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    MODIFYING RANKINGS OF ITEMS IN SEARCH RESULTS BASED ON ITEM AVAILABILITIES AND SEARCH QUERY ATTRIBUTES
    182.
    发明申请
    MODIFYING RANKINGS OF ITEMS IN SEARCH RESULTS BASED ON ITEM AVAILABILITIES AND SEARCH QUERY ATTRIBUTES 有权

    公开(公告)号：US20250005654A1

    公开(公告)日：2025-01-02

    申请号：US18217329

    申请日：2023-06-30

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Raochuan Fan ,  Prakash Putta ,  Vinesh Reddy Gudla ,  Nkemakonam Paulet Okoye ,  Taesik Na ,  Tejaswi Tenneti

    IPC: G06Q30/0601

    Abstract: An online concierge system allows a customer to search items offered by a retailer by providing a set of items to the customer based on a search query. To account for varying availability of items at the retailer, the online concierge system modifies rankings in the set of items having less than a threshold predicted availability at the retailer. This reduces a likelihood selection of an item likely to be unavailable at the retailer. To maintain customer confidence in the items selected based on the search results by maintaining visibility of items relevant to the search query, the online concierge system determines how much an item is modified within the set based on search query attributes, item attributes, or customer characteristics. This allows different items to be adjusted different amounts in a set based on the item, as well as the search query for which the item was selected.

    Machine Learning Model for Predicting Likelihoods of Events on Multiple Different Surfaces of an Online System
    183.
    发明申请
    Machine Learning Model for Predicting Likelihoods of Events on Multiple Different Surfaces of an Online System 有权

    公开(公告)号：US20250005381A1

    公开(公告)日：2025-01-02

    申请号：US18217356

    申请日：2023-06-30

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Peng Qi ,  Cheng Jia ,  Xiyu Wang ,  Qiao Jiang ,  Sharad Gupta ,  David Pal ,  Joseph Haraldson ,  Zhenbang Chen

    IPC: G06N5/022

    Abstract: An online system manages presentation of content items in various presentation contexts such as when the users are browsing pages or when the users have entered a search query. The online system trains a single unified machine learning model that predicts one or more likelihoods of a target event associated with presentation of a content item in the different presentation contexts. The learned model is applied to a set of candidate content items associated with a presentation opportunity in a specific context. Features that are inapplicable to the specific context may be masked when applying the model. The online system may select between the candidate content items based on the predicted likelihoods using the model trained across the multiple different contexts, such that the prediction for one context may be based in part on learned outcomes in other related contexts.

    SUGGESTING FULFILLMENT SOURCES FOR A USER AT A NEW LOCATION BASED ON USER'S HISTORICAL ACTIVITY
    184.
    发明申请
    SUGGESTING FULFILLMENT SOURCES FOR A USER AT A NEW LOCATION BASED ON USER'S HISTORICAL ACTIVITY 有权

    公开(公告)号：US20240428315A1

    公开(公告)日：2024-12-26

    申请号：US18213764

    申请日：2023-06-23

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Sharath Rao Karikurve ,  Ramasubramanian Balasubramanian

    IPC: G06Q30/0601

    Abstract: An online system provides a platform for users to place orders at different physical retailers. When a user moves from one location to another (e.g., the user physically moves or is traveling), where the user's preferred retailer is not available, the online system suggests a new retailer for the user and optionally items to purchase at the new retailer. When a user accesses the online system from a new location, the system obtains the user's previous purchases and computes a repurchase probability. The system then ranks candidate new retailers in the new location based on their match to the likely repurchased items. To suggest new items to buy at the new retailer, the system uses existing replacement models to suggest replacements for the items that the user is likely to buy based on previous purchases.

    Providing search suggestions based on previous searches and conversions
    185.
    发明授权
    Providing search suggestions based on previous searches and conversions 有权

    公开(公告)号：US12175482B2

    公开(公告)日：2024-12-24

    申请号：US17486395

    申请日：2021-09-27

    Applicant: Maplebear Inc.

