This is a series of four articles on AI and IP protection:
If you are an AI entrepreneur that wants to try to patent something, it is certainly useful to start from mapping the landscape of existing patents to see whether something similar has already been filed and whether an opportunity materializes for your innovation.
This is a very specific process related to what you want to build and goes beyond the scope of this post, but what it can be intriguing to present is the current AI spectrum of patents and players.
CB Insights analyzed over 1,150 AI companies in the last decade (since 2009) finding that 21% applied for a patent and only 11% eventually got at least one. In particular, in the last five years, the issue is becoming more tricky and the US IP law started drawing some clear lines (the most famous case up to date is Alice Corp. v. CLS Bank, where they rejected a patent application on computer software because too abstract). Nowadays, it seems therefore clear that training sets, proprietary information, a particular expression of source code, and many other steps in an AI value chain cannot be patented. On the other side though, trade secret protection is suitable for a wide variety of circumstances (e.g., neural networks, training sets, AI-generated code, learning algorithm, etc.).
CBinsights further divided the patents of the “Big 5” (Apple, Google, Amazon, Facebook, and Microsoft) from the patents obtained by startups.In this fashion, big tech giants patented several things, although many times because the software is often attached to a hardware component (Amazon for logistic robotics, Apple for the iPhone, and even Google for their smartphone products). Google leads the pack in applying for AI patents, while Microsoft is the most prolific in filing for patents overall. We are talking here about a relatively small magnitude (less than 40 AI patents in 2017, with a peak of 164 in 2015). On the startup side instead, Cortica and Numenta are dominating this space (with 38 and 37 patents, respectively) followed by Butterfly Network (27), SoundHound (26) and Smart Drive (24). Interestingly enough, in addition to traditionally strong-IP fields (e.g., healthcare), most of the patents have been filed on horizontal AI type of applications (in other words, platform, neuroscience approach to AI, GAI, core AI, etc.).
If we then widen the spectrum of what is being patented worldwide at any level and from any organization, it seems clear that most of the AI patents focus on enabling intelligent robots (e.g., self-driving cars, automated delivery drones, AI assistants, etc.), deep learning, face recognition and AI hardware (especially in China).
Finally, US and China are the two countries where most of this innovation (over 50% of the patents registered) has been happening over the last decade or so (which one of the two is leading is still controversial though), followed by Japan, South Korea, Germany, Canada, the U.K., Australia, India, and Russia. China seems to have uncontested supremacy over deep learning and machine vision, where US is keener to develop NLP and other machine learning technologies, and it is filing up patents at a much faster pace than its American counterparts (interestingly enough, Chinese researchers more than doubled the number of AI scientific papers published in 2017 compared with 2010, trend that is going downward in the US). Europe instead places itself in the middle, with a flattening growth in patents applications (OECD, 2017) and accounting for 10% of the US quantity. However, even if patenting is not so widespread in Europe, the scientific publication side is steadily growing (and of course, many recent announcements have been done to better position Europe in the AI race).
When I started thinking about this IP issue, my main question was “how is it possible to patent something when the market is driven by open-source forces?”, cause I was naively assuming the two elements being incompatible. The reality is that they are not because they might serve different purposes and appear in different stages of a company life.
Bottom line, keep your mind open about the chance of protecting your IP when you are very early-stage or to send a signal out there about what you are building, but do not waste too much money and time on it if you are already building a good traction quickly. Do not also assume that a patent will vouch for everything you say and do because it won’t, and it is not a substitute to a great co-founding team or a strong market validation, and by all means not having a patent does not imply your product is inferior or your company is worse than others.
To say that with terms that we all know, “patents are vitamins, not painkillers”.
I am always interested in speaking to, learning from or simply connecting with interesting founders working in highly impactful fields like life sciences, energy, and others. If you are one of them, feel free to reach out here!
OECD. (2017).“Science, innovation and the digital revolution”, in OECD Science, Technology and Industry Scoreboard 2017: The digital transformation, OECD Publishing, Paris.