AI for Intellectual Property: Foundations of Modern Patent and Trademark Work

Intellectual property work has historically required slow, manual searches across crowded databases, with human judgment applied at every step. AI does not replace that judgment, but it does accelerate the parts of the work where pattern matching and language understanding scale poorly for humans. This course gives you a calm, structured introduction to where AI fits in modern IP practice. You will learn what AI tools actually do in IP work, where they are already useful, and where the work still belongs firmly with trained attorneys and analysts. The course stays grounded in widely used concepts and respects the high-stakes nature of intellectual property decisions. What you'll learn: - Understand the main categories of intellectual property and the workflows behind protecting each - Recognize where AI adds value including prior art searches, trademark similarity, and portfolio analytics - Explore the role of natural language processing in reading patents and surfacing relevant prior art - Read the typical workflow from initial search through filing, prosecution, and post-grant monitoring - Identify the risks of automated IP work including missed art, explainability gaps, and overreliance on tools - Understand the integration points between AI tools, IP management systems, and outside counsel workflows The course begins with the goals of IP work, moves through where AI adds value, and closes with the risks and responsibilities that come with these tools. Written exercises help you connect each concept to a realistic technology or brand scenario. This course is designed for IP professionals, paralegals, and technology operators with no prior AI background. It is also useful for technology professionals exploring IP work. No prerequisites are needed. The course is informational and does not provide legal advice; it builds the literacy that lets you ask better questions of qualified attorneys.