KellDann’s Kirk Sigmon authored a guide to valuing artificial intelligence-related intellectual property for the National Association of Certified Valuators and Analysts (NACVA), published in its QuickRead publication. The guide provides practical guidance for valuing U.S. patents, copyrights, and trade secrets involving AI, machine learning, and large language models (LLMs), with particular attention to legal scope, technical substance, transferability, detectability, and enforceability. You can read Kirk’s guide for free here.
Article Focuses on Issues in Valuing AI-Related Patents, Copyrights, and Trade Secrets
One primary aspect of Kirk’s guide focuses on how broadly-written AI-related patents are not necessarily valuable:
For valuation purposes, a broad-sounding AI-related patent is not necessarily valuable. Value depends on what the claims cover, whether competitors infringe them, whether infringement can be detected, whether the claims can be designed around, and whether the claimed invention still matters commercially. A patent may look formidable and still cover no meaningful revenue stream. Modern news coverage often misses this point: an impressive-looking patent directed to a lofty AI-related goal (such as using AI across an entire industry) may, because of claim limitations or recent changes in patent law, cover such a narrow niche that enforcement is practically unrealistic.
Kirk also surveyed the complex considerations inherent to the value of AI-related copyrights and trade secrets:
The value of copyright- or trade-secret-protected assets can depend heavily on employee activity, rights agreements, and external conduct. Were employee and contractor assignments signed to confirm ownership of the code or secret? Is third-party or open-source code embedded in the product? Are access controls, confidentiality agreements, logging, repository histories, and offboarding procedures actually in place? Can the company recognize misuse and identify a defendant worth suing? If the company sued and won, could the target pay damages? Each question has significant valuation implications.
Guide Provides Starting Framework for Valuing AI IP
The guide culminates in a high-level but ultimately functional and non-legalese framework for valuing AI IP:
Despite that complexity, valuation professionals do not need to become IP professionals. One useful approach is to ask six key questions:
- What AI-related IP is being valued? Define protection and what it covers; a specific secret, a specific source-code repository, or a specific claimed invention.
- How does the IP create economic benefit? It might make the system more attractive to customers, reduce costs, such as compute expense, or create another measurable advantage.
- Does the company own and control the IP? Review assignments, licenses, open-source obligations, data rights, model-provider terms, liens, joint-development agreements, and other restrictions.
- How long will the advantage last? Do not just focus on legal life, include the likelihood that (for example) others might independently discover a trade secret and/or that the relevant AI might become technologically obsolete.
- Can the IP be enforced, and are there consequences to enforcement? Can infringement be identified? Is there budget and willingness to sue? Would suing trigger a countersuit?
- Where should the risk enter the model? Depending on the facts, it may affect forecast cash flows, a royalty rate, commercialization probability, remaining economic life, enforcement cost, or terminal value. Avoid the temptation to bury all concerns into the discount rate.
About the National Association of Certified Valuators and Analyts® (NACVA®) and QuickRead
The National Association of Certified Valuators and Analysts is a professional organization serving the business valuation, financial litigation, and related financial consulting fields. NACVA’s QuickRead publication covers developments in business and intangible-asset valuation, litigation consulting, financial forensics, mergers and acquisitions, and related areas.