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    GENERATING SIGNALS FOR MACHINE LEARNING, DISPLAYING CONTENT, OR DETERMINING USER PREFERENCES BASED ON VIDEO DATA CAPTURED WITHIN A RETAILER LOCATION
    201.
    发明公开
    GENERATING SIGNALS FOR MACHINE LEARNING, DISPLAYING CONTENT, OR DETERMINING USER PREFERENCES BASED ON VIDEO DATA CAPTURED WITHIN A RETAILER LOCATION 审中-公开

    公开(公告)号：US20240362678A1

    公开(公告)日：2024-10-31

    申请号：US18141396

    申请日：2023-04-29

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Chakshu Ahuja ,  Girija Narlikar ,  Karuna Ahuja

    IPC: G06Q30/0251 ,  G06N20/00

    CPC classification number: G06Q30/0261 ,  G06N20/00

    Abstract: For each retailer location associated with multiple retailers, an online system associated with the retailers receives video data captured within the retailer location by a camera of a client device associated with an online system user. The online system detects, based at least in part on the video data, a location associated with the user within the retailer location and/or an interaction by the user with an item included among an inventory of the retailer location. The online system generates a set of signals associated with the user based at least in part on the detection of the location and/or the interaction. Based at least in part on the set of signals, the online system determines a set of preferences associated with the user, trains a machine learning model to predict a metric associated with the user, and/or sends content for display to a client device associated with the user.

    Machine Learning Model Trained to Predict User Interactions with Items for Inventory Assortment
    202.
    发明公开
    Machine Learning Model Trained to Predict User Interactions with Items for Inventory Assortment 审中-公开

    公开(公告)号：US20240362582A1

    公开(公告)日：2024-10-31

    申请号：US18141398

    申请日：2023-04-29

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Haochen Luo ,  Kenneth Jason Sanchez ,  Eric Hermann

    IPC: G06Q10/087

    CPC classification number: G06Q10/087

    Abstract: An inventory interaction model predicts user interactions with items to be included in an item assortment in a warehouse. The item is described with features that include the co-located items and the respective user interactions, so that the item interactions for the evaluated item incorporate item-item effects in its predictions. To train the model effectively in the absence of prior interaction data for an item, training examples are generated from existing item and user interaction data of co-located items by selecting a portion of the items for the examples and including co-located item data, labeling the training example output with item interactions for the item. The trained model is then applied for an item assortment by describing co-located item features of the item assortment in evaluating candidate items.

    User Interface Arranging Groups of Items by Similarity for User Selection
    203.
    发明公开
    User Interface Arranging Groups of Items by Similarity for User Selection 审中-公开

    公开(公告)号：US20240354828A1

    公开(公告)日：2024-10-24

    申请号：US18137404

    申请日：2023-04-20

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Luis Manrique ,  Sanchit Gupta ,  Aref Kashani Nejad ,  Diego Goyret ,  Kurtis Mirick ,  Joshua Roberts

    IPC: G06Q30/0601

    CPC classification number: G06Q30/0631 ,  G06Q30/0641

    Abstract: An online system receives a request from a user to access an ordering interface for a retailer and identifies a retailer location based on the user's location. The system uses a machine learning model to predict availabilities of items at the retailer location and identifies anchor items the user previously ordered from the retailer that are likely available. The system computes a first score for each anchor item based on an expected value associated with it and/or a likelihood the user will re-order it, determines categories associated with the anchor items, and ranks the categories based on the first score. For each category, the system identifies associated candidate items likely to be available and ranks them based on a second score for each candidate item computed based on a probability of user satisfaction with it as an anchor item replacement. The ordering interface is then generated based on the rankings.

    CONTENT SELECTION WITH INTER-SESSION REWARDS IN REINFORCEMENT LEARNING
    205.
    发明公开
    CONTENT SELECTION WITH INTER-SESSION REWARDS IN REINFORCEMENT LEARNING 审中-公开

    公开(公告)号：US20240330695A1

    公开(公告)日：2024-10-03

    申请号：US18129023

    申请日：2023-03-30

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Saurav Manchanda ,  Ramasubramanian Balasubramanian

    IPC: G06N3/092 ,  G06N3/04

    CPC classification number: G06N3/092 ,  G06N3/04

    Abstract: A reinforcement learning model selects a content composition based, in part, on inter-session rewards. In addition to near-in-time rewards of user interactions with a content composition for evaluating possible actions, the reinforcement learning model also generates a reward and/or penalty based on between-session information, such as the time between sessions. This permits the reinforcement learning model to learn to evaluate content compositions not only on the immediate user response, but also on the effect of future user engagement. To determine a composition for a search query, the reinforcement learning model generates a state representation of the user and search query and evaluates candidate content compositions based on learned parameters of the reinforcement learning model that evaluates inter-session rewards of the content compositions.

