IMAGE-BASED BARCODE DECODING
    1.
    发明申请

    公开(公告)号:US20250094749A1

    公开(公告)日:2025-03-20

    申请号:US18969074

    申请日:2024-12-04

    Applicant: Maplebear Inc.

    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.

    CART-BASED AVAILABILITY DETERMINATION FOR AN ONLINE CONCIERGE SYSTEM

    公开(公告)号:US20240054449A1

    公开(公告)日:2024-02-15

    申请号:US17936232

    申请日:2022-09-28

    CPC classification number: G06Q10/087 G06V20/52

    Abstract: An online concierge system may use images received from shopping carts within retailers to determine the availability of items within those retailers. A shopping cart includes externally-facing cameras that automatically capture images of the area around the shopping cart as the shopping cart travels through a retailer. The online concierge system receives these images, which depict displays within the retailers from which a picker or a retailer patron can collect items. The online concierge system determines which items should be depicted in the images and which items are actually depicted in the images. The online concierge system identifies which items should be depicted, but are not depicted, and determines that these items are unavailable (e.g., out of stock) at that retailer. The online concierge system updates an availability database to indicate that these items are unavailable and may notify the retailer that the item is unavailable.

    TRAINING A MODEL TO IDENTIFY ITEMS BASED ON IMAGE DATA AND LOAD CURVE DATA

    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.

    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
    5.
    发明授权

    公开(公告)号:US12050960B2

    公开(公告)日:2024-07-30

    申请号:US17703076

    申请日:2022-03-24

    Applicant: Maplebear Inc.

    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 model to identify items based on image data and load curve data

    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.

    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
    8.
    发明授权

    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.

    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
    9.
    发明公开

    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.

    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.

    Subregion transformation for label decoding by an automated checkout system

    公开(公告)号:US12299529B2

    公开(公告)日:2025-05-13

    申请号:US18587719

    申请日:2024-02-26

    Applicant: Maplebear Inc.

    Abstract: An automated checkout system modifies received images of machine-readable labels to improve the performance of a label detection model that the system uses to decode item identifiers encoded in the machine-readable labels. For example, the automated checkout system may transform subregions of an image of a machine-readable label to adjust for distortions in the image's depiction of the machine-readable label. Similarly, the automated checkout system may identify readable regions within received images of machine-readable labels and apply a label detection model to those readable regions. By modifying received images of machine-readable labels, these techniques improve on existing computer-vision technologies by allowing for the effective decoding of machine-readable labels based on real-world images using relatively clean training data.

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