What kinds of IP issues can subsist and arise in AI?
Challenges of protecting AI with patents
Patent or trade secret?


This article is part of a series on AI technology.(1)

What kinds of IP issues can subsist and arise in AI?

As with any kind of technology, patent rights are important. One thing that is unique in the context of artificial intelligence (AI) is that there are a number of different aspects of a particular AI system that can be protected with patents.

AI systems can be thought of in terms of three stages:

  • data acquisition;
  • data processing; and
  • data output.

There may be patentable subject matter in any one of these sets of functions, or in a combination of them. The development of AI tools is generally premised on assembling high-quality, ideally large data sets. In some cases, there may be inventive subject matter in how the data sets are assembled.

Sometimes, a new type of data may be what enables the use of AI – for example, a new type of imaging. Other times, existing data must be transformed in order to enable effective AI treatment. For example, images may be translated into histograms showing pixel brightness data, or a signal trace may be converted to a frequency domain.

Data processing might mean advances in how the actual model is constructed. For example, the algorithms used to produce the model may be new, or there may be inventiveness in the features defined to characterise the data set. Output might mean advances to broader products or processes that result from the use of AI. For example, there may be artifacts of AI use in a product, or the steps of a process may be changed.

There are lots of ways AI or AI-adjacent technology can be protected with patents and, in turn, there are also potentially many aspects that could be subject to third-party patent rights. Companies that are working on or with AI tend to be aware that they are breaking new ground and tend to be working to patent what they can.

Challenges of protecting AI with patents

In many countries, patents cannot be obtained for mere abstract ideas, although there are subtle and not-so-subtle differences in the way the lines are drawn.

In some jurisdictions, the law requires that a patent be directed to a practical application of an abstract idea, rather than an abstract idea itself. There are several ways of establishing a practical application in a given circumstance. For example, one way to establish physicality – that is, a discernible physical effect or change – is by describing a given application of AI in terms of:

  • how it works;
  • how it is used;
  • how the data is retrieved;
  • the problem it is being used to solve; and
  • how each of these may interface with the physical world.

Patent offices generally may view that simply applying standard machine learning techniques to a problem may not be enough for a patent. This means that a patent application relating to machine learning will need to include a lot of detail in order to satisfy the traditional requirements of patentability, such as non-obviousness and enablement. The following questions should be considered:

  • How was the training data set generated?
  • Is there any nuance in that?
  • How was the trained model generated?
  • How is the model used?
  • How does it fit into an overall system that provides a practical application?

Patent applications for machine learning-related inventions cannot be prepared cheaply – applicants will need to make detailed disclosure and this costs in terms of both time and agency fees.

This also means applicants cannot necessarily hold back when filing a patent application relating to machine learning. Applicants should consider whether to file a patent application and make a detailed disclosure or maintain the invention as a trade secret.

Patent or trade secret?

Inventors should consider the following considerations:

  • Will the system be amenable to patenting? If not, trade secret protection may be the best option.
  • If the invention is patentable, how broad a scope of protection will it be possible to obtain? Will it be possible to determine if competitors infringe?

Making this kind of assessment generally