ML-Based Cutting Element Detection in Personal Care Appliances

The patent replaces traditional mechanical or manual methods of determining cutting element status with a sensor-based detection system. Sensors collect operational data (vibration, temperature, current) and a machine learning model analyzes this data to automatically determine whether the cutting element is new or not new, eliminating the need for physical inspection or complex mechanical indicators.

The patent introduces sensors as intermediary devices that mediate between the cutting element and the control system. The sensors capture physical parameters during operation, and a machine learning model acts as an intermediary to process this data and infer the cutting element status, creating a bridge between physical wear and digital detection.

The patent applies partial action by selecting only the most relevant features from sensor data for analysis. Instead of processing all possible sensor outputs, the machine learning model focuses on key indicators such as vibration patterns, temperature changes, and current consumption that are most indicative of cutting element status, reducing unnecessary processing while maintaining detection accuracy.

The machine learning model is trained offline using historical data, allowing it to make rapid predictions during operation without requiring complex real-time computations. The model serves itself by having pre-learned patterns that enable quick classification of cutting element status based on simple sensor inputs, minimizing processing demands during actual use.