Ali Talip Pinarbasi

Since the Alice decision, the U.S. courts have adopted different views related to the role of the preemption test in eligibility analysis. While some courts have ruled that lack of preemption of abstract ideas does not make an invention patent-eligible [Ariosa Diagnostics Inc. v. Sequenom Inc.], others have not referred to it at all in their patent eligibility analysis. [Enfish LLC v. Microsoft Corp., 822 F.3d 1327] Contrary to those examples, recent cases from Federal Courts have used the preemption test as the primary guidance to decide patent eligibility. Inventive concepts enabled by new algorithms can be vital to the effective functioning of machine learning systems—enabling new capabilities, making systems faster or more energy efficient are examples of this. These inventions are likely to be the subject of patent applications. However, the preemption test adopted by U.S. courts may lead to certain types of machine learning algorithms being held ineligible subject matter.