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    RECEIPT CONTENT CAPTURE DEVICE FOR INVENTORY TRACKING
    82.
    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.

    Predictive inventory availability
    85.
    发明授权
    Predictive inventory availability 有权

    公开(公告)号：US11544810B2

    公开(公告)日：2023-01-03

    申请号：US15885492

    申请日：2018-01-31

    Applicant: Maplebear, Inc.

    Inventor： Sharath Rao ,  Shishir Prasad ,  Jeremy Stanley

    IPC: G06Q50/28 ,  G06Q10/06 ,  G06Q10/08

    Abstract: A method for predicting inventory availability, involving receiving a delivery order including a plurality of items and a delivery location, and identifying a warehouse for picking the plurality of items. The method retrieves a machine-learned model that predicts a probability that an item is available at the warehouse. The machine-learned model is trained, using machine learning, based in part on a plurality of datasets. The plurality of datasets include data describing items included in previous delivery orders, whether each item in each previous delivery order was picked, a warehouse associated with each previous delivery order, and a plurality of characteristics associated with each of the items. The method predicts the probability that one of the plurality of items in the delivery order is available at the warehouse, and generates an instruction to a picker based on the probability. An instruction is transmitted to a mobile device of the picker.

    TRAINING A MODEL TO PREDICT TRAVEL DISTANCE BETWEEN TWO GEOGRAPHIC LOCATIONS
    87.
    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
    89.
    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.

    CLUSTERING ITEMS OFFERED BY AN ONLINE CONCIERGE SYSTEM TO CREATE AND TO RECOMMEND COLLECTIONS OF ITEMS TO USERS
    90.
    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.

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