Contemplating the Impact of the USPTO’s AI-Focused Patent Eligibility Guidance on Networking Applications

By Frank L. Gerratana, Jonathon P. Western

For years, artificial intelligence (AI) has been deployed in the networking industry to make evaluations and predictions about computer networks for the purpose of improving overall efficiency, performance, and security.  Unlike human administrators who act in response to events that occur in the network, AI allows for predictive network management that is proactive, eliminating performance degradations and security breaches before they ever occur.  As the need for patent protection continues to grow, patent applicants in this technology area are often challenged by subject matter eligibility requirements that can render even the most innovative AI-powered networking tools unpatentable—if not claimed correctly.

In this article, we cover new guidance issued by the United States Patent and Trademark Office (USPTO) on patent subject matter eligibility under 35 U.S.C. § 101, focusing on AI and other software-related emerging technologies.  Utilizing USPTO’s Example 47: Anomaly Detection as a case study, we contemplate the implications for networking applications, specifically, and the patent eligibility of claims directed to use of AI within the networking field. 

Example 47 describes a hypothetical invention involving the use of an artificial neural network (ANN) to detect anomalies in a computer network. This ANN, a type of machine learning model with interconnected "neurons," is designed to be more accurate and efficient than traditional methods for detecting network anomalies. The model may be implemented by an application-specific integrated circuit (ASIC), which itself may be customized for AI applications and offers superior performance compared to traditional computer processing units (CPUs). 

In our analysis of the latest guidance update, we dissect three sample independent claims presented in Example 47, one of which is deemed ineligible, and the other two eligible. These samples provide valuable insight on the patent eligibility of claims directed to networking applications that utilize AI. We conclude by highlighting key takeaways for developing compelling claims less likely to encounter a subject matter eligibility rejection under 35 U.S.C. § 101.

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    Frank L. Gerratana

    Member

    Frank Gerratana is a Member at Mintz who partners with innovators to develop and execute smart patent strategies to compete in global markets. His clients include companies pioneering next-generation electrical and computer technologies including cryptocurrency and blockchain systems, social media and Internet applications, autonomous vehicles, and medical tools and devices.

    Jonathon P. Western

    Of Counsel

    Jonathon P. Western, an Of Counsel at Mintz, is a versatile patent attorney whose practice encompasses US and international patent prosecution and portfolio management, strategic IP counseling, and post-grant proceedings before the USPTO. He works with clients in a broad spectrum of industries, such as technology, artificial intelligence, semiconductors, automotive, hospitals, and education.