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    DOMAIN-ADAPTIVE CONTENT SUGGESTION FOR AN ONLINE CONCIERGE SYSTEM
    33.
    发明公开
    DOMAIN-ADAPTIVE CONTENT SUGGESTION FOR AN ONLINE CONCIERGE SYSTEM 审中-公开

    公开(公告)号：US20230186361A1

    公开(公告)日：2023-06-15

    申请号：US17550960

    申请日：2021-12-14

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Saurav Manchanda ,  Ramasubramanian Balasubramanian

    IPC: G06Q30/06 ,  G06Q30/02

    CPC classification number: G06Q30/0619 ,  G06Q30/0282 ,  G06Q30/0641

    Abstract: An online concierge system uses a domain-adaptive suggestion module to score products that may be presented to a user as suggestions in response to a user’s search query. The domain-adaptive suggestion module receives data that is relevant to scoring products as suggestions in response to a search query. The domain-adaptive suggestion module uses one or more domain-neutral representation models to generate a domain-neutral representation of the received data. The domain-neutral representation is a featurized representation of the received data that can be used by machine-learning models in the search domain or the suggestion domain. The domain-adaptive suggestion module then scores products by applying one or more machine-learning models to domain-neutral representations generated based on those products. By using domain-neutral representations, the domain-adaptive suggestion module can be trained based on training examples from a similar prediction task in a different domain.

    MACHINE LEARNING MODEL FOR CLICK THROUGH RATE PREDICTION USING THREE VECTOR REPRESENTATIONS
    35.
    发明申请
    MACHINE LEARNING MODEL FOR CLICK THROUGH RATE PREDICTION USING THREE VECTOR REPRESENTATIONS 有权

    公开(公告)号：US20230135683A1

    公开(公告)日：2023-05-04

    申请号：US17513739

    申请日：2021-10-28

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Ramasubramanian Balasubramanian ,  Saurav Manchanda

    IPC: G06Q30/06 ,  G06Q30/02

    Abstract: An online concierge system uses a machine learning click through rate model to select promoted items based on user embeddings, item embeddings, and search query embeddings. Embeddings obtained by an embedding model may be used as inputs to the click through rate model. The embedding model may be trained using different actions to score the strength of a customer interaction with an item. For example, a customer purchasing an item may be a stronger signal than a customer placing an item in a shopping cart, which in turn may be a stronger signal than a customer clicking on an item. The online concierge system generates a ranking of candidate promoted items based on the search query and using the click through rate model. Based on the ranking, the online concierge system displays promoted items along with the organic search results to the customer.

    RECEIPT CONTENT CAPTURE DEVICE FOR INVENTORY TRACKING
    36.
    发明申请
    RECEIPT CONTENT CAPTURE DEVICE FOR INVENTORY TRACKING 有权

    公开(公告)号：US20230091975A1

    公开(公告)日：2023-03-23

    申请号：US18071649

    申请日：2022-11-30

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Robert Russel Adams

    IPC: G06Q10/08 ,  G06F16/25 ,  G06Q20/20 ,  G06Q40/00 ,  G06Q20/04

    Abstract: A receipt capture device can collect transaction information from transactions conducted at a point of sale system by capturing receipt data transmitted from the point of sale system for the purpose of printing receipts at an external receipt printer. The receipt capture device can then send the collected receipt data to an online system for analysis. At the online system, received receipt data can be decoded from the printer-readable format it is transmitted in and used to enhance the online system's understanding of transactions occurring at a retailer associated with the point of sale system. For example, the online system can determine an approximate inventory of items available at purchase at the retailer by aggregating items recently purchased in transactions at the point of sale system.

    TRAINING A MODEL TO PREDICT TRAVEL DISTANCE BETWEEN TWO GEOGRAPHIC LOCATIONS
    39.
    发明申请
    TRAINING A MODEL TO PREDICT TRAVEL DISTANCE BETWEEN TWO GEOGRAPHIC LOCATIONS 有权

    公开(公告)号：US20220391965A1

    公开(公告)日：2022-12-08

    申请号：US17338421

    申请日：2021-06-03

    Applicant: Maplebear, Inc.(dba Instacart)

    Inventor： Jagannath Putrevu ,  Reza Faturechi

    IPC: G06Q30/06 ,  G06Q10/08 ,  G01C21/20 ,  G06K9/62 ,  G06N3/08

    Abstract: An online concierge system receives orders from users and assigns orders to shoppers for fulfillment. Each order specifies a destination location and a warehouse from which items in the order are obtained. When assigning orders to shoppers, the online concierge system seeks to minimize distances traveled by shoppers fulfilling orders. To more efficiently assign orders to shoppers, the online concierge system trains a distance prediction model to predict a distance traveled between a starting location and a destination location from the starting location, the destination location, and a Haversine distance between the destination location and the starting location. Information identifying distances traveled by shoppers when fulfilling previous orders or information about distances between locations from a third party system may be used to train the distance prediction model.

    OVERLAP DETECTION FOR AN ITEM RECOGNITION SYSTEM
    40.
    发明申请
    OVERLAP DETECTION FOR AN ITEM RECOGNITION SYSTEM 有权

    公开(公告)号：US20220343308A1

    公开(公告)日：2022-10-27

    申请号：US17726389

    申请日：2022-04-21

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Shiyuan Yang ,  Shray Chandra

    IPC: G06Q20/20 ,  H04N5/247 ,  G06V10/22 ,  G06V10/10 ,  G06V10/70 ,  G06T7/50 ,  G06V10/94 ,  G06V20/60 ,  G06Q20/18 ,  G01G19/414

    Abstract: An item recognition system uses a top camera and one or more peripheral cameras to identify items. The item recognition system may use image embeddings generated based on images captured by the cameras to generate a concatenated embedding that describes an item depicted in the image. The item recognition system may compare the concatenated embedding to reference embeddings to identify the item. Furthermore, the item recognition system may detect when items are overlapping in an image. For example, the item recognition system may apply an overlap detection model to a top image and a pixel-wise mask for the top image to detect whether an item is overlapping with another in the top image. The item recognition system notifies a user of the overlap if detected.

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