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    INFERRING CATEGORIES IN A PRODUCT TAXONOMY USING A REPLACEMENT MODEL
    1.
    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.

    CLUSTERING ITEMS OFFERED BY AN ONLINE CONCIERGE SYSTEM TO CREATE AND TO RECOMMEND COLLECTIONS OF ITEMS TO USERS
    3.
    发明申请
    CLUSTERING ITEMS OFFERED BY AN ONLINE CONCIERGE SYSTEM TO CREATE AND TO RECOMMEND COLLECTIONS OF ITEMS TO USERS 有权

    公开(公告)号：US20220335489A1

    公开(公告)日：2022-10-20

    申请号：US17232621

    申请日：2021-04-16

    Applicant: Maplebear, Inc.(dba Instacart)

    Inventor： Sharath Rao Karikurve ,  Angadh Singh

    IPC: G06Q30/06 ,  G06N20/00 ,  G06F17/16

    Abstract: An online concierge system maintains information about items offered for purchase and users of the online concierge system. Based on prior purchases of items by users, the online concierge system trains a model to determine a likelihood of a user purchasing an item based on an embedding for the object and embedding for the user. The online concierge system identifies a collection of items and generates an embedding for the collection. The collection may be a cluster of items determined from similarities between embeddings of items. Alternatively, the collection may be a group of items having a common category. The online concierge system includes one or more collections of items along with individual items when recommending items for the users, so the trained model is applied to embeddings of the individual items and to embeddings of the one or more collections to generate recommendations for a user.

    SUGGESTING FULFILLMENT SOURCES FOR A USER AT A NEW LOCATION BASED ON USER'S HISTORICAL ACTIVITY
    5.
    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.

    Inferring User Brand Sensitivity Using a Machine Learning Model
    6.
    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.

    PERSONALIZED RECOMMENDATION OF RECIPES INCLUDING ITEMS OFFERED BY AN ONLINE CONCIERGE SYSTEM BASED ON EMBEDDINGS FOR A USER AND FOR STORED RECIPES
    7.
    发明申请
    PERSONALIZED RECOMMENDATION OF RECIPES INCLUDING ITEMS OFFERED BY AN ONLINE CONCIERGE SYSTEM BASED ON EMBEDDINGS FOR A USER AND FOR STORED RECIPES 有权

    公开(公告)号：US20220358562A1

    公开(公告)日：2022-11-10

    申请号：US17682444

    申请日：2022-02-28

    Applicant: Maplebear Inc.(dba Instacart)

    Inventor： Manmeet Singh ,  Tyler Russell Tate ,  Tejaswi Tenneti ,  Sharath Rao Karikurve

    IPC: G06Q30/06

    Abstract: An online concierge shopping system identifies recipes to users to encourage them to include items from the recipes in orders. The online concierge system maintains user embeddings for users and recipe embeddings for recipes. For users who have not placed orders, recipes are recommended based on global user interactions with recipes. Users who have previously ordered items from recipes are suggested recipes selected based on a similarity of their user embedding to recipe embeddings. Users who have purchased items but not from recipes are compared to a set of similar users based on the user embeddings, and recipes with which users of the set of similar users interacted are used for identifying recipes to the users. A recipe graph may be maintained by the online concierge system to identify similarities between recipes for expanding candidate recipes to suggest to users.

    MACHINE-LEARNED MODEL FOR PERSONALIZING SERVICE OPTIONS IN AN ONLINE CONCIERGE SYSTEM USING LOCATION FEATURES
    9.
    发明申请
    MACHINE-LEARNED MODEL FOR PERSONALIZING SERVICE OPTIONS IN AN ONLINE CONCIERGE SYSTEM USING LOCATION FEATURES 有权

    公开(公告)号：US20240428309A1

    公开(公告)日：2024-12-26

    申请号：US18214150

    申请日：2023-06-26

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Robert Fletcher ,  Ramasubramanian Balasubramanian ,  Tilman Drerup ,  Sharath Rao Karikurve

    IPC: G06Q30/0601

    Abstract: Based on logged information about prior events, an online concierge system generates a set of location metrics that quantify properties of locations such as retailers at which items may be acquired, and residences to which the items are brought. The location metrics can be used for a variety of purposes to aid customers or other users of the online concierge system, such as providing the users with more information (e.g., likely delivery delays) or alternative options (e.g., pricing options), or emphasizing options that the location metrics indicate would be of particular value to the user. To determine whether to emphasize a particular option, the online concierge system applies a machine-learned model that predicts whether emphasizing that option would effect a positive change in user behavior, relative to not emphasizing it.

    USING MACHINE LEARNING TO PREDICT ACCEPTANCE OF LARGER SIZE VARIANTS
    10.
    发明公开
    USING MACHINE LEARNING TO PREDICT ACCEPTANCE OF LARGER SIZE VARIANTS 审中-公开

    公开(公告)号：US20240070745A1

    公开(公告)日：2024-02-29

    申请号：US17899190

    申请日：2022-08-30

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Sharath Rao Karikurve

    IPC: G06Q30/06 ,  G06Q30/02

    CPC classification number: G06Q30/0631 ,  G06Q30/0201 ,  G06Q30/0202 ,  G06Q30/0625

    Abstract: An online concierge system recommends a larger size variant for replacement. The system receives one or more items for an order from a user. The one or more items include a first item. The system identifies a set of candidate replacement items for the first item, and the candidate replacement items comprise one or more larger size variants. The system estimates a benefit value for each of the candidate larger size variants to replace the first item and applies a machine learned acceptance model to each candidate larger size variant to predict a likelihood that the user would accept a suggestion to replace the respective candidate larger size variant for the first item. Based on the estimated benefit value and the predicted likelihood, the system determines a larger size variant as a replacement item and sends the replacement item for display in a user interface on a user device.

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