Machine Learning Localization for Vehicle Seating Zones

The patent applies segmentation by dividing the calibration process into device-specific segments. Instead of applying a single uniform calibration across all devices, the system collects calibration data separately for each device type and trains individual machine learning models for each device. This allows each device to have its own optimized calibration parameters, achieving both uniform calibration methodology and device-specific accuracy.

The patent applies dynamics by making the calibration approach adaptive rather than static. The system dynamically selects and applies appropriate calibration models based on the specific device being used. Machine learning models are trained on device-specific calibration data and automatically applied when that device is detected, allowing the system to adapt to varying device characteristics while maintaining a unified calibration framework.

The patent applies self-service by enabling devices to automatically undergo calibration without requiring manual configuration or user intervention. The system collects calibration data automatically during normal operation and uses machine learning to generate device-specific models autonomously. This reduces the complexity burden on users while maintaining high localization accuracy through automated, device-specific calibration processes.

The patent applies feedback by using collected localization data to continuously improve calibration models. The system gathers calibration data from devices during operation, uses this feedback to retrain and refine machine learning models, and then applies the improved models back to the system. This closed-loop feedback mechanism allows the system to self-optimize calibration accuracy without increasing operational complexity for users.