Breakthroughs in artificial intelligence (AI) include both simple quality of life upgrades and transformative innovations spanning every industry, from autonomous vehicles to medical diagnostic tools. Within these numerous technologies, there are a number of applications well worth patenting, begging the question: do any of your AI discoveries fall under intellectual property (IP)?
By asking this question, businesses can take steps to protect their most valuable innovations and ensure they do not fall into the wrong hands.
Protecting your innovation
Do not doubt that plenty of people are already protecting their AI inventions. Since the 1960s, more than 300,000 applications for AI-related patents have been filed, and over 1.5 million scientific papers have been published. The pace has recently quickened, however. Over half of the inventions have been published since 2013, and the ratio of papers to inventions has dropped from 8:1 in 2010 down to 3:1 in 2016, demonstrating the shift towards practical applications.
The shift is not just from theoretical to practical. Recent patent applications show that people are moving towards protecting the commercial applications of their inventions. This should not be surprising – the leading filers of AI patents are commercial organisations: Microsoft, IBM and Samsung, for example.
Data on patent applications shows some interesting geographical trends. For example, when patents are filed first in Japan or the US, around one-third are also filed elsewhere. However, only about four per cent of patents that are first filed in China go on to be filed elsewhere too. This may give information about the likely markets for AI products – or it may say more about the regulatory situation around the world. Indeed, regulators are at best struggling to keep up, and at worst well behind the times, especially on issues related to governance. It is inevitable that many companies are choosing the use of patent law to protect their property.
Generative adversarial networks
One area in particular that deserves a closer look is the machine learning technique for generative adversarial networks. A generative network is trained on a set of data to produce or replicate similar examples of that data. The goal of the technique is to try to fool another network into thinking that the new data is real (that is, not made by the generative network). Examples of these networks in use include creating photos of models for advertising, creating images for video games, and modelling dark matter in space.
They may, therefore, produce some particularly important examples of commercially useful IP. Concerns have also been raised, however, that they could be used to create and populate fake social media profiles or to produce fake pornography without the consent of the original models. Protecting your IP could therefore have ramifications well beyond the immediate commercial applications.
Factors to consider
What action should you take to protect your IP rights? Lawyers suggest that there are three main avenues: patents, trade secrets and copyright.
1) Patenting AI
There is no question that patents are useful: proof against someone else claiming independent invention. However, they do not and cannot cover everything. For example, the creation of training data sets is not covered by patent law — and, of course, if you can feed a learning algorithm the same data set, it can potentially learn the same things.
As a result, you also need to consider issues of copyright and trade secrets. It is important to be clear what basis you are using to protect your IP. Applying for a patent puts your application in the public domain. If a patent is not granted, therefore, your invention is public without any protection.
2) Machine learning: Trade secrets
It may be better to keep your AI innovation as a trade secret, especially if you do not wish to license it to anyone else. Trade secrets are things like programmes, formulae and processes that give a competitive advantage. They are protected by law provided you have taken reasonable steps to keep them secret. However, the definition of reasonable is up to the courts to decide. It follows that you need to take strong measures to safeguard anything that really matters.
Trade secrets can be further protected through passwords and encryption, in addition to limiting who knows about them. Of course, those limitations can also mean we are limiting who gets to use the technology, so trade secrets often work best during times of development and experimentation, before a new technology is released for broader use.
3) Copyrighting AI
You can use copyright law to protect software. However, there are issues with AI, not least that copyright law only applies to something created by a human. Arguably, therefore, the output of an AI algorithm is typically not copyrightable.
As more developers experiment with using AI to write songs, produce images or even create new works of art, copyright law as it applies to AI may evolve. It is an area we should all continue to watch.
Getting ahead in the AI world
The right AI innovation has the potential to bring you leagues above the competition. Commercial entities have caught onto this, meaning we are seeing more businesses look to protect their AI discoveries. The precise path you decide to take will be unique to your business, based entirely on the contextual information and legal advice. However, recognition that these innovations can be protected is a key starting point in the brave new world of AI and machine learning.
Claudio Broggio, Digital Acceleration Leader, SAS