IdeaForge: A Knowledge Graph-Grounded Multi-Agent Framework for Cross-Methodology Innovation Analysis and Patent Claim Generation

2026-05-15 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

IdeaForge is a knowledge graph-grounded multi-agent framework designed for innovation analysis and patent claim generation, addressing limitations of current AI-assisted systems that use single ideation methodologies and lack structured reasoning. It integrates TRIZ, Design Thinking, and SCAMPER methodologies via specialist agents operating on a FalkorDB knowledge graph. These agents contribute structured entities like contradictions, inventive principles, user needs, and candidate claims. A key feature is its cross-methodology convergence mechanism, which links claims supported by multiple methodologies using "CONVERGENT" relationships, identifying high-confidence innovation candidates. A patent drafting agent then generates structured patent drafts based on these convergent claim subgraphs, reducing reliance on unconstrained language model output. The framework also includes an InnovationScore formula to rank claims based on convergent support, methodology diversity, claim strength, and prior art challenges.

Key takeaway

For research scientists developing AI-assisted innovation tools, IdeaForge demonstrates a robust approach to overcoming the limitations of single-methodology systems. You should consider adopting a knowledge graph-grounded, multi-agent framework to enhance traceability, synthesize insights across diverse ideation methodologies, and generate more reliable, explainable patent claims. This can significantly improve the quality and confidence of your innovation candidates.

Key insights

IdeaForge integrates multiple innovation methodologies via a knowledge graph to generate and rank high-confidence patent claims.

Principles

• Integrate diverse methodologies for robust innovation.
• Preserve intermediate reasoning with structured graphs.
• Convergent support indicates higher innovation confidence.

Method

IdeaForge uses specialist agents for TRIZ, Design Thinking, and SCAMPER, populating a FalkorDB knowledge graph. It detects cross-methodology convergence via graph-based claim linkage and ranks claims using an InnovationScore.

In practice

• Use FalkorDB for structured innovation data.
• Implement multi-agent systems for ideation.
• Rank innovation candidates by convergence and diversity.

Topics

• IdeaForge
• Knowledge Graphs
• Multi-Agent Frameworks
• Innovation Methodologies
• Patent Claim Generation

Best for: Research Scientist, AI Scientist, AI Engineer, Legal Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.