Stephen Hou

Artificial intelligence (AI) is a fast-evolving field with new technical methods, systems, and products constantly being developed. This growth has also been reflected in the dramatic increase in patent filings for AI-related inventions. According to Patents and Artificial Intelligence: A Primer from the Center for Security and Emerging Technology, more than ten times as many AI-related patent applications were published worldwide in 2019 than in 2013, and the increasing trend has only continued since.

Although AI-related patent applications have been on the rise, explicit guidance on patentability requirements have only recently begun to be published by patent offices around the world. Indeed, as a burgeoning field of technology, AI inventions have unique features, such as the importance of training data and the lack of explainability and predictability of trained AI models, that differentiate such innovations from traditional types of computer-implemented inventions (CII). 

These features raise questions about the interpretation of disclosure requirements, among other patentability requirements, for AI-related inventions. For example, how much information, such as source code, training data sets, or machine learning model architectures, should be provided to satisfy the written description and enablement requirements of Title 35 of the U.S. Code § 112(a) or analogs in other patent jurisdictions?

As we await further official guidance from the U.S. Patent & Trademark Office (USPTO) on disclosure requirements for AI-related inventions, we can gather initial indications from recent patent prosecution decisions from the Patent Trial & Appeal Board (PTAB) on such issues. In this article, we study a selection of PTAB appeals decisions for applications for AI-related inventions rejected under § 112. To set the background, we first review a classification of AI inventions and USPTO guidelines on disclosure requirements for computer-implemented inventions. After analyzing three case studies, we conclude with general takeaways and best practices, which emphasize that applicants must disclose specific algorithms and implementation details, not just desired outcomes, to satisfy written description requirements.

As AI-generated content floods the digital landscape, how should we rethink prior art in patent law? In this deep dive, Andrew Zhang and Stephen Hou explore the USPTO’s recent questions on the impact of AI, the evolving knowledge of a person having ordinary skill in the art, and whether we should consider a new legal standard for prior art eligibility and assessment.

Stephen Hou, VP & COO of American Patent Agency and long-time customer of Solve Intelligence™, explores the impact of artificial intelligence on patent law, focusing on new regulations for AI patent drafting and AI inventorship across major global patent offices. Stephen describes how jurisdictions in the IP5 (China, Europe, Japan, Korea, and the United States) are updating their guidelines to address the challenges of AI in the patent process, specifically stating that AI cannot be an inventor but highlighting its role in aiding human-driven patentable innovations. This highlights the growing role of artificial intelligence in patent law.