Stupid Patent of the Month: Will Patents Slow Artificial Intelligence?

We have written many times about why the patent system is a bad fit for software. Too often, the Patent Office reviews applications without ever looking at real world software and hands out broad, vague, or obvious patents on software concepts. These patents fuel patent trolling and waste. As machine learning and artificial intelligence become more commonplace, it is worth considering how these flaws in the patent system might impact advances in AI. 

Some have worried about very broad patents being issued in the AI space. For example, Google has a patent on a common machine learning technique called dropout. This means that Google could insist that no one else use this technique until 2032. Meanwhile, Microsoft has a patent application with some very broad claims on active machine learning (the Patent Office recently issued a non-final rejection, though the application remains pending and Microsoft will have the opportunity to argue why it should still be granted a patent). Patents on fundamental machine learning techniques have the potential to fragment development and hold up advances in AI.

As a subset of software development, AI patents are likely to raise many of the same problems as software patents generally. For example, we’ve noted that many software patents take the form: apply well-known technique X in domain Y. For example, our Stupid Patent of the Month from January 2015 applied the years-old practice of remotely updating software to sports video games (the patent was later found invalid). Other patents have computers do incredibly simple things like counting votes or counting calories. We can expect the Patent Office to hand out similar patents on using machine learning techniques in obvious and expected ways.

Indeed, this has already happened. Take U.S. Patent No. 5,944,839, for a “system and method for automatically maintaining a computer system.” This patent includes very broad claims applying AI to diagnosing problems with computer systems. Claim 6 of this patent states:

A method of optimizing a computer system, the method comprising the steps of:

detecting a problem in the computer system;

activating an AI engine in response to the problem detection;

utilizing, by the AI engine, selected ones of a plurality of sensors to gather information about the computer system;

determining, by the AI engine, a likely solution to the problem from the gathered information; and

when a likely solution cannot be determined, saving a state of the computer system.

Other than the final step of saving the state of the computer where a solution cannot be found, this claim essentially covers using AI to diagnose computer problems. (The claim survived a challenge before the Patent Trial and Appeal Board, but the Federal Circuit recently ordered [PDF] that the Board reconsider whether prior art, in combination, rendered the claim obvious.) 

A more recent patent raises similar concerns. U.S. Patent No. 9,760,834 (the ’834 patent), owned by Hampton Creek, Inc., relates to using machine learning techniques to create models that can be used to analyze proteins. This patent is quite long, and its claims are also quite long (which makes it easier to avoid infringement because every claim limitation has to be met for there to be infringement). But the patent still reflects a worrying trend. In essence, Claim 1 of the patent amount