"Secure and Ethical Innovations: Patenting Ai Models for Precision Medicine, Personalized Treatment, and Drug Discovery in Healthcare"

Abstract

AI's prominence has surged in the digital economy's evolving landscape in the last decade, marking a transformative shift. This paper delves into the global race for AI development, examining the distinctive strategies adopted by countries and their evolution over time. The study uses worldwide patent data to identify countries specializing in AI technologies, analyse their efforts to attract foreign inventors and explore trends in specific AI techniques. The research introduces innovative indices, the National Breeding Ground (NBG) and the International Breeding Ground (IBG), shedding light on market favourability and cross-border patent protection. Comparing different strategies for identifying AI patents, the study employs a keyword-based approach and contrasts its results with those based on the International Patent Classification. The findings demonstrate the efficiency of the keyword-based method in capturing patents associated with the essence of AI, providing a high-quality dataset. The paper also delves into the fundamental distinction between software and ML, addressing challenges in proving the patentability of software innovations. The discussion extends to the patenting landscape in India, offering insights into the complexities of obtaining software patents and outlining strategies for drafting claims and specifications. Transitioning to the realm of ML, the paper defines ML and its subset, Deep Learning, emphasizing the programming of computers to learn from data. It navigates through examples of ML applications in chemistry, highlighting innovative patents that leverage ML for pathogen detection and regulatory molecule identification. The discussion extends to patentability considerations for European ML inventions, categorizing ML techniques based on their potential for technical solutions to technical problems. The paper offers a comprehensive overview of the global AI race, nuances in patent identification strategies, challenges in software patenting, and the patentability landscape for ML inventions, contributing valuable insights to the intersection of law, technology, and innovation.