{
  "$type": "site.standard.document",
  "description": "Systems and methods for using machine learning classifiers to identify anomalous driving behavior in vehicle driver data obtained from vehicle telematics devices are provided. In one example, a vehicle telematics device receives vehicle driver data from sensors, identifies anomalies in the vehicle…",
  "path": "/patents/1299957",
  "publishedAt": "2021-09-16T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "B60W40/09",
    "CALAMP CORP."
  ],
  "textContent": "Systems and methods for using machine learning classifiers to identify anomalous driving behavior in vehicle driver data obtained from vehicle telematics devices are provided. In one example, a vehicle telematics device receives vehicle driver data from sensors, identifies anomalies in the vehicle driver data by using an unsupervised machine learning process, calculates a driver risk score by using the anomalies identified in the vehicle driver data, and transmits the risk score to a remote server system. In another example, a server system receives vehicle driver data from a plurality of vehicle telematics devices, identifies anomalies in the vehicle driver data by using an unsupervised machine learning process, and calculates a driver risk score by using the anomalies identified in the vehicle driver data.",
  "title": "SYSTEMS AND METHODS FOR DRIVER SCORING WITH MACHINE LEARNING"
}