    Inventor： Tejaswi Tenneti ,  Tyler Russell Tate ,  Jonathan Lennart Bender ,  Shishir Kumar Prasad ,  Qingyuan Chen

    IPC: G06Q10/00 ,  G06F16/901 ,  G06F16/9535 ,  G06Q30/0201 ,  G06Q30/0601

    Abstract: An online concierge system suggests subsequent search queries based on previous search queries and whether the previous search queries resulted in conversions. The online concierge system trains a machine learning model using previous delivery orders and whether initial and subsequent search queries in the previous delivery orders resulted in conversions. When the online concierge system receives a search query to identify one or more items from a customer, the online concierge system parses the search query into combinations of terms and identifies items related to the search query. In response to the search query resulting in a conversion, the online concierge system retrieves a conversion graph and presents a suggested subsequent search query based on the conversion graph. In response to the search query not resulting in a conversion, the online concierge system retrieves a non-conversion graph and presents a suggested subsequent search query based on the non-conversion graph.

    EXTRACTING ITEM ATTRIBUTES FROM ITEM DESCRIPTIONS USING LARGE LANGUAGE MACHINE-LEARNED MODELS
    186.
    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.

    INCREMENTALLY UPDATING EMBEDDINGS FOR USE IN A MACHINE LEARNING MODEL BY ACCOUNTING FOR EFFECTS OF THE UPDATED EMBEDDINGS ON THE MACHINE LEARNING MODEL
    188.
    发明公开
    INCREMENTALLY UPDATING EMBEDDINGS FOR USE IN A MACHINE LEARNING MODEL BY ACCOUNTING FOR EFFECTS OF THE UPDATED EMBEDDINGS ON THE MACHINE LEARNING MODEL 审中-公开

    公开(公告)号：US20240362657A1

    公开(公告)日：2024-10-31

    申请号：US18767909

    申请日：2024-07-09

    Applicant: Maplebear Inc.

    Inventor： Chuanwei Ruan ,  Ramasubramanian Balasubramanian ,  Peng Qi

    IPC: G06Q30/0202 ,  G06Q10/087

    CPC classification number: G06Q30/0202 ,  G06Q10/087

    Abstract: An online concierge system uses a model to predict a user's interaction with an item, based on a user embedding for the user and an item embedding for the item. For the model to account for more recent interactions by users with items without retraining the model, the online concierge system generates updated item embeddings and updated user embeddings that account for the recent interactions by users with items. The online concierge system compares performance of the model using the updated item embeddings and the updated user embeddings relative to performance of the model using the existing item embeddings and user embeddings. If the performance of the model decreases, the online concierge system adjusts the updated user embeddings and the updated item embeddings based on the change in performance of the model. The adjusted updated user embeddings and adjusted updated item embeddings are stored for use by the model.

    WAREHOUSE ITEM ASSORTMENT COMPARISON AND DISPLAY CUSTOMIZATION
    189.
    发明公开
    WAREHOUSE ITEM ASSORTMENT COMPARISON AND DISPLAY CUSTOMIZATION 审中-公开

    公开(公告)号：US20240362580A1

    公开(公告)日：2024-10-31

    申请号：US18141394

    申请日：2023-04-29

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Kenneth Jason Sanchez ,  Haochen Luo ,  Rishab Saraf ,  Eric Hermann ,  Dario Fidanza

    IPC: G06Q10/087 ,  G06Q30/0202

    CPC classification number: G06Q10/087 ,  G06Q30/0202

    Abstract: An online system evaluates different item assortments for a physical warehouse having limited capacity to stock items. Each item assortment is stocked at the physical warehouse in proportion to an assortment split weight. The items at the warehouse are available for users to order, for example to be gathered by a picker and physically delivered to users near the warehouse. Rather than display all items actually stocked at the physical warehouse to all users, the different item assortments are displayed to different users. Users may order items from the assigned item assortment and, because both item assortments are actually stocked at the physical warehouse, orders from either item assortment may be successfully fulfilled for delivery. The different user interfaces thus permit evaluation of the preferred item assortment by users while maintaining expected delivery capability and while using the same storage capacity of the physical warehouse.

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