US 11,954,636 B2
Techniques for programmatically updating the status of assets tracked in a supply chain environment
Alexis Cohen, Atlanta, GA (US); Jacob Mapel, Atlanta, GA (US); Hunter Shinn, Atlanta, GA (US); Zach Ritter, Atlanta, GA (US); David Lee, Atlanta, GA (US); Amanda Marotti, Atlanta, GA (US); and Barbara Hernandez, Atlanta, GA (US)
Assigned to Cox Communications, Inc., Atlanta, GA (US)
Filed by Cox Communications, Inc., Atlanta, GA (US)
Filed on Jun. 29, 2022, as Appl. No. 17/809,857.
Prior Publication US 2024/0005261 A1, Jan. 4, 2024
Int. Cl. G06Q 10/0833 (2023.01); G06F 9/448 (2018.01)
CPC G06Q 10/0833 (2013.01) [G06F 9/4498 (2018.02)] 16 Claims
OG exemplary drawing
 
1. A method, comprising:
obtaining, by one or more processors of a device, sensor data collected from a tracking device associated with a first asset, wherein the sensor data includes indirect sensor data, and wherein the indirect sensor data includes at least one of environmental and temperature data;
determining, by the one or more processors of the device, asset workflow information associated with the first asset, wherein the asset workflow information indicates an expected workflow associated with the first asset;
training an intelligence engine configured to generate an inference of a status associated with the first asset;
using, by the one or more processors of the device, the intelligence engine to generate an inferred state based on the indirect sensor data and the asset workflow information;
determining, by the one or more processors of the device, the status associated with the first asset based on the inferred state;
storing, by the one or more processors of the device, the status of the first asset in an asset tracking database;
determining historical sensor data associated with the first asset, wherein the historical sensor data includes direct sensor data;
determining, by the one or more processors of the device and based on the historical sensor data, ground truth data for the intelligence engine;
comparing the inferred state and the ground truth data; and
re-training, by the one or more processors of the device, the intelligence engine based on comparing the inferred state and the ground truth data.