Jack Norton, a third-year law student at Drake University Law School, discusses in a Mar. 31 article how the growing use of artificial intelligence (AI) in machine vision may challenge established principles of U.S. trademark law. The article focuses on the intersection between AI and color trademarks, particularly regarding the doctrine of initial-interest confusion.
As AI systems become more involved in commercial processes like product identification and logistics, questions arise about whether existing legal frameworks can address situations where machines, rather than humans, are making critical decisions. Norton explores if traditional concepts such as consumer confusion still apply when an AI system is deceived by manipulated input data designed to bypass detection measures for protected trademarks.
Norton explains that Vision Language Models (VLMs), which combine language models with visual encoders to interpret images, remain vulnerable to adversarial attacks. For example, specially crafted patterns or colors can trick these systems into misidentifying products—a tactic already demonstrated by companies designing clothing that confuses facial recognition algorithms.
The article reviews relevant case law including Qualitex Co. v. Jacobson Products Co., Inc., which affirmed that color alone can be registered as a trademark if it has acquired secondary meaning and is non-functional. Norton also discusses cases such as Brookfield v. West Coast Entertainment Corp., where using hidden code to divert consumers was found to be infringement under the initial-interest confusion doctrine.
However, recent judicial trends suggest courts are now requiring evidence of actual consumer confusion at the point of sale before finding infringement—raising further questions about how this standard applies when AI acts as an intermediary in commerce.
Norton concludes that if courts view AI algorithms as proxies for human consumers, then traditional analyses may still hold; otherwise new legal frameworks might be needed to address potential deception aimed at non-human intermediaries.
