Organizations across all industries are adopting generative AI systems as critical components of their business strategy. These systems often take the form of hosted or on-premises pretrained large language models (LLMs), both proprietary and open source. Organizations acquiring access to pretrained LLMs from a small but growing list of providers can apply various customization techniques. Once customized, LLM usage by an organization can potentially result in an output that constitutes an invention like those on which thousands of U.S. patents are granted every year. As just some examples, a suitably customized LLM could generate a technique to determine a navigation plan consistent with an ODD associated with an autonomous vehicle, an algorithm to predict disease onset based on clinical and environmental factors, or computer code to detect malware by overcoming dynamic obfuscation attempts.
Typical license provisions vest ownership of intellectual property rights in such output in the organization as user of the LLM. A statutory predicate to the contractual outcome regarding ownership of patent rights is the requirement of a sufficient contribution by a natural person in the effort that yielded the output. The issues implicated by this requirement are one development among more to come as patent law and policy try to catch up to proliferating AI technology.
