Overview
The AI Issue
Artificial Intelligence Is Reshaping Innovation, and Intellectual Property Law is Evolving Right Alongside It
In this special AI issue, we examine how AI is influencing the development, protection, and enforcement of intellectual property. Our attorneys share practical insights on the legal, strategic, and ethical questions businesses face as AI continues to advance.
In the Practice Group Insights section, our attorneys explore topics including minimizing risk in AI partnerships for life sciences companies, the use of AI in IP litigation, legal ethical considerations surrounding AI, and notable decisions on AI training and the fair use defense to copyright infringement.
In addition, we’re pleased to share firm updates reflecting Leason Ellis’ continued growth, including the expansion of our Executive Committee and the addition of a nine-member lateral patent team that enhances our life sciences capabilities.
Ethical Considerations When Using AI for Legal Services
Generative Artificial Intelligence (GAI), particularly Large Language Models (LLMs), are reshaping the way legal work is performed. These tools can streamline drafting, research, discovery review, and more. With the emergence of this new technology, it is essential to recall the professional rules which govern attorneys and apply to GAI tools. LLMs, such as ChatGPT, introduce new contexts in which long-standing ethical rules already apply. Recent court decisions across the country highlight the importance of using AI responsibly and in accordance with the ethical rules, and underscore the need for careful human oversight. Competence and Diligence in an AI Environment The…
TTAB Extends Initial Time to File Answers
The Trademark Trial and Appeal Board recently revised the amount of time allowed for filing answers in Board proceedings. The Board is required to designate an amount of time “not less than thirty days” in which answers must be filed. Typically, the Board sets this deadline at forty days, but effective as of September 4, 2025, this deadline has been extended to sixty days. The US is a member of the Madrid Protocol, an international treaty to facilitate international trademark registration, and this change stems from a parallel amendment to the Madrid Regulation Rules. This change should lead to consistency…
AI in IP Litigation: Balancing Promise and Peril
As artificial intelligence (AI) transforms the legal landscape, it looks to be a promising tool to improve the speed, and thereby lessen the cost of otherwise time-consuming and expensive intellectual property litigation tasks. However, to effectively harness the power of AI, litigants must balance the advantages of AI with its limitations. Anticipating potential pitfalls is a first step; utilizing human-in-loop protocols to ensure responsible use of AI is not only an important but necessary second one. Responsible Use of AI in Formulating Case Theory and Drafting It may be tempting to use AI to assist with case theory or preparation…
Fair Use and AI Training: A Comparative Analysis of Bartz v. Anthropic and Kadrey v. Meta
In the past few years, a wave of authors has taken AI companies to court, accusing them of quietly feeding copyrighted books into the training pipelines of large language models (LLMs). The core allegation is simple: you used our books without permission . The legal response from LLM developers has been equally straightforward—they’ve leaned heavily on the doctrine of fair use, the long-standing safety valve in U.S. copyright law that allows certain unlicensed uses of copyrighted works. Fair use, codified in 17 U.S.C. § 107, permits “the fair use of a copyrighted work … for purposes such as criticism, comment,…
Minimizing Risk in AI Partnership for Life Sciences Companies
Artificial intelligence is rapidly transforming the life sciences fuelling everything from molecule prediction to biomarker discovery. The opportunities are enormous, but so are the legal and strategic risks. For biotech and pharmaceutical companies, the question is not whether to embrace AI, but how to do so safely without compromising intellectual property, data integrity, or long-term value. Here’s what forward-thinking companies are doing to scale safely and protect what matters most. 1. Data Confidentiality Starts at the Source As companies share proprietary data such as compound libraries, assay results, and clinical datasets with AI partners to train powerful models, it is…
