From Patent Databases to Big Data: Using AI-Driven Patent Analytics as a Pedagogical Innovation in Teaching Patent Law

The rapid growth of global patent filings has transformed patent information into one of the largest and most complex knowledge repositories in the modern innovation economy. In 2024 alone, innovators filed approximately 3.7 million patent applications worldwide, reflecting sustained growth in innovation activity over the past decade (World Intellectual Property Organization [WIPO], 2025a). This expansion has generated vast patent datasets that increasingly require advanced analytical tools to interpret. At the same time, artificial intelligence (AI)-driven patent analytics platforms are reshaping how patent information is searched, analysed, and applied in professional practice. These developments pose important implications for legal education, particularly for the teaching of patent law. Traditional pedagogical approaches in law schools-centered primarily on statutes, case law, and doctrinal analysis-do not adequately prepare students for a practice environment increasingly defined by data-driven research and AI-assisted analysis. This chapter examines how AI-driven patent analytics can be incorporated into patent law pedagogy as a teaching innovation. It argues that integrating patent analytics tools into the curriculum enables students to engage with real-world patent datasets, understand technological trends, and develop data literacy alongside doctrinal competence. Ultimately, the chapter contends that teaching patent law in the age of AI requires a shift toward data-informed legal education that complements traditional doctrinal training.