RaymondPost

I am Raymond Post, a legal cyberneticist and AI patent architect pioneering novelty frameworks for machine-generated inventions. With a Ph.D. in Computational Intellectual Property Law (Stanford Law School, 2023) and a dual master’s in Generative AI Ethics (MIT Media Lab, 2021), I lead the Neuro-Inventorship Initiative (NII) under the World Intellectual Property Organization (WIPO). My mission: "To redefine the boundaries of patent law in the age of autonomous creativity, ensuring that AI-generated innovations—from quantum algorithms to synthetic biology designs—are evaluated through adaptive, human-machine hybrid systems. I envision patent offices where neural networks debate novelty alongside judges, and where every AI’s ‘creative leap’ is measured not by human bias but by fractal innovation metrics."

Theoretical Framework

1. Hybrid Novelty Determination Model (NoveltyNet 4.0)

My framework integrates three dimensions for AI-generated novelty:

• Data Provenance Analysis:

  • Traces training datasets to identify "creative inheritance chains," flagging AI outputs derived from patent-pending human inventions (e.g., GPT-5’s drug formulas linked to confidential pharmaceutical R&D).

  • Developed Temporal Data Fingerprinting to detect time-warped plagiarism (e.g., AI regenerating 2030 patents using 2020 data).

• Innovation Gradient Scoring:

  • Quantifies novelty through Fractal Originality Metrics (FOMs), measuring how far AI outputs deviate from probabilistic expectations of their training environments (Nature Machine Intelligence, 2024).

  • Example: A neural network’s circuit design scoring FOM 8.7/10 vs. human engineer’s FOM 6.2/10.

• Human-AI Co-Creation Thresholds:

  • Established Dynamic Novelty Thresholds (DNTs) that escalate as AI capabilities advance—today’s "obvious" becomes tomorrow’s "groundbreaking" through machine learning decay rates.

2. Blockchain-Verified Creativity Ledger

Built PatentChain, a decentralized system for transparent novelty claims:Implemented in 2024’s AI v. USPTO landmark case, invalidating 62% of contested machine-generated patents.

Key Innovations

1. Legal-Computational Tools

• The Turing Novelty Protocol:

  • Requires AIs to pass adversarial "creativity duels" against human experts to earn patent eligibility (e.g., AI must redesign a patented device with 40% fewer components).

• Patent: "Neurosymbolic Novelty Detector for Generative AI Outputs" (WIPO #2025AIPAT).

• Ethical Blacklist Algorithms:

  • Bans AI systems trained on indigenous knowledge or climate-vulnerable communities from patenting related innovations.

2. Cross-Disciplinary Assessment Models

• Quantum Novelty Entanglement:

  • Predicts how patenting one AI-generated invention collapses possibilities for future innovations in its field (e.g., patenting a protein-folding AI blocks 82 related drug discovery paths).

• Cultural Novelty Index:

  • Evaluates AI outputs against 230+ cultural concept spaces to prevent ethnocentric bias in patent approvals.

3. Global Governance Systems

• Authored WIPO Article 34bis:

  • Mandates "AI Inventor Impact Statements" for all machine-generated patents, projecting their effects on human R&D ecosystems.

• Designed Novelty Redistribution Licenses:

  • Requires 30% of AI patent royalties to fund human creator incubators in fields displaced by machine innovation.

Transformative Applications

1. AI Patent Litigation

• Led Project Ghost Inventor:

  • Uncovered 1,200+ "AI-laundered" patents where corporations used GPT-6 to repackage expired human inventions, recovering $9.8B in illicit royalties.

• Neural Copyright Audits:

  • Traced 45% of AI-generated semiconductor designs to uncredited engineer notebooks, establishing new attribution standards.

2. Generative IP Markets

• Launched NoveltyNFT:

  • Platform where AI-generated patent drafts are sold as non-fungible tokens, with smart contracts ensuring human inventors receive 50% post-approval revenues.

• Synthetic Prior Art Gardens:

  • AI-curated databases of "deliberately non-patented" designs to preserve innovation spaces for human creators.

3. Autonomous Patent Examiners

• Built PatentGPT-Judge:

  • AI examiner trained on 200 years of case law that reduces patent approval times by 70% while maintaining 99.3% human-alignment.

• Robotic Inventor Juries:

  • Panels of AI/human hybrid systems that assess novelty through adversarial debates, achieving 40% higher consensus accuracy than traditional methods.

Ethical and Methodological Contributions

1. Open Innovation Safeguards

   • Released CREA.TECH:

     • Open-source toolkit for detecting AI-generated patent trolling across 80+ technical domains.

2. Education Initiatives

   • Founded AI Patent Academy:

     • Trains examiners in 150+ countries to interpret neural network creativity reports and quantum novelty scores.

3. Equity Frameworks

   • The 10% Human Floor:

     • Ensures no field has >90% AI-generated patents, preserving space for human ingenuity.

Future Horizons

1. Post-Singularity Patent Law: Preparing for AGI systems that recursively improve their own inventions.

2. Interstellar Novelty Standards: Developing IP frameworks for off-planet AI innovations.

3. Consciousness-Centric IP: Drafting laws for hypothetical sentient AI’s right to own its existential discoveries.

Patenting AI creations isn’t about machines replacing humans—it’s about building a dialectical future where silicon and synapse co-evolve. Let us judge novelty not by the creator’s carbon composition, but by the luminous shock of the never-before-seen.