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.

翻译：当前的AI辅助创新系统通常应用单一构思方法论（如TRIZ或设计思维），采用基于顺序提示的工作流程，无法保留中间推理结构。因此，跨方法论生成的洞察仍然碎片化，限制了可追溯性、综合性和新颖性的系统评估。我们提出IdeaForge，一种基于知识图谱的多智能体框架，用于创新分析与专利权利要求生成。IdeaForge通过专业智能体集成多种创新方法论（TRIZ、设计思维和SCAMPER），这些智能体在持久化的FalkorDB知识图谱上运行。每个智能体贡献结构化实体和关系，代表矛盾、发明原理、用户需求、转换、类比和候选权利要求。IdeaForge的核心贡献在于一种通过基于图谱的权利要求联动实现的跨方法论收敛机制。由多种方法论独立支持的权利要求通过CONVERGENT关系连接，从而能够通过图谱遍历识别高置信度的创新候选方案。下游的专利起草智能体生成基于收敛权利要求子图的结构化专利草案，减少对无约束语言模型生成的依赖。一项创新评分公式（InnovationScore）根据收敛支持度、方法论多样性、权利要求强度和现有技术挑战数量对权利要求进行排序。我们描述了图谱模式、智能体架构、收敛检测流程和专利合成工作流程。在一个法律技术用例上的实验表明，与单一方法基线相比，基于图谱的多方法论合成生成了更多样化且可追溯的创新候选方案。我们讨论了其对计算创造力、可解释的AI辅助发明以及图谱原生创新系统的启示。