DNRTML Schema

The Unified Conceptual Framework/Grand Unified Tensor Theory (UCF/GUTT) is a theoretical framework that models reality as a dynamic network of relational interactions. It supports applications in physics, mathematics, and interdisciplinary domains by representing systems as nested relational structures that evolve over time.

• Relational Ontology: All entities exist through their relations, enabling unified modeling across scales.
• Emergent Dynamics: Complex phenomena (e.g., physical, mathematical, biological) arise from relational interactions.
• Interdisciplinary Applications: The framework supports diverse fields, from black hole physics to algebraic structures.

The Dynamic Nested Relational Tensor Markup Language (DNRTML) is a computational language designed to implement UCF/GUTT’s principles. It provides a structured approach to model dynamic systems with:

• Nested relational tensors for multi-scale interactions.
• Integration with external data for real-time validation.
• Machine learning capabilities for pattern recognition and prediction.

• Physics: Modeling complex phenomena like black hole dynamics.
• Mathematics: Representing algebraic structures and theorems.
• Medicine: Simulating disease spread and drug interactions for personalized treatments.
• Education: Enabling interactive learning through dynamic visualizations.

DNRTML is under active development, with proprietary implementations available for licensing. Contact us for collaboration opportunities or to explore its applications in research and industry.

The Dynamic Nested Relational Tensor Markup Language (DNRTML) is a proprietary computational framework implementing the Unified Conceptual Framework/Grand Unified Tensor Theory (UCF/GUTT). It enables modeling of complex, dynamic systems across physics (e.g., black hole dynamics), mathematics (e.g., algebraic K-theory), medicine (e.g., disease spread, drug interactions), and other domains (e.g., geopolitics, AI). This licensing model offers flexible, tiered options to support diverse applications while protecting intellectual property through patents, trade secrets, and copyrights.

• Use Case: Strategic applications in national security, resource management, diplomacy, and scientific research.
  • Examples:
    • Simulating critical mineral supply chains (e.g., rare earth elements) using DNRTML’s nested tensor structure.
    • Modeling geopolitical conflict dynamics with the Relational Conflict Game (RCG) integrated with DNRTML.
    • Simulating black hole dynamics (e.g., M87* entropy, shadow diameter) validated against Event Horizon Telescope (EHT) data.
• Features:
  • Full access to the DNRTML schema, including proprietary modules for non-linear coupling (( \kappa, \lambda )).
  • Integration with the Relational Systems Python Library (RS Library) for simulation (e.g., engine.py, dashboard.py).
  • Real-time API integration for data validation (e.g., EHT, LIGO, public health databases).
  • Custom ML algorithms (e.g., graph neural networks for pattern recognition, reinforcement learning for hypothesis generation).
  • Dedicated support for integration and bespoke model development.
• IP Protections:
  • Access requires a non-disclosure agreement (NDA) to protect trade secrets (e.g., non-linear coupling algorithms).
  • Covered by pending patent applications (filed by July 1, 2026) for proprietary implementations.
  • Copyrighted DNRTML schema and code (© 2023–2025 Michael Fillippini).
• Pricing: $250,000–$2,000,000 annually, based on scope, number of users, and customization needs.
• Example: A government agency licenses DNRTML to optimize rare earth element supply chains, achieving 95% resource autonomy using validated simulations.

• Use Case: Commercial applications in finance, logistics, technology, and pharmaceuticals.
  • Examples:
    • Financial forecasting using fractal-based trading models with DNRTML’s tensor structure.
    • Supply chain optimization for resilience, integrating real-time market data.
    • Drug interaction modeling for personalized medicine, validated against clinical data.
• Features:
  • Cloud-based access to DNRTML via a secure SaaS platform.
  • Scalable analytics with API integration (e.g., financial APIs, medical databases).
  • Visualization tools (e.g., D3.js for interactive graphs) for data exploration.
  • Limited access to proprietary ML modules (e.g., pattern recognition for market trends).
  • Standard support for integration and training.
• IP Protections:
  • NDA required for access to proprietary simulation algorithms.
  • Covered by pending patents for non-linear coupling and ML-driven validation.
  • Copyrighted DNRTML schema and visualization code.
• Pricing: $25,000–$250,000 annually, with tiered plans based on features, user volume, and data throughput.
  • Basic Tier: $25,000/year (core analytics, up to 10 users).
  • Pro Tier: $100,000/year (advanced ML, up to 50 users).
  • Enterprise Tier: $250,000/year (full features, unlimited users).
• Example: A pharmaceutical company licenses the Pro Tier to model drug interactions, achieving 90% accuracy in personalized treatment predictions.

