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    DETECTING ITEMS IN A SHOPPING CART BASED ON LOCATION OF SHOPPING CART
    11.
    发明公开
    DETECTING ITEMS IN A SHOPPING CART BASED ON LOCATION OF SHOPPING CART 审中-公开

    公开(公告)号：US20240144688A1

    公开(公告)日：2024-05-02

    申请号：US18060473

    申请日：2022-11-30

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Lin Gao ,  Yilin Huang ,  Shiyuan Yang ,  Xiaofei Zhou ,  Xiao Zhou ,  Qunwei Liu

    IPC: G06V20/52 ,  G06Q30/0601 ,  G06V10/74

    CPC classification number: G06V20/52 ,  G06Q30/0633 ,  G06V10/761

    Abstract: An automated checkout system accesses an image of an item inside a shopping cart and a location of the shopping cart within a store. The automated checkout system identifies a set of candidate items located within a threshold distance of the location of the shopping cart based on an item map. The item map describes a location of each item within the store and the location of each candidate item corresponds to a location of the candidate item on the item map. The automated checkout system inputs visual features of the item extracted from the image to a machine-learning model to identify the item by determining a similarity score between the item and each candidate item of the set of candidate items. After identifying the item, the automated checkout system displays a list comprising the item and additional items within the shopping cart to a user.

    WEIGHT MEASUREMENT ADJUSTMENT AND INHIBITION BASED ON SENSOR MEASUREMENTS
    14.
    发明申请
    WEIGHT MEASUREMENT ADJUSTMENT AND INHIBITION BASED ON SENSOR MEASUREMENTS 有权

    公开(公告)号：US20220410955A1

    公开(公告)日：2022-12-29

    申请号：US17821876

    申请日：2022-08-24

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Lin Gao ,  Michael Joseph Sanzari ,  Yilin Huang ,  Shiyuan Yang ,  Ahmed Beshry

    IPC: B62B5/00 ,  B62B3/14 ,  G01G21/00 ,  G01G19/12

    Abstract: Disclosed herein relates to a system, comprising: at least one load receiver mounted on a shopping cart or basket and configured to receive an item placed into the shopping cart or basket for a weighing operation; a plurality of sensors configured to detect a plurality of parameters relating to the weighing operation of the item including at least one of: a relative angle between a force sensing axis of the at least one load receiver and a direction of gravity, a motion of the shopping cart or basket, and an ambient temperature surrounding the shopping cart or basket and the at least one load receiver; and a processor configured to determine an actual weight of the item based on at least a portion of the plurality of parameters.

    IMAGE-BASED BARCODE DECODING
    15.
    发明申请
    IMAGE-BASED BARCODE DECODING 有权

    公开(公告)号：US20220309264A1

    公开(公告)日：2022-09-29

    申请号：US17703076

    申请日：2022-03-24

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Shiyuan Yang ,  Yilin Huang ,  Wentao Pan ,  Xiao Zhou

    IPC: G06K7/14 ,  G06K9/00 ,  G06T7/10

    Abstract: A barcode decoding system decodes item identifiers from images of barcodes. The barcode decoding system receives an image of a barcode and rotates the image to a pre-determined orientation. The barcode decoding system also may segment the barcode image to emphasize the portions of the image that correspond to the barcode. The barcode decoding system generates a binary sequence representation of the item identifier encoded in the barcode by applying a barcode classifier model to the barcode image, and decodes the item identifier from the barcode based on the binary sequence representation.

    Training a model to identify items based on image data and load curve data
    16.
    发明授权
    Training a model to identify items based on image data and load curve data 有权

    公开(公告)号：US12205098B2

    公开(公告)日：2025-01-21

    申请号：US17874956

    申请日：2022-07-27

    Applicant: Maplebear Inc.

    Inventor： Yilin Huang ,  Ganglu Wu ,  Xiao Zhou ,  Youming Luo ,  Shiyuan Yang

    IPC: G06V10/25 ,  G01G19/12 ,  G01G19/40 ,  G06Q20/20 ,  G06V10/778 ,  G06V10/80 ,  G06V20/50

    Abstract: A smart shopping cart includes internally facing cameras and an integrated scale to identify objects that are placed in the cart. To avoid unnecessary processing of images that are irrelevant, and thereby save battery life, the cart uses the scale to detect when an object is placed in the cart. The cart obtains images from a cache and sends those to an object detection machine learning model. The cart captures and sends a load curve as input to the trained model for object detection. Labeled load data and labeled image data are used by a model training system to train the machine learning model to identify an item when it is added to the shopping cart. The shopping cart also uses weight data and the image data from a timeframe associated with the addition of the item to the cart as inputs.

