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    Item type discovery and classification using machine learning
    2.
    发明授权
    Item type discovery and classification using machine learning 有权

    公开(公告)号：US11321580B1

    公开(公告)日：2022-05-03

    申请号：US16673769

    申请日：2019-11-04

    Applicant: Amazon Technologies, Inc.

    Inventor： Krzysztof Marcin Walczak ,  Emilio Ian Maldonado ,  Bella Dubrov

    IPC: G06K9/62 ,  G06F16/957 ,  G06F40/284 ,  G06N3/08 ,  G06N3/04

    Abstract: Systems and methods are provided for learning item types of items listed in an electronic repository, and for training a machine learning model to predict the item type of a given input item. For example, a machine learning model may be obtained or accessed that has been previously trained to classify an input item to a browse node. Vector representations of individual items assigned to different browse nodes may be obtained from an intermediate layer of the previously trained machine learning model, and a vector representation of individual browse nodes may then be generated based on the vector representations of individual items assigned to that browse node. A clustering algorithm may be applied to the browse node vector representations in order to identify clusters of similar browse nodes, where individual clusters may represent different unique item types.

    Management of sensor failure in a facility
    7.
    发明授权
    Management of sensor failure in a facility 有权

    公开(公告)号：US11412185B1

    公开(公告)日：2022-08-09

    申请号：US17128774

    申请日：2020-12-21

    Applicant: AMAZON TECHNOLOGIES, INC.

    Inventor： Emilio Ian Maldonado ,  Daniel Bibireata ,  Nishitkumar Ashokkumar Desai ,  Yasser Baseer Asmi ,  Xiaofeng Ren ,  Jaechul Kim

    IPC: H04N7/18 ,  H04N17/00

    Abstract: Sensors in a facility generate sensor data associated with a region of the facility, which can be used to determine a 3D location of an object in the facility. Some sensors may sense overlapping regions of the facility. For example, a first sensor may generate data associated with a first region of the facility, while a second sensor may generate data associated with a second region of the facility that partially overlaps the first region. Sensors may fail at times as determined from sensor output data or status data. In response to identifying a failed sensor, an undetected region corresponding to the failed sensor is identified, as well as a substitute sensor that partially senses the undetected region. Sensor data from the substitute sensor, such as 2D data, is acquired and used to estimate a 3D location of an object in the undetected region.

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