The increasing complexity of modern engineering design demands intelligent systems capable of integrating vast and heterogeneous sources of knowledge. AI, particularly through autonomous and multi-agent systems, is revolutionizing how designers and researchers access, interpret, and utilize knowledge for product innovation. AI-based knowledge search enables the automated retrieval and synthesis of relevant technical and scientific information, transforming unstructured data into actionable insights. Among these sources, patent databases represent a valuable but underexploited reservoir of technological knowledge.

By combining machine learning, natural language processing, and semantic reasoning, AI systems can extract inventive principles, technology trends, and design opportunities from patent data with unprecedented accuracy. These insights can be systematically linked to engineering design processes, supporting creativity, novelty assessment, and strategic decision-making in early development phases. The integration of AI-driven patent analysis with methods of systematic innovation (such as TRIZ and design-by-analogy) opens new perspectives for data-informed creativity and intelligent design automation.

This special session invites contributions on AI-powered knowledge discovery, patent analytics, and computational innovation methods aimed at enhancing product development. The goal is to foster a multidisciplinary dialogue connecting AI, design theory, and innovation engineering.