CalAmp. has filed a patent for technologies aimed at reducing event notifications in telematics systems. The system includes a telematics server, device, and notification manager that analyze data to determine unexpected events in vehicles. Notifications are generated based on historical data and transmitted to a notification device. GlobalData’s report on CalAmp gives a 360-degree view of the company including its patenting strategy. Buy the report here.
According to GlobalData’s company profile on CalAmp, Vehicle fault diagnosis automation was a key innovation area identified from patents. CalAmp's grant share as of January 2024 was 69%. Grant share is based on the ratio of number of grants to total number of patents.
Telematics system for reducing event notifications in vehicles

The patent application (Publication Number: US20230421930A1) describes a telematics server designed to provide notifications to a telematics device in a vehicle. The server includes an event model with rules based on historical telematics data, a device interface to receive vehicle characteristics, and a notification manager to analyze data and generate notifications for unexpected events. The notifications are then transmitted to a notification device, potentially instructing it to generate an alert or provide information related to the event. The system can update the event model using machine learning based on data received from the telematics device or feedback from the notification device.
Furthermore, the patent application outlines a method for providing notifications to a telematics device, involving receiving telematics data, analyzing it using the event model to detect unexpected events, generating notifications, and transmitting them to a notification device. The method also includes updating the event model based on incoming data or feedback using a machine learning engine. The analysis of telematics data can be based on various rules within the event model, such as temporal-based, location-based, historical-based, sequential-based, or relational-based rules, each defining an expected event or characteristic based on historical data. This approach aims to enhance the monitoring and notification capabilities of telematics systems in vehicles.
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