Smart physical closure in supply chain

    公开(公告)号:US11062256B2

    公开(公告)日:2021-07-13

    申请号:US16800691

    申请日:2020-02-25

    Abstract: A sensor tag for attachment to at least a portion of a tracked container is disclosed. The sensor tag comprises a wireless transceiver, a logistics sensor, a power source, and a breakable link. The wireless transceiver reports an electronic identifier for the sensor tag. The power source provides power to the sensor tag. The breakable link removably attaches to the container. Removal of the sensor tag from the tracked container is determined with data from the logistics sensor. The removal of the sensor tag is reported by the wireless transceiver away from the sensor tag.

    SMART PHYSICAL CLOSURE IN SUPPLY CHAIN
    5.
    发明申请

    公开(公告)号:US20200272986A1

    公开(公告)日:2020-08-27

    申请号:US16800691

    申请日:2020-02-25

    Abstract: A sensor tag for attachment to at least a portion of a tracked container is disclosed. The sensor tag comprises a wireless transceiver, a logistics sensor, a power source, and a breakable link. The wireless transceiver reports an electronic identifier for the sensor tag. The power source provides power to the sensor tag. The breakable link removably attaches to the container. Removal of the sensor tag from the tracked container is determined with data from the logistics sensor. The removal of the sensor tag is reported by the wireless transceiver away from the sensor tag.

    SYSTEMS AND METHODS FOR TRACKING GOODS CARRIERS

    公开(公告)号:US20190122173A1

    公开(公告)日:2019-04-25

    申请号:US16059455

    申请日:2018-08-09

    Abstract: Provided are methods, devices, and computer-program products for tracking goods carriers from a particular source. According to some embodiments of the invention, a computer-implemented method includes training an artificial neural network to count the number of goods carriers from a particular source within an image. Further, the method includes receiving a first image file generated by a first imaging device; using the trained artificial neural network to determine a first number of goods carriers from the particular source in the first image; receiving a second image file generated by a second imaging device; using the trained artificial neural network to determine a second number of goods carriers from the particular source in the second image; and determining whether the first number of goods carriers from the particular source in the first image is equal to the second number of goods carriers from the particular source in the second image.

    PLACEMENT OF TRACKING DEVICES ON PALLETS

    公开(公告)号:US20210142275A1

    公开(公告)日:2021-05-13

    申请号:US17135616

    申请日:2020-12-28

    Abstract: Various approaches for attaching tracking devices to pallets allow pallets to be remotely tracked through any phase of a transportation lifecycle. In a first approach, one or more electronic components of a tracking device may be disposed within a cavity of a block of a pallet. In a second approach, one or more electronic components of a tracking device may be disposed within a strut that interfaces between two beams of a pallet. In a third approach, one or more electronic components of a tracking device may be disposed within a cavity of a beam.

    Systems and methods for tracking goods carriers

    公开(公告)号:US10956854B2

    公开(公告)日:2021-03-23

    申请号:US16059455

    申请日:2018-08-09

    Abstract: Provided are methods, devices, and computer-program products for tracking goods carriers from a particular source. According to some embodiments of the invention, a computer-implemented method includes training an artificial neural network to count the number of goods carriers from a particular source within an image. Further, the method includes receiving a first image file generated by a first imaging device; using the trained artificial neural network to determine a first number of goods carriers from the particular source in the first image; receiving a second image file generated by a second imaging device; using the trained artificial neural network to determine a second number of goods carriers from the particular source in the second image; and determining whether the first number of goods carriers from the particular source in the first image is equal to the second number of goods carriers from the particular source in the second image.

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