Korea University · 工学
Professor Chang-Yong Lee's research lab specializes in technology and innovation management, with a focus on leveraging large-scale patent analytics to uncover technological trends, industry convergence, and intellectual property dynamics. The lab develops advanced computational methods—such as hierarchical keyword vectors, tree matching algorithms, and network centrality analysis—to model complex relationships in patent data and support strategic decision-making in R&D and technology policy. Their work bridges computer science, information systems, and innovation studies, emphasizing practical applications in patent infringement detection and foresight analysis. The lab also investigates the structural properties of complex networks, particularly in technological and industrial contexts, to understand the drivers of innovation and convergence.
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We propose a semantic patent claim analysis that can examine patents for possible infringements and identify which needs to be manually perused. So far, numerous approaches have been devised to systemise this burden, but have not been useful in practice because of a lack of consideration of semi-structure of patent claim data and claim element-based procedure of adjudicating patent infringement. At the heart of our method is a hierarchical keyword vector for representing the dependency relations
Industry convergence has been the subject of many prior studies, yet most have focused on certain domains based on ex post evaluation. This study presents a systematic approach to anticipating technology-driven industry convergence using large-scale patent analysis covering all technology fields. Our approach includes patent co-classification analysis with the concordance between patent classes and industrial sectors to measure technological relations between industries; centrality and brokerage
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