Personalized Treatment System Using Machine Learning Models

The patent segments the treatment recommendation process into distinct functional modules: a physiological data acquisition module that collects user-specific biological data, a machine learning model module that processes the data and generates predictions, and a treatment recommendation module that outputs customized treatment plans. This segmentation allows the system to achieve high treatment efficacy through personalized recommendations while managing complexity through modular architecture, where each module can be independently developed and optimized.

The patent introduces a machine learning model as an intermediary component that bridges the gap between raw physiological data and treatment recommendations. This intermediary automatically processes and analyzes user-specific physiological data, identifying patterns and predicting treatment outcomes without requiring complex manual analysis. The machine learning model serves as a mediator that transforms raw data into actionable insights, enabling high adaptability and customization while managing system complexity through automated processing.

The patent implements preliminary action by performing comprehensive physiological data collection and analysis before treatment selection and implementation. The system gathers user-specific physiological data, processes it through machine learning models to predict treatment outcomes, and generates customized treatment recommendations prior to actual treatment administration. This preliminary analysis ensures that treatments are informed and personalized, reducing adverse effects while maintaining ease of operation during the actual treatment implementation phase, as the complex decision-making has already been completed in advance.