Multi-Omics Responder Classification for Personalized Skincare Regimens

The patent segments the complex omics data analysis into multiple independent classifiers, each handling a specific type of omics data (genomics, transcriptomics, proteomics, metabolomics, microbiomics). This segmentation allows the system to process different data types separately and combine results, reducing the complexity burden while maintaining high accuracy in responder category determination.

The patent introduces classifier models as intermediary components between raw omics data and responder category determination. These classifiers act as mediators that transform complex multi-omics data into actionable predictions, enabling accurate responder categorization without requiring direct complex analysis of all omics data simultaneously.

The patent implements dynamic responder category determination by continuously updating classifier models with new omics data and clinical measurements over time. The system transitions from static classification to dynamic adaptation, where responder categories evolve as the subject's biological state changes, maintaining high accuracy without requiring excessive repeated measurements.

The patent incorporates feedback mechanisms where clinical measurements and treatment outcomes are fed back into the classifier models to update and refine predictions. This feedback loop allows the system to learn from actual treatment responses and improve future predictions, reducing the need for frequent re-measurements while maintaining accuracy.