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

• URL: http://arxiv.org/abs/2507.01717v1
• Date: Wed, 02 Jul 2025 13:47:17 GMT
• Title: Agent Ideate: A Framework for Product Idea Generation from Patents Using Agentic AI
• Authors: Gopichand Kanumolu, Ashok Urlana, Charaka Vinayak Kumar, Bala Mallikarjunarao Garlapati,
• Abstract summary: This work explores the use of Large Language Models (LLMs) and autonomous agents to mine and generate product concepts from a given patent.<n>In this work, we design Agent Ideate, a framework for automatically generating product-based business ideas from patents.
• Score: 1.0194469147760787
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training [67.895981259683]
  General AI Agents are increasingly recognized as foundational frameworks for the next generation of artificial intelligence.<n>Current agent systems are either closed-source or heavily reliant on a variety of paid APIs and proprietary tools.<n>We present Cognitive Kernel-Pro, a fully open-source and (to the maximum extent) free multi-module agent framework.
  arXiv  Detail & Related papers  (2025-08-01T08:11:31Z)
• Artificial Intelligence In Patent And Market Intelligence: A New Paradigm For Technology Scouting [2.9954831490478044]
  This paper presents the development of an AI powered software platform to transform technology scouting and solution discovery in industrial R&D.<n>The proposed platform utilizes cutting edge LLM capabilities including semantic understanding, contextual reasoning, and cross-domain knowledge extraction.<n>The system processes unstructured patent texts, such as claims and technical descriptions, and systematically extracts potential innovations aligned with the given problem context.<n>In addition to patent analysis, the platform integrates commercial intelligence by identifying validated market solutions and active organizations addressing similar challenges.
  arXiv  Detail & Related papers  (2025-07-27T15:22:39Z)
• Probing and Inducing Combinational Creativity in Vision-Language Models [52.76981145923602]
  Recent advances in Vision-Language Models (VLMs) have sparked debate about whether their outputs reflect combinational creativity.<n>We propose the Identification-Explanation-Implication (IEI) framework, which decomposes creative processes into three levels.<n>To validate this framework, we curate CreativeMashup, a high-quality dataset of 666 artist-generated visual mashups annotated according to the IEI framework.
  arXiv  Detail & Related papers  (2025-04-17T17:38:18Z)
• Creation-MMBench: Assessing Context-Aware Creative Intelligence in MLLM [58.42678619252968]
  Creation-MMBench is a benchmark designed to evaluate the creative capabilities of Multimodal Large Language Models.<n>The benchmark comprises 765 test cases spanning 51 fine-grained tasks.<n> Experimental results reveal that open-source MLLMs significantly underperform compared to proprietary models in creative tasks.
  arXiv  Detail & Related papers  (2025-03-18T17:51:34Z)
• EvoPat: A Multi-LLM-based Patents Summarization and Analysis Agent [0.0]
  EvoPat is a multi-LLM-based patent agent designed to assist users in analyzing patents through Retrieval-Augmented Generation (RAG) and advanced search strategies.<n>We demonstrate that EvoPat outperforms GPT-4 in tasks such as patent summarization, comparative analysis, and technical evaluation.
  arXiv  Detail & Related papers  (2024-12-24T02:21:09Z)
• AutoPatent: A Multi-Agent Framework for Automatic Patent Generation [16.862811929856313]
  We introduce a novel and practical task known as Draft2Patent, along with its corresponding D2P benchmark, which challenges Large Language Models to generate full-length patents averaging 17K tokens based on initial drafts.<n>We propose a multi-agent framework called AutoPatent which leverages the LLM-based planner agent, writer agents, and examiner agent with PGTree and RRAG to generate lengthy, intricate, and high-quality complete patent documents.
  arXiv  Detail & Related papers  (2024-12-13T02:27:34Z)
• Large Language Model for Patent Concept Generation [2.4368308736427697]
  Existing large language models (LLMs) often fall short in the innovative concept generation due to a lack of specialized knowledge.<n>We propose a novel knowledge finetuning (KFT) framework to endow LLM-based AI with the ability to autonomously mine, understand, and apply domain-specific knowledge.<n>Our proposed PatentGPT integrates knowledge injection pre-training (KPT), domain-specific supervised finetuning (SFT), and reinforcement learning from human feedback.
  arXiv  Detail & Related papers  (2024-08-26T12:00:29Z)
• LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application [54.984348122105516]
  Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework synergizes open-world knowledge with collaborative knowledge.<n>We propose an Llm-driven knowlEdge Adaptive RecommeNdation (LEARN) framework that synergizes open-world knowledge with collaborative knowledge.
  arXiv  Detail & Related papers  (2024-05-07T04:00:30Z)
• A Survey on Patent Analysis: From NLP to Multimodal AI [14.090575139188422]
  This interdisciplinary survey aims to serve as a comprehensive resource for researchers and practitioners who work at the intersection of NLP, Multimodal AI, and patent analysis.
  arXiv  Detail & Related papers  (2024-04-02T20:44:06Z)
• AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System [91.41155892086252]
  We open-source a new AI agent library, AgentLite, which simplifies research investigation into LLM agents. AgentLite is a task-oriented framework designed to enhance the ability of agents to break down tasks. We introduce multiple practical applications developed with AgentLite to demonstrate its convenience and flexibility.
  arXiv  Detail & Related papers  (2024-02-23T06:25:20Z)
• The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
  Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI) We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents. We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
  arXiv  Detail & Related papers  (2023-09-14T17:12:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.