Bxb Digital Pty Limited

Michael D Souder

Ryan Daigle

Juan Castillo

Prasad Srinivasamurthy

Shankar Jayaraman

Daniel Bricarello

Raj Valame

Sandeep K Bandil

Vikram Vipul

Dan Bricarello

Provided are methods, including computer-implemented methods, devices, and computer-program products applying systems and methods for facility matching and localization. According to some embodiments of the invention, the location of a pallet at a facility within a supply chain may be determined from location data received from a beacon on the pallet. In some embodiments, the position of the pallet within the facility may also be determined. Thus, the status of pallets may be monitored as they move through the supply chain.

Systems and methods for facility matching and localization

Provided are methods, including computer-implemented methods, devices, and computer-program products applying systems and methods for pallet identification. According to some embodiments of the invention, a pallet may be visually identified through photographs without attaching physical labels. Thus, the status of pallets may be monitored (e.g., their location and structural integrity) as they move through the supply chain.

Systems and methods for pallet identification

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

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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Fremont, CA, United States of America

Michael D Souder