Abstract

The standard three-tier approach for patentability (i.e., novelty, inventive step, industrial applicability) as provided in the World Trade Organization’s (WTO) agreement on Trade Related Aspects of Intellectual Property (TRIPs) is well-documented in the national laws and legal frameworks of most of the TRIPs member countries; nonetheless exclusion of certain inventions from patent-eligible subject matter under TRIPs agreement (such as scientific theories or mathematical methods, abstract ideas, laws of nature, natural phenomenon, business methods, programs for computers, and diagnostic methods) has been implemented by different member states, differently. Evolution of ever-new technologies in biological and computer sciences has created the need for pushing the boundaries of patent-eligible subject matters through revisiting the existing laws and approaches on the subject. Currently, big data science, artificial intelligence (AI) and machine-learning (ML)-assisted innovations involving use of genetic, mathematical, and other optimization algorithms are at the forefront of the technology revolution. Use of AI/ML and the concept of deep learning (neural networks) in accelerating innovations and incentivizing further innovations in the digital healthcare ecosystem (personalized therapies), transformation of clinical trials, and other biomedical applications has substantially disrupted the conventional corporate approaches to patent-eligible subject matter. This article systematically reviews and discusses the subject matter eligibility standards set by the United States Patent and Trademark Office (USPTO) and European Patent Office (EPO), policies and the judicial decisions providing a guiding framework for analyzing eligibility, and anatomy of patent claims for AI/ML-powered innovations. The article concludes that patent laws create a balance between the two opposing and risky ends of under- and over-protection of inventions. Following the USPTO and EPO guidelines, technology owners may get protection of their technological innovations without creating patent thickets and/or restricting the free flow of technical information generated through AI/ML systems.

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