🏛️ The Invention Parliament and the AMCP protocol

10 AI Agents Invent Patents in 1 Hour via Async Protocol

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🔬 The Problem

Traditional R&D costs $180k and takes 6 months per innovation. Even AI multi-agent systems suffer from sequential bottlenecks—agents wait for each other, limiting scalability to ~10 agents max.

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🚀 Our Two-Layer Solution

Layer 1: Invention Parliament - 10 specialized AI experts (materials scientists, engineers, patent attorneys) collaborate asynchronously to generate USPTO-ready patents in 60 minutes.

Layer 2: AMCP Protocol - Novel Asynchronous Model Context Protocol enabling true parallel agent collaboration without coordination overhead. Our core technical contribution.

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🌐 AMCP: Protocol Innovation

Problem with existing protocols (like Anthropic’s MCP):

• Sequential turn-taking: Agent A → Agent B → Agent C
• O(n²) coordination complexity
• Max ~10 agents before collapse

AMCP solution:

All agents broadcast to shared vector memory →  
Retrieve relevant context via semantic search →  
Form coalitions dynamically →  
Converge via emergent consensus  

Key innovations:

• Broadcast publishing (zero routing overhead)
• Semantic retrieval (read only relevant context)
• Coalition formation (self-organizing groups)
• Convergence detection (auto-detect consensus)

Result: Scales to 1,000+ agents with 10x throughput

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🧬 How It Works (4 Phases)

Phase 1: Parallel Ideation (5min)
Input: “Reduce EV battery degradation by 50%”
10 agents broadcast 3 ideas each → 30 proposals in Qdrant

Phase 2: Semantic Clustering (5min)
Vector search groups similar concepts → 5-7 innovation clusters

Phase 3: Async Deliberation (30min)
Agents query relevant critiques, form coalitions, debate in parallel. Contrarian agent ensures rigor. No turn-taking.

Phase 4: Patent Generation (20min)
Patent attorney searches 120k prior art patents, identifies novel elements, generates USPTO application.

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🛠️ Tech Stack

• Mistral Agents API: 10 autonomous experts with memory
• Qdrant: AMCP backbone (message broadcast + 120k patent DB)
• n8n: Async workflow orchestration
• Google Cloud: Parallel agent execution
• Lovable: Real-time debate visualization
• Custom AMCP SDK: Python library for protocol

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📊 Business Impact

Traditional	Invention Parliament
6 months	1 hour
$180,000	$200
1 view	10 experts
Sequential	Zero wait
Max 10 people	1000+ agents

Target: R&D labs, startups, universities, patent firms
TAM: $50B corporate R&D + $5B patent market

Revenue Models:

• $5k per patent session (SaaS)
• $50k/year enterprise license
• AMCP infrastructure hosting

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🎯 Why “One-Person Unicorn”

1. Two-layer value: Application (revenue) + Protocol (platform)
2. Truly autonomous: Zero human input (problem → patent)
3. Infinite scale: AMCP enables 10 → 10,000 agents
4. Technical moat: Novel protocol = first-mover advantage
5. Research contribution: Publishable AMCP specification

One founder out-innovates entire R&D departments.

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🏆 Demo Deliverables

✅ Live prototype: Problem → Patent in 60min
✅ Real-time viz: 10 agents debating via AMCP
✅ Sample output: Full USPTO provisional patent
✅ AMCP spec document v0.1
✅ Performance metrics: Latency, convergence
✅ 2-min video demo

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👥 Team

Wilfred Doré
Staff Engineer at Qualcomm | 5G/6G Research
Thesis: Turn-based AI is dead. Future is parallel, autonomous intelligence.
Role: AMCP protocol design, agent architecture

Gowshigan Selladurai
ML Engineer at Cleva | Distributed Systems
Thesis: AI systems scale horizontally, not sequentially.
Role: Infrastructure, Qdrant vector search, visualization

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🔮 Roadmap

Today: 10 agents invent batteries
3 months: 100 agents, AMCP v1.0, 3 research labs adopt
1 year: 1,000 agents, 500 patents/month, $2M ARR
5 years: AMCP powers autonomous AI economy, $100M ARR

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🚀 Why We Win

1. Novel protocol (AMCP) = publishable research
2. Clear ROI: 900x cost reduction
3. Perfect theme: Autonomous multi-agent unicorn
4. Proven scale: 10 → 1,000 agents
5. Open source: AMCP benefits entire community

Not just a project—infrastructure for autonomous AI companies.

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📚 Post-Hackathon

• Publish AMCP specification (open standard)
• Release Python SDK for community adoption
• Paper submission: “AMCP: Scaling Multi-Agent Systems via Async Communication”
• Position as async alternative to Anthropic’s MCP

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One person. One protocol. Infinite agents. 🏛️⚡
https://github.com/GowshiganS/HackathonInventionParliament

Past ideas:

• https://devpost.com/software/magistral (interoperability before MCP was released)
• https://github.com/Alistorm/Magellan/blob/main/ressources/presentations/1pager.pdf (asynchronous AI as a side note)
  Our submission and idea is completely new