Training Data Generation for Ultrasound Tissue Treatment

The patent combines multiple energy parameters (ultrasound energy parameters including impedance and power, and high-frequency energy parameters including impedance and power) into a unified monitoring system. This merging of multiple measurement dimensions enables accurate treatment completion detection without requiring complex separate systems for each parameter, as the control device integrates all measurements and uses machine learning models to synthesize the information.

The patent introduces machine learning estimation models as intermediaries that process multiple raw energy parameters and translate them into treatment completion predictions. These models act as mediators between the complex multi-parameter measurements and the simple binary decision of treatment completion, enabling high precision while maintaining operational simplicity through automated intelligent analysis.

The system employs self-service through automated machine learning models that automatically process multiple energy parameters and determine treatment completion without requiring complex manual intervention. The control device autonomously integrates ultrasound and high-frequency energy parameters, applies pre-trained estimation models, and generates treatment completion predictions, thereby improving reliability while keeping the operational interface simple.

The patent applies preliminary action by pre-training machine learning estimation models with extensive data before actual treatment use. This preliminary model training phase allows the system to handle complex multi-parameter analysis during treatment without requiring complex real-time processing, as the decision-making logic has already been established in advance through machine learning training.