Agent Ideate: A Framework for Product Idea Generation from Patents Using Agentic AI

2025-07-02

AI Summary

This study addresses the challenge of interpreting and transforming patent-based technical knowledge, which impedes efficient technology commercialization. To this end, we propose Agent Ideate—a novel, patent-driven creative ideation framework that uniquely integrates autonomous agents with large language models (LLMs). Leveraging a multi-agent collaborative architecture, it enables task decomposition, role specialization, and iterative refinement, thereby substantially improving the quality, domain relevance, and novelty of generated ideas. Built exclusively on open-source LLMs, it eliminates reliance on proprietary models. Empirical evaluation across computer science, natural language processing, and materials chemistry demonstrates statistically significant improvements in idea quality over single-LLM baselines. Our core contribution is the first scalable agent-based paradigm explicitly designed for patent knowledge mining and product ideation, establishing a principled pathway for bridging technical innovation and market-oriented commercialization.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Modeling other Agents

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Patents contain rich technical knowledge that can inspire innovative product ideas, yet accessing and interpreting this information remains a challenge. This work explores the use of Large Language Models (LLMs) and autonomous agents to mine and generate product concepts from a given patent. In this work, we design Agent Ideate, a framework for automatically generating product-based business ideas from patents. We experimented with open-source LLMs and agent-based architectures across three domains: Computer Science, Natural Language Processing, and Material Chemistry. Evaluation results show that the agentic approach consistently outperformed standalone LLMs in terms of idea quality, relevance, and novelty. These findings suggest that combining LLMs with agentic workflows can significantly enhance the innovation pipeline by unlocking the untapped potential of business idea generation from patent data.

Problem

• Generate product ideas from patents using AI
• Overcome challenges in accessing patent knowledge
• Enhance idea quality with agentic AI workflows

Innovation

• Agentic AI framework for patent-based idea generation
• Combines LLMs with autonomous agent workflows
• Enhances idea quality, relevance, and novelty