The Federal government is accelerating AI‑enabled innovation by launching the Genesis Mission, a sweeping national initiative to accelerate scientific discovery using artificial intelligence. The goal of the Mission is to “build an integrated AI platform to harness Federal scientific datasets…to train scientific foundation models and create AI agents to test new hypotheses, automate research workflows, and accelerate scientific breakthroughs.” Under the leadership of the Assistant to the President for Science and Technology, the Department of Energy will implement the Mission.
AI, Algorithms and Abstract Ideas: Federal Circuit Reinforces Limits in Recentive v. Fox
In April, the Federal Circuit issued a significant patent law ruling involving artificial intelligence. In Recentive Analytics, Inc. v. Fox Corp, the Court addressed a core question facing many AI-driven businesses: When are solutions applying machine learning to real-world problems inventive and patentable? The Federal Circuit affirmed the trial court’s dismissal of the underlying case at the pleading stage under § 101 and held that applying generic machine learning models to scheduling and programming tasks—without disclosing any technological advances to the underlying machine learning techniques—failed to meet the eligibility standards under 35 U.S.C. § 101.
The Role of Generative Artificial Intelligence in Patent Litigation: A New Frontier for Inventorship, Infringement and Validity
As generative artificial intelligence (AI) continues to transform industries, its impact on patent law is raising critical legal questions. From the recognition of AI as an inventor and potential infringement risks posed by the AI-generated outputs to the use of AI in patent validity challenges, the legal landscape is rapidly evolving. This article explores how generative AI is reshaping patent litigation, including the legal implications for inventorship, infringement and validity.
Update on Artificial Intelligence: USPTO Urges Federal Circuit to Affirm Decision That AI Cannot Qualify as an “Inventor”
In three previous blog posts, we have discussed recent inventorship issues surrounding Artificial Intelligence (“AI”) and its implications for life sciences innovations – focusing specifically on scientist Stephen Thaler’s attempt to obtain a patent for an invention created by his AI system called DABUS (“Device for Autonomus Bootstrapping of Unified Sentence). Most recently, we considered Thaler’s appeal of the September 3, 2021 decision out of the Eastern District of Virginia, which ruled that under the Patent Act, an AI machine cannot qualify as an “inventor.” Continuing this series, we now consider the USPTO’s recently filed opposition to Thaler’s appeal.
Update on Artificial Intelligence as a Patent Inventor
Our previous blog posts, Artificial Intelligence as the Inventor of Life Sciences Patents? and Update on Artificial Intelligence: Court Rules that AI Cannot Qualify As “Inventor,” discuss recent inventorship issues surrounding AI and its implications for life sciences innovations. Continuing our series, we now look at the appeal recently filed by Stephen Thaler (“Thaler”) in his quest to obtain a patent for an invention created by AI in the absence of a traditional human inventor.
Update on Artificial Intelligence: Court Rules that AI Cannot Qualify As “Inventor”
Striking a blow to patent applicants seeking to assert inventorship by artificial intelligence (“AI”) systems, the U.S. District Court for the Eastern District of Virginia ruled on September 3, 2021 that an AI machine cannot qualify as an “inventor” under the Patent Act. The fight is now expected to move to the Federal Circuit on appeal.
Artificial Intelligence as the Inventor of Life Sciences Patents?
The question whether an artificial intelligence (“AI”) system can be named as an inventor in a patent application has obvious implications for the life science community, where AI’s presence is now well established and growing. For example, AI is currently used to predict biological targets of prospective drug molecules, identify candidates for drug design, decode genetic material of viruses in the context of vaccine development, determine three-dimensional structures of proteins, including their folding form, and many more potential therapeutic applications.
Synthetic Data Gets Real
As we mentioned in the early days of the pandemic, COVID-19 has been accompanied by a rise in cyberattacks worldwide. At the same time, the global response to the pandemic has accelerated interest in the collection, analysis, and sharing of data – specifically, patient data – to address urgent issues, such as population management in hospitals, diagnoses and detection of medical conditions, and vaccine development, all through the use of artificial intelligence (AI) and machine learning. Typically, AIML churns through huge amounts of real world data to deliver useful results. This collection and use of that data, however, gives rise to legal and practical challenges. Numerous and increasingly strict regulations protect the personal information needed to feed AI solutions. The response has been to anonymize patient health data in time consuming and expensive processes (HIPAA alone requires the removal of 18 types of identifying information). But anonymization is not foolproof and, after stripping data of personally identifiable information, the remaining data may be of limited utility. This is where synthetic data comes in.