Designing AI-Supported Patent Search and Trademark Monitoring Workflows

Building AI-supported workflows for patent and trademark work requires careful design at every stage. The data sources you draw on, the way you frame queries, the human review steps you keep in place, and the way you document decisions all shape whether the workflow stands up to scrutiny. This course walks through those decisions in the order they typically arise. You will work through written design exercises that mirror how an IP operations team would plan AI-supported workflows. The emphasis is on the practical tradeoffs that decide whether these tools accelerate the right work without quietly missing what matters. What you'll learn: - Plan AI-supported prior art search workflows that combine semantic search with traditional classification searches - Design trademark monitoring routines that surface visually and phonetically similar marks across jurisdictions - Apply natural language processing for analyzing patent claims, specifications, and office actions - Build portfolio analytics that summarize coverage, gaps, renewals, and competitive positioning - Plan human review steps that preserve attorney judgment at every consequential decision - Design documentation routines that capture every search, decision, and exception for future reference The course progresses from search workflows through trademark monitoring, claim analysis, portfolio analytics, and documentation. A capstone written exercise asks you to draft a one-page workflow design for AI-supported IP work in a specific industry or technology area. This course is designed for IP operations professionals, paralegals, and technology operators with some software or data background. No deep AI experience is required. The course treats workflow design as a practical exercise and is informational; it does not provide legal advice for specific situations.