{
  "$type": "site.standard.document",
  "description": "Examples provide systems and methods for detecting vehicle tamper events without relying on a clear line-of-sight. Namely, examples leverage an intelligent insight that many types of vehicle tamper events have unique audio signatures. Accordingly, examples detect/classify vehicle tamper events…",
  "path": "/patents/1376043",
  "publishedAt": "2025-06-26T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "B60R25/1004",
    "SNTNL LLC dba Canopy"
  ],
  "textContent": "Examples provide systems and methods for detecting vehicle tamper events without relying on a clear line-of-sight. Namely, examples leverage an intelligent insight that many types of vehicle tamper events have unique audio signatures. Accordingly, examples detect/classify vehicle tamper events based on these unique audio signatures. Moreover, examples can verify these audio-based classifications by analyzing acceleration-related data (e.g., relative acceleration data for a body of a vehicle, relative jerk data for a body of a vehicle, etc.) to determine suspicious movement of a body of a vehicle during a potential/suspected vehicle tamper event. This acceleration-related verification step can reduce occurrence of false positive audio-based classifications caused by other noise events proximate to the vehicle that have similar audio signatures to vehicle tamper events (e.g., drilling or other noise from a construction site, rain, etc.).",
  "title": "SYSTEMS AND METHODS OF USING A MACHINE LEARNING MODEL TO CLASSIFY CATALYTIC CONVERTER THEFT TAMPER EVENTS BASED ON AUDIO DATA"
}