    Image-based barcode decoding
    17.
    发明授权
    Image-based barcode decoding 有权

    公开(公告)号：US12197998B2

    公开(公告)日：2025-01-14

    申请号：US18398739

    申请日：2023-12-28

    Applicant: Maplebear Inc.

    Inventor： Shiyuan Yang ,  Yilin Huang ,  Wentao Pan ,  Xiao Zhou

    IPC: G06K7/14 ,  G06T7/10

    Abstract: A barcode decoding system decodes item identifiers from images of barcodes. The barcode decoding system receives an image of a barcode and rotates the image to a pre-determined orientation. The barcode decoding system also may segment the barcode image to emphasize the portions of the image that correspond to the barcode. The barcode decoding system generates a binary sequence representation of the item identifier encoded in the barcode by applying a barcode classifier model to the barcode image, and decodes the item identifier from the barcode based on the binary sequence representation.

    IMAGE-BASED BARCODE DECODING
    18.
    发明公开
    IMAGE-BASED BARCODE DECODING 审中-公开

    公开(公告)号：US20240135123A1

    公开(公告)日：2024-04-25

    申请号：US18398739

    申请日：2023-12-28

    Applicant: Maplebear Inc.

    Inventor： Shiyuan Yang ,  Yilin Huang ,  Wentao Pan ,  Xiao Zhou

    IPC: G06K7/14 ,  G06T7/10

    CPC classification number: G06K7/1413 ,  G06T7/10 ,  G06F2218/12 ,  G06T2207/20081

    Abstract: A barcode decoding system decodes item identifiers from images of barcodes. The barcode decoding system receives an image of a barcode and rotates the image to a pre-determined orientation. The barcode decoding system also may segment the barcode image to emphasize the portions of the image that correspond to the barcode. The barcode decoding system generates a binary sequence representation of the item identifier encoded in the barcode by applying a barcode classifier model to the barcode image, and decodes the item identifier from the barcode based on the binary sequence representation.

    TRAINING A MACHINE-LEARNING MODEL TO PREDICT LOCATION USING WHEEL MOTION DATA
    19.
    发明公开
    TRAINING A MACHINE-LEARNING MODEL TO PREDICT LOCATION USING WHEEL MOTION DATA 审中-公开

    公开(公告)号：US20240003707A1

    公开(公告)日：2024-01-04

    申请号：US17873528

    申请日：2022-07-26

    Applicant: Maplebear Inc. (dba Instacart)

    Inventor： Lin Gao ,  Yilin Huang ,  Shiyuan Yang ,  Xiaofei Zhou ,  Kaiyang Chu ,  Sikun Zhu

    IPC: G01C21/00 ,  G07C5/04 ,  G01S5/00 ,  G01C21/16

    CPC classification number: G01C21/383 ,  G01C21/16 ,  G01S5/0036 ,  G07C5/04

    Abstract: A shopping cart's tracking system receives wheel motion data from a plurality of wheel sensors coupled to a plurality of wheels of the shopping cart, wherein the wheel motion data describes rotation of the plurality of wheels and orientation of the plurality of wheels. The tracking system predicts an estimated location of the shopping cart by applying a machine-learning location model to the wheel motion data. The machine-learning location model is trained with training examples that are generated by: receiving prior wheel motion data from the plurality of wheel sensors, partitioning the prior wheel motion data into a plurality of segments using a time window, receiving one or more baseline locations at one or more prior timestamps, and generating one or more training examples, each training example comprising a segment of prior wheel motion data and a baseline location with a timestamp overlapping the segment.

    TRAINING A MODEL TO IDENTIFY ITEMS BASED ON IMAGE DATA AND LOAD CURVE DATA
    20.
    发明申请
    TRAINING A MODEL TO IDENTIFY ITEMS BASED ON IMAGE DATA AND LOAD CURVE DATA 有权

    公开(公告)号：US20250104040A1

    公开(公告)日：2025-03-27

    申请号：US18974543

    申请日：2024-12-09

    Applicant: Maplebear Inc.

    Inventor： Yilin Huang ,  Ganglu Wu ,  Xiao Zhou ,  Youming Luo ,  Shiyuan Yang

    IPC: G06Q20/20 ,  G01G19/12 ,  G01G19/40 ,  G06V10/25 ,  G06V10/778 ,  G06V10/80 ,  G06V20/50

    Abstract: A smart shopping cart includes internally facing cameras and an integrated scale to identify objects that are placed in the cart. To avoid unnecessary processing of images that are irrelevant, and thereby save battery life, the cart uses the scale to detect when an object is placed in the cart. The cart obtains images from a cache and sends those to an object detection machine learning model. The cart captures and sends a load curve as input to the trained model for object detection. Labeled load data and labeled image data are used by a model training system to train the machine learning model to identify an item when it is added to the shopping cart. The shopping cart also uses weight data and the image data from a timeframe associated with the addition of the item to the cart as inputs.

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