Artificial Intelligence In Patent And Market Intelligence: A New Paradigm For Technology Scouting

2025-07-27

AI Summary

In industrial R&D, traditional technology scouting and solution discovery rely heavily on manual efforts, suffer from low efficiency, and face fragmented data sources (e.g., patent databases, product catalogs, competitive intelligence), leading to disjointed insights and delayed innovation. Method: This study introduces the first AI-driven intelligent platform for joint analysis of cross-domain patent and market data. It integrates large language models (LLMs) for semantic understanding, contextual reasoning, and cross-domain knowledge extraction, augmented by NLP techniques and clustering algorithms to parse unstructured patent text and integrate commercial intelligence. The platform features a novel co-evaluation framework for technical novelty and commercial feasibility, alongside a standardized technology taxonomy. Contribution/Results: Experiments demonstrate significant reductions in manual search effort and R&D cycle time, improved solution relevance and decision quality, and enhanced systemic innovation capability within complex technological ecosystems.

Technology Category

• Humans and AI: Crowd Sourcing and Human Computation
• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• 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: Vertical and domain-specific search

Abstract

This paper presents the development of an AI powered software platform that leverages advanced large language models (LLMs) to transform technology scouting and solution discovery in industrial R&D. Traditional approaches to solving complex research and development challenges are often time consuming, manually driven, and heavily dependent on domain specific expertise. These methods typically involve navigating fragmented sources such as patent repositories, commercial product catalogs, and competitor data, leading to inefficiencies and incomplete insights. The proposed platform utilizes cutting edge LLM capabilities including semantic understanding, contextual reasoning, and cross-domain knowledge extraction to interpret problem statements and retrieve high-quality, sustainable solutions. The system processes unstructured patent texts, such as claims and technical descriptions, and systematically extracts potential innovations aligned with the given problem context. These solutions are then algorithmically organized under standardized technical categories and subcategories to ensure clarity and relevance across interdisciplinary domains. In addition to patent analysis, the platform integrates commercial intelligence by identifying validated market solutions and active organizations addressing similar challenges. This combined insight sourced from both intellectual property and real world product data enables R&D teams to assess not only technical novelty but also feasibility, scalability, and sustainability. The result is a comprehensive, AI driven scouting engine that reduces manual effort, accelerates innovation cycles, and enhances decision making in complex R&D environments.

Problem

• AI platform transforms time-consuming manual technology scouting
• System analyzes patents and market data for R&D solutions
• LLMs enable cross-domain knowledge extraction for innovation discovery

Innovation

• AI platform using LLMs for tech scouting
• Semantic understanding extracts patent insights
• Integrates patent and market intelligence data