• Use Case: Research and educational applications in universities, research institutes, and non-profits.
  • Examples:
    • Modeling algebraic K-theory (e.g., ( K_0(\mathbb{Z}) = \mathbb{Z} )) using DNRTML’s machineLearning module.
    • Simulating disease spread dynamics for epidemiological studies.
    • Exploring UCF/GUTT’s relational ontology (Proposition 1) in social or physical sciences.
• Features:
  • Access to a limited, non-proprietary DNRTML schema (e.g., basic tensorType, entityType).
  • RS Library modules for academic simulations (e.g., K-theory computations, disease models).
  • Visualization tools for educational use (e.g., interactive graphs for K-group relations).
  • Community support and access to open-source datasets.
• IP Protections:
  • Non-proprietary components covered by open-source licenses (e.g., MIT with academic restrictions).
  • Proprietary modules (e.g., non-linear coupling, advanced ML) excluded, requiring NDAs for access.
  • Copyrighted educational materials (e.g., educational_note, interactive_prompt).
• Pricing: Free for qualifying institutions, with optional premium support at $5,000–$20,000/year.
• Example: A university licenses DNRTML to teach K-theory, using interactive visualizations to explore ( K_1(\mathbb{Z}) = \mathbb{Z}/2\mathbb{Z} ).

• Use Case: Bespoke projects requiring tailored DNRTML implementations.
  • Examples:
    • Developing a custom disease spread model for a public health agency.
    • Simulating relativistic jet dynamics for an astronomical observatory.
    • Consulting on geopolitical negotiations using RCG integrated with DNRTML.
• Features:
  • Custom DNRTML deployments with proprietary modules (e.g., non-linear coupling, ML algorithms).
  • Integration with client-specific data sources (e.g., EHT, clinical databases).
  • Strategic consulting by UCF/GUTT experts for model development and validation.
  • Full access to RS Library and RCG for project-specific simulations.
• IP Protections:
  • NDAs required to protect proprietary algorithms and simulation code.
  • Covered by pending patents and copyrighted materials.
  • Custom deliverables protected as trade secrets.
• Pricing: $100,000–$1,000,000 per project, based on complexity, duration, and data integration needs.
• Example: A health organization licenses a custom DNRTML model for pandemic prediction, achieving 92% accuracy in outbreak forecasting.

Submit Inquiry:

• Email Michael_Fill@Protonmail.com with:
  • Organization name and contact details.
  • Intended use case (e.g., black hole simulation, disease modeling).
  • Desired licensing tier (government, SaaS, academic, consulting).
  • Scope, audience, and timeline.

Review and NDA:

• Review within 5–10 business days.
• NDA required for access to proprietary materials (e.g., DNRTML schema, RS Library code).

Proposal and Agreement:

• Customized proposal with pricing, terms, and support details.
• Signed licensing contract outlining usage rights, restrictions, and ethical guidelines.

Integration and Support:

• Access to licensed materials and technical support.
• Optional consulting for bespoke implementations.

• Patents: Pending applications (to be filed by July, 2026) cover DNRTML’s non-linear coupling algorithms, API integration, and ML-driven validation, protecting proprietary implementations not disclosed on https://relationalexistence.com/dnrtml.
• Trade Secrets: Proprietary code (e.g., simulation algorithms, coupling constants) protected via NDAs and secure storage.
• Copyrights: DNRTML schema, RS Library code, and educational materials copyrighted (© 2023–2025 Michael Fillippini).
• Open-Source Option: Limited DNRTML components available under MIT license with commercial restrictions for academic use.
• Trademarks: “DNRTML,” “UCF/GUTT,” and “RS Library” trademarked to protect brand identity.

• Precision: Validated simulations (e.g., M87* entropy, disease spread) achieve high accuracy (e.g., MSE ≈ 1.25e-06).
• Scalability: Supports high-dimensional tensor operations (e.g., ( 32 \times 32 \times 32 )) with optimization (e.g., Tucker decomposition).
• Interdisciplinary Impact: Enables breakthroughs in physics, mathematics, medicine, and geopolitics.
• Ethical Innovation: Aligns with UCF/GUTT’s vision for a unified, ethical global society (Proposition 52).

Email Michael_Fill@Protonmail.com to explore licensing opportunities and collaborate on transformative applications.