请日：2023-01-25

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Taesik Na ,  Vinesh Reddy Gudla ,  Xiao Xiao

    IPC: G06Q30/0601 ,  G06F16/9535 ,  G06Q30/0201

    CPC classification number: G06Q30/0631 ,  G06F16/9535 ,  G06Q30/0201

    Abstract: An online system displays search results in response to a query by receiving a query from a customer. An online system accesses a set of candidate items and computes a relevance score and personalization score for each item. The online system computes the relevance score based on query data and item data and may normalize the relevance score. The online system computes the personalization score based on item data, such as an item embedding, and user data, such as a user embedding. The online system computes a query specificity score and adjusts the personalization score with the query specificity score such that generic queries have high personalization scores and specific queries have low personalization scores. The online system combines the relevance and personalization scores for each candidate item into a ranking score and displays the candidate items to the customer based on their ranking scores.

    Providing and displaying search results in response to a query
    4.
    发明授权
    Providing and displaying search results in response to a query 有权

    公开(公告)号：US12266006B2

    公开(公告)日：2025-04-01

    申请号：US18159357

    申请日：2023-01-25

    Applicant: Maplebear Inc.

    Inventor： Taesik Na ,  Vinesh Reddy Gudla ,  Xiao Xiao

    IPC: G06Q30/06 ,  G06F16/9535 ,  G06Q30/0201 ,  G06Q30/0601

    Abstract: An online system displays search results in response to a query by receiving a query from a customer. An online system accesses a set of candidate items and computes a relevance score and personalization score for each item. The online system computes the relevance score based on query data and item data and may normalize the relevance score. The online system computes the personalization score based on item data, such as an item embedding, and user data, such as a user embedding. The online system computes a query specificity score and adjusts the personalization score with the query specificity score such that generic queries have high personalization scores and specific queries have low personalization scores. The online system combines the relevance and personalization scores for each candidate item into a ranking score and displays the candidate items to the customer based on their ranking scores.

    MACHINE LEARNED MODELS FOR SEARCH AND RECOMMENDATIONS
    5.
    发明公开
    MACHINE LEARNED MODELS FOR SEARCH AND RECOMMENDATIONS 审中-公开

    公开(公告)号：US20240241897A1

    公开(公告)日：2024-07-18

    申请号：US18415551

    申请日：2024-01-17

    Applicant: Maplebear Inc.

    Inventor： Haixun Wang ,  Taesik Na ,  Li Tan ,  Jian Li ,  Xiao Xiao

    IPC: G06F16/33 ,  G06F16/338 ,  G06N20/00

    CPC classification number: G06F16/3344 ,  G06F16/338 ,  G06N20/00

    Abstract: A system may generate a prompt based in part on a search query from a customer client device. The prompt instructs a machine learned model to provide item predictions. And the model was trained by: converting structured data describing items of an online catalog to annotated text data (unstructured data), generating training examples based in part on the annotated text data, and training the model using the training examples. The system may receive item predictions generated by the prompt being applied to the machine learned model, the item predictions may have corresponding item identifiers. The item predictions are processed to identify a recommended item from the item predictions. The processing includes determining item information for the recommended item using an item identifier associated with the recommended item. The item information is provided to the customer client device.

    FALSE NEGATIVE PREDICTION FOR TRAINING A MACHINE-LEARNING MODEL
    7.
    发明申请
    FALSE NEGATIVE PREDICTION FOR TRAINING A MACHINE-LEARNING MODEL 有权

    公开(公告)号：US20250147997A1

    公开(公告)日：2025-05-08

    申请号：US18932301

    申请日：2024-10-30

    Applicant: Maplebear Inc.

    Inventor： Xiaochen Wang ,  Taesik Na ,  Xiao Xiao ,  Ruhan Zhang ,  Xuan Zhang ,  Tejaswi Tenneti ,  Haixun Wang

    IPC: G06F16/35 ,  G06F16/383

    Abstract: An online system updates the labels on negative examples to account for the possibility that the example is a false negative. The system generates a set of initial training examples that each include a query input by the user and item data for an item presented as a result to the user's query. Each training example also includes an initial label, which represents whether the user interacted with the item presented as a search result. The online system updates the initial label for a negative training example by identifying a set of bridge queries and computing a similarity score between the query for the training example and the bridge queries. The online system computes an updated label for the negative example based on the similarity scores and updates the training example with the updated label.

    Using Language Model To Automatically Generate List Of Items At An Online System Based on a Constraint
    8.
    发明申请
    Using Language Model To Automatically Generate List Of Items At An Online System Based on a Constraint 有权

    公开(公告)号：US20240427808A1

    公开(公告)日：2024-12-26

    申请号：US18214275

    申请日：2023-06-26

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Vinesh Reddy Gudla ,  Sudha Rani Kolavali ,  Taesik Na ,  Xiao Xiao ,  Nkemakonam Paulet Okoye

    IPC: G06F16/332 ,  G06F16/33

    Abstract: Embodiments relate to using a large language model (LLM) to generate a list of items at an online system with a user defined constraint. The online system receives a query that includes at least one constraint. The online system generates a prompt for input into the LLM, based at least in part on the query. The online system requests the LLM to generate, based on the prompt, a set of constraints for a set of item types. The online system generates a list of candidate items by searching through a set of items stored in one or more non-transitory computer-readable media using the set of constraints for the set of item types. The online system causes a device of the user to display a user interface with the list of items for inclusion into a cart, the list of items obtained from the list of candidate items.

    DETERMINING SEARCH RESULTS FOR AN ONLINE SHOPPING CONCIERGE PLATFORM
    10.
    发明公开
    DETERMINING SEARCH RESULTS FOR AN ONLINE SHOPPING CONCIERGE PLATFORM 审中-公开

    公开(公告)号：US20240177212A1

    公开(公告)日：2024-05-30

    申请号：US18072353

    申请日：2022-11-30

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Aditya Subramanian ,  Prakash Putta ,  Tejaswi Tenneti ,  Jonathan Lennart Bender ,  Xiao Xiao ,  Taesik Na

    IPC: G06Q30/0601

    CPC classification number: G06Q30/0631

    Abstract: To determine search results for an online shopping concierge platform, the platform may receive, from a computing device associated with a customer of an online shopping concierge platform, data describing one or more search parameters input by the customer; identify, based at least in part on the data describing the search parameter(s), products offered by the online shopping concierge platform that are at least in part responsive to the search parameter(s); and determine, for each product and based at least in part on one or more machine learning (ML) models, a relevance of the product to one or more taxonomy levels of a product catalog associated with the online shopping concierge platform, a likelihood that the customer would be offended by inclusion of the product amongst displayed responsive search results, and/or the like.

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