The proposed system is an AI-driven platform that analyzes a person’s genomic sequence to recommend personalized health, longevity, and wellness interventions. It combines cutting-edge deep learning for genomic interpretation with knowledge-driven recommendations for both CRISPR-based genetic edits and non-invasive interventions (like diet, lifestyle, or medications). All components are designed with today’s technology in mind (no speculative hardware) while being quantum-ready – meaning the architecture can exploit quantum computing accelerations in the future for heavy genomic computations​ frontiersin.org. The system integrates with global genomic databases (e.g. ClinVar) to leverage known variant information​ usuhs.libguides.com, and it adheres to strict privacy and ethical guidelines (using data anonymization, secure handling of personal genomic data, and requiring informed consent at every step). Deep learning serves as the core analytical engine, supplemented by other methods (databases, rule-based logic, even reinforcement learning for planning interventions) where most effective.

At a high level, the system works as follows: A user’s genomic data is securely ingested and processed to identify genetic variants. A genomic deep learning model then analyzes these variants (and surrounding sequences) to predict health risks or traits, akin to how NLP models analyze language (genomic DNA can be treated like a sequential “language”​ pmc.ncbi.nlm.nih.gov). The system cross-references known variant databases (e.g. ClinVar’s archive of variant pathogenicity​ usuhs.libguides.com) and identifies key genetic factors influencing the individual’s health. Based on these insights, it generates two categories of recommendations: genetic interventions (e.g. CRISPR edits for deleterious variants, guided by AI tools to maximize on-target precision​ frontiersin.org) and non-invasive interventions (personalized lifestyle changes, diets, or existing drugs tailored to the person’s genetic predispositions). Throughout the process, data privacy is maintained via anonymization and secure computation (e.g. using de-identified variant IDs when querying databases, and federated or on-device analysis to avoid sharing raw genomes). Ethical compliance checks are integrated before any recommendations are finalized, ensuring suggestions align with medical guidelines and the user’s consent (for example, only recommending gene edits in medically justifiable scenarios, and never violating regulations on human genome editing).