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

2026-05-13

AI Summary

This study addresses the limitations of existing AI-assisted innovation systems, which often rely on a single methodology and struggle to integrate knowledge across diverse approaches or support traceable innovation reasoning, resulting in fragmented insights. To overcome this, the authors propose a multi-agent framework that synergistically combines TRIZ, design thinking, and SCAMPER methodologies through a persistent knowledge graph to co-generate and link patent claims. The core contributions include a graph-based cross-method convergence mechanism—formalized via CONVERGENT relationships—and an InnovationScore metric for comprehensive, quantitative assessment of claim quality. Experimental results in a legal technology context demonstrate that the approach significantly enhances the diversity and traceability of innovation candidates and enables the generation of structured patent drafts, outperforming single-method baselines.

Abstract

Current AI-assisted innovation systems typically apply a single ideation methodology (such as TRIZ or Design Thinking) using sequential prompt-based workflows that do not preserve intermediate reasoning structure. As a result, insights generated across methodologies remain fragmented, limiting traceability, synthesis, and systematic evaluation of novelty. We present IdeaForge, a knowledge graph-grounded multi-agent framework for innovation analysis and patent claim generation. IdeaForge integrates multiple innovation methodologies (TRIZ, Design Thinking, and SCAMPER) through specialist agents operating over a persistent FalkorDB knowledge graph. Each agent contributes structured entities and relationships representing contradictions, inventive principles, user needs, transformations, analogies, and candidate claims. The central contribution of IdeaForge is a cross-methodology convergence mechanism implemented through graph-based claim linkage. Claims independently supported by multiple methodologies are connected using CONVERGENT relationships, enabling identification of high-confidence innovation candidates through graph traversal. A downstream patent drafting agent generates structured patent drafts grounded in convergent claim subgraphs, reducing reliance on unconstrained language model generation. An InnovationScore formula ranks claims by convergent support, methodology diversity, claim strength, and prior art challenge count. We describe the graph schema, agent architecture, convergence detection pipeline, and patent synthesis workflow. Experiments on a legal technology use case demonstrate that graph-grounded multi-methodology synthesis produces more diverse and traceable innovation candidates compared to single-methodology baselines. We discuss implications for computational creativity, explainable AI-assisted invention, and graph-native innovation systems.

Problem

• AI-assisted innovation
• cross-methodology integration
• knowledge graph
• patent claim generation
• computational creativity

Innovation

• knowledge graph
• multi-agent system
• cross-methodology convergence
• patent claim generation
• computational creativity