    SIMULATED TRAINING DATA GENERATION FOR A MULTI-ARMED BANDIT MODEL
    206.
    发明公开
    SIMULATED TRAINING DATA GENERATION FOR A MULTI-ARMED BANDIT MODEL 审中-公开

    公开(公告)号：US20240290501A1

    公开(公告)日：2024-08-29

    申请号：US18175723

    申请日：2023-02-28

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Xiao Gong ,  Konrad Gustav Miziolek

    IPC: G16H50/50 ,  G06N3/08

    CPC classification number: G16H50/50 ,  G06N3/08

    Abstract: An online system adjusts a guardrail setting used by a user treatment engine based on conditions faced by the online system. The online system simulates the performance of the user treatment engine using different candidate guardrail settings and computes a score for each of the guardrail settings based on the performance of the user treatment engine using each of the guardrail settings. The online system selects a new guardrail setting for the user treatment engine based on the performance scores for the candidate guardrail settings. Furthermore, the online system generates simulated training examples to initially train a user treatment engine. The online system uses a treatment performance model to simulate the effect of treatments applied to users and generates simulated training examples based on the predicted effect of the treatments. The online system retrains the user treatment engine on real training examples that are generated based on actual treatments.

    DETERMINING ITEM DESIRABILITY TO USERS BASED ON ITEM ATTRIBUTES AND ITEM EXPIRATION DATE
    207.
    发明公开
    DETERMINING ITEM DESIRABILITY TO USERS BASED ON ITEM ATTRIBUTES AND ITEM EXPIRATION DATE 审中-公开

    公开(公告)号：US20240289857A1

    公开(公告)日：2024-08-29

    申请号：US18113874

    申请日：2023-02-24

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Shaun Navin Maharaj ,  Brent Scheibelhut ,  Mark Oberemk

    IPC: G06Q30/0601

    CPC classification number: G06Q30/0623 ,  G06Q30/0603

    Abstract: An online concierge system delivers items from multiple retailers to customers. To avoid delivery of expired or near-expired items, the online concierge system obtains attributes of items offered by a retailer, such as from images of items at the retailer from client devices and uses a trained desirability model to predict a desirability score of an item based on the item's attributes. The desirability model is trained using training examples with labels indicating whether an item was suitable for inclusion in an order. The desirability model may be used to determine if an item is suitable for inclusion in an order, to provide suggestions for a retailer for using the item, or to select a retailer for fulfilling an order.

    TRAINED MACHINE LEARNING MODELS FOR PREDICTING REPLACEMENT ITEMS USING EXPIRATION DATES
    208.
    发明公开
    TRAINED MACHINE LEARNING MODELS FOR PREDICTING REPLACEMENT ITEMS USING EXPIRATION DATES 审中-公开

    公开(公告)号：US20240289855A1

    公开(公告)日：2024-08-29

    申请号：US18113965

    申请日：2023-02-24

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Ramasubramanian Balasubramanian ,  Sharath Rao Karikurve

    IPC: G06Q30/0601 ,  G06N3/08

    CPC classification number: G06Q30/0613 ,  G06N3/08

    Abstract: A specific item is identified to suggest a replacement therefor to a user. A set of candidate replacement items for the specific item is determined. For at least one of the candidate replacement items, an expiration score is determined based on expiration information associated with the item. A replacement score for the candidate replacement item is determined by inputting the determined expiration score as a feature into a machine learning model that is trained using features of historical samples of candidate replacement items suggested as a replacement to users and the replacement suggestion being accepted by the users. One or more of the candidate replacement items is selected based on respective replacement scores as one or more suggested replacement items. A graphical user interface of a client device of the user is caused to display the one or more suggested replacement items as the replacement for the specific item.

    DYNAMIC GUARDRAIL ADJUSTMENTS FOR A MULTI-ARMED BANDIT MODEL
    209.
    发明公开
    DYNAMIC GUARDRAIL ADJUSTMENTS FOR A MULTI-ARMED BANDIT MODEL 审中-公开

    公开(公告)号：US20240289853A1

    公开(公告)日：2024-08-29

    申请号：US18175720

    申请日：2023-02-28

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Xiao Gong ,  Konrad Gustav Miziolek

    IPC: G06Q30/0601 ,  G06Q30/0202

    CPC classification number: G06Q30/0601 ,  G06Q30/0202

    Abstract: An online system adjusts a guardrail setting used by a user treatment engine based on conditions faced by the online system. The online system simulates the performance of the user treatment engine using different candidate guardrail settings and computes a score for each of the guardrail settings based on the performance of the user treatment engine using each of the guardrail settings. The online system selects a new guardrail setting for the user treatment engine based on the performance scores for the candidate guardrail settings. Furthermore, the online system generates simulated training examples to initially train a user treatment engine. The online system uses a treatment performance model to simulate the effect of treatments applied to users and generates simulated training examples based on the predicted effect of the treatments. The online system retrains the user treatment engine on real training examples that are generated based on actual treatments.

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