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公开(公告)号：US20250094956A1
公开(公告)日：2025-03-20
申请号：US18961337
申请日：2024-11-26
Applicant: Maplebear Inc.
Inventor： Shiyuan Yang , Shray Chandra
IPC: G06Q20/20 , G01G19/414 , G06Q20/18 , G06T7/10 , G06T7/50 , G06V10/10 , G06V10/22 , G06V10/70 , G06V10/94 , G06V20/60 , G06V20/64 , G07G1/00 , H04N23/90
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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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.
Inventor： Shiyuan Yang , Yilin Huang , Wentao Pan , Xiao Zhou
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.
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公开(公告)号：US20250058814A1
公开(公告)日：2025-02-20
申请号：US18937402
申请日：2024-11-05
Applicant: Maplebear Inc.
Inventor： Lin Gao , Yilin Huang , Shiyuan Yang , Xiaofei Zhou , Kaiyang Chu , Sikun Zhu
Abstract: A shopping cart's tracking system determines a baseline location of the shopping cart at a first timestamp with a wireless device located on the shopping cart detecting one or more external wireless devices (e.g., RFID tags). The shopping cart's tracking system receives wheel motion data from one or more wheel sensors coupled to one or more wheels of the shopping cart, wherein the wheel motion data describes rotation and orientation of the one or more wheels. The shopping cart's tracking system calculates a translation traveled by the shopping cart from the baseline location based on the wheel motion data. The shopping cart's tracking system determines an estimated location of the shopping cart at a second timestamp based on the baseline location and the translation. The shopping cart provides functionality with the estimated location.
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公开(公告)号：US20240202694A1
公开(公告)日：2024-06-20
申请号：US18169012
申请日：2023-02-14
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Ganglu Wu , Shiyuan Yang , Xiao Zhou , Qi Wang , Qunwei Liu , Youming Luo
IPC: G06Q20/20 , G06K7/14 , G06Q30/0601 , G06T3/40 , G06V10/25
CPC classification number: G06Q20/208 , G06K7/1443 , G06Q30/0633 , G06Q30/0641 , G06T3/40 , G06V10/25
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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公开(公告)号：US20240054449A1
公开(公告)日：2024-02-15
申请号：US17936232
申请日：2022-09-28
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Lin Gao , Yilin Huang , Shiyuan Yang , Hao Wu , Ganglu Wu , Xiao Zhou
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.
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Patent Agency Ranking
公开(公告)号：US20240034381A1
公开(公告)日：2024-02-01
申请号：US17936226
申请日：2022-09-28
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Lin Gao , Yilin Huang , Shiyuan Yang , Jianbo Meng , Yakun Li , Linhua Luo , Weiting Chen
CPC classification number: B62B5/0096 , B62B3/1424 , G06Q30/06
Abstract: An automated checkout system uses a shopping cart that is automatically charged when stacked into another shopping cart. Each shopping cart has a front charging connector and a rear charging connector. When a first shopping cart is stacked into a second shopping cart, the front charging connector of the first shopping cart connects with the rear charging connector of the second shopping cart. Electrical power can flow to the first shopping cart via the second shopping cart to charge a battery of the first shopping cart. The second shopping cart may be similarly stacked into a third shopping cart, wherein the second shopping cart receives electrical power from the third shopping cart. The second shopping cart may use this electrical power to charge its own battery or may provide some or all of the electrical power to the first shopping cart to charge the first shopping cart's battery.
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公开(公告)号：US20240013185A1
公开(公告)日：2024-01-11
申请号：US17874987
申请日：2022-07-27
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Lin Gao , Yilin Huang , Shiyuan Yang , Ganglu Wu , Yang Wang , Wentao Pan
CPC classification number: G06Q20/208 , G06V10/803 , G01G19/40 , G01G19/12
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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公开(公告)号：US20240001981A1
公开(公告)日：2024-01-04
申请号：US17873526
申请日：2022-07-26
Applicant: Maplebear Inc. (dba Instacart)
Inventor： Lin Gao , Yilin Huang , Shiyuan Yang , Xiaofei Zhou , Kaiyang Chu , Sikun Zhu
CPC classification number: B62B5/0096 , B62B3/1424 , H04W4/029
Abstract: A shopping cart's tracking system determines a first baseline location of the shopping cart at a first timestamp with a wireless device located on the shopping cart detecting one or more external wireless devices (e.g., RFID tags) in the indoor environment. The shopping cart's tracking system receives wheel motion data from one or more wheel sensors coupled to one or more wheels of the shopping cart, wherein the wheel motion data describes rotation of the one or more wheels. The shopping cart's tracking system calculates a translation traveled by the shopping cart from the first baseline location based on the wheel motion data. The shopping cart's tracking system determines an estimated location of the shopping cart at a second timestamp based on the first baseline location and the translation. With the estimated location, the shopping cart can update a map with the estimated location of the shopping cart.
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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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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
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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