Robert Plotkin

Imagine a pharmaceutical researcher in 2015 searching for new drug candidates to treat a rare disease. Through traditional methods, they might screen a few thousand compounds over several months, carefully evaluating each candidate’s potential based on known chemical properties and biological mechanisms. Fast forward to 2025: using modern AI tools, that same researcher can screen millions of compounds in days, with the AI system predicting binding affinities, potential side effects, and even suggesting novel molecular structures that human chemists might never have conceived. This dramatic expansion of capabilities raises a crucial question for patent law: Has the widespread adoption of AI tools fundamentally changed what constitutes “ordinary skill” in drug discovery?

Software as a Service (SaaS) has emerged as a dominant model for delivering software solutions. Before SaaS, we tended to buy and use increasingly powerful computers to run increasingly powerful software locally. The advent of high-speed, wireless, always-available Internet connectivity shifted this model towards cloud-based software execution, with our local devices acting mainly as “thin clients” that interface with remotely executed software. However, certain functions, such as highly sensitive encryption and low-latency tasks like conversational AI, still need to be performed locally. This results in a complex and fluid division of labor among distributed systems.

While many see intellectual property merely as a shield, its greater power rests in its strategic use to spark innovation and propel business growth. In this article I describe a systematic approach for developing IP strategies that are tailored to the technology and objectives of each business, so that the resulting IP can be used to drive the achievement of those goals.

Artificial Intelligence (AI) stands at the forefront of innovation, transforming industries and shaping the future of global economies. Although AI innovators understand the value of intellectual property (IP) protection for their innovations, they often don’t know how to secure the right kind of IP protection for their innovations. Employing a process for systematically mining AI innovations to create a map of those innovations is one option for identifying the most suitable form(s) of IP protection to obtain, based on the innovation and the business model within which that innovation will be commercially deployed.

The first software patent was granted in 1968. It’s now been three decades since the “Year of the Algorithm” in 1994, when cases such as In re Allapat, In re Lowry, and In re Beauregard initiated a wave of software patents. Well over half of U.S. patents granted annually are at least “software-related,” and even a cursory search of U.S. patents reveals software patents in fields ranging from encryption to speech recognition to network security. Why, then, do so many people continue to think that software cannot be patented at all? What explains the stark contrast between the long-standing legal reality and the beliefs of otherwise well-informed engineers, high-tech business people, and even some lawyers?