Patent ID: 11972388
Assignee: COX COMMUNICATIONS, INC.
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A method, comprising:
obtaining, by one or more processors of a device, historical asset tracking data for previous assets;
obtaining, by the one or more processors of the device, historical external data associated with transportation of the previous assets;
analyzing, by the one or more processors of the device, the historical asset tracking data and the historical external data to determine historical outcomes; and
training, by the one or more processors of the device, a machine learning model that is usable to determine a probability of failure and a mode of failure using the historical asset tracking data, the historical external data, and the historical outcomes;
obtaining, by the one or more processors of the device, first sensor data collected from a first tracking device associated with a first asset;
determining, by the one or more processors of the device, a first set of external data associated with the first asset;
determining, by the one or more processors of the device, using the machine learning model, and based at least in part on the first sensor data and the first set of external data, a first probability of failure associated with a first mode of failure and a second probability of failure associated with a second mode of failure;
re-training the machine learning model based on the first probability of failure and the second probability of failure; and
responsive to a determination, by the one or more processors of the device, that the first probability of failure exceeds a first failure threshold and the second probability of failure is less than a second failure threshold:
determining, by the one or more processors of the device and using the machine learning model, a first mitigation for the first mode of failure and a second mitigation for the second mode of failure that are available based at least in part on the first sensor data and the first set of external data;
determining, by the one or more processors of the device and using the machine learning model, one or more first real-world actions corresponding to the first mitigation that, if performed, remediate the first mode of failure, and one or more second real-world actions corresponding to the second mitigation that, if performed, remediate the second mode of failure; and
causing, by the one or more processors of the device, the one or more first real-world actions to be performed instead of the one or more second real-world actions based on the first probability of failure exceeding the first failure threshold and the second probability of failure being less than the second failure threshold.