Developing a Strategy to Protect and Monetize AI and Agents in the Digital Economy

This program will discuss how attorneys can help clients navigate the rapidly evolving legal and commercial landscape surrounding artificial intelligence (AI), including generative AI systems and autonomous agents. As AI technologies become more embedded in digital products and services-from software and SaaS platforms to data pipelines and algorithmic decision systems-clients increasingly require tailored strategies for protecting, licensing, and monetizing their innovations.

The course will explore key intellectual property considerations, including patent and copyright protections, trade secret and data rights, and open-source versus fair-source licensing frameworks. It will also analyze recent legal developments and case law shaping how AI-generated content, training datasets, prompt engineering, and reinforcement learning agents are treated under current law. This program will benefit both new and experienced attorneys advising clients on AI-assisted innovation, including those working with startups, universities, or established enterprises seeking to build or license AI-driven digital assets.

Learning Objectives:

1. Identify different types of AI systems-including generative AI, machine learning models, and autonomous agents-and their key technical and legal components
2. Prepare a legal strategy to protect AI-assisted innovations through patents, copyrights, trade secrets, and data rights
3. Discuss recent legal developments, including USPTO and Copyright Office guidance, and how they impact protectability of AI-generated content.
4. Evaluate the role of human authorship and inventorship in AI-assisted creations and its implications for ownership rights
5. Compare various licensing models-including open-source, fair-source, SaaS, and data licensing-and their suitability for different AI commercial strategies
6. Assess practical approaches to identifying and extracting value from digital assets across the AI ecosystem, including data, models, algorithms, and outputs