Testing Innovation

How does one determine if an invention is truly innovative, worthy of a patent? Or, say, a song is truly original, worthy of copyright protection? The answer is not very straightforward, says Dr Ryan Whalen of the Faculty of Law, who is using large language learning models to try to improve that decision.

“These are really difficult doctrinal questions to answer that we historically have used pretty hazy legal tests to answer,” he said. “The jurists have built up language and multi-prong tests to determine this, but frankly, a lot of scholars and practitioners think this comes down to gut feelings.

“If you’re a patent examiner, you’re reading a specification of an invention and comparing it to the sum total of human knowledge that preceded it, and asking yourself, is this obviously a new step or not? It’s a difficult question to answer with any degree of certainty and objectivity.”

This is where data and AI can come in. Dr Whalen has developed a model that can study the natural language of a patent application and compare it to other patents and scientific publications to get a more explicit signal of its originality. He layers that on top of the social network of the inventor to see whether the patent filer’s invention is non-obvious compared to those of his contacts (non-obviousness makes it a candidate for a patent).

The intention is to provide jurists with more information. “At this point, it’s much more appropriate to use AI tools to aid human decision-making rather than be a kind of automated decision-making machine. They provide another signal that might help patent judges make a really tough decision,” he said.

Networks and innovation

In China, interestingly, the question of originality is often left to private operators. Alibaba has a platform to compare new products being uploaded with existing ones, to see if there is any infringement of intellectual property (IP). This is faster and more efficient than going through the courts, but it does raise questions about allowing private corporations to be in charge of granting and arbitrating IP rights, he said.

Dr Whalen has also applied his model to innovation policy by looking at collaborative research and the backgrounds of the collaborators, including their disciplines and previous outputs. He and his team find that while the most popular collaborations are between people of similar disciplines working on a similar problem, followed by people of very different disciplines working on a problem that neither of them has worked on before, they do not produce particularly high-impact results.

“The best type of collaboration we identify is where there is a substantial difference between the collaborators, so they work in different fields but they choose to work on a topic that’s somewhat in the middle between them. These types of collaborations are highly under-represented in the empirical data, but they are quite successful.

“One of the conclusions we draw from that is when you’re developing innovation policies and looking at funding at the university or grants level, you might want to take this topographic reality into consideration to guide things like academic hiring and funding disbursements,” he said.

Teething issues

AI is not only an assessment tool, though. It may also one day create innovations itself – in which case, to whom would a patent or copyright be granted? The issue has excited his students, but Dr Whalen believes it is a niche problem that will not exist for the foreseeable future. First, because humans are inevitably involved in terms of directing AI to solve certain problems. And second, if AI did take over product development, it would undermine the profit incentive that underpins patents and motivates developers to bring their products to market.

Even if – when – AI is able to invent things, “it can be remedied quite easily from a legislative standpoint. You just tweak the inventorship definition to include artificial intelligence but grant the patent to the operator of the AI – the person who used it,” he said.

Dr Whalen said it was important to bear in mind that AI is still going through teething issues. “Right now, it’s basically a highly sophisticated autocomplete machine,” he said.

Still, it already has the potential to impact the legal profession by doing tasks that lawyers typically see as low value in terms of income, such as writing wills, or even high value in terms of doing complicated research that generative AI could far more easily conduct and summarise. “For sure there will be resistance to this, but there will also be rewards for agile firms that are able to adopt these new technologies quickly,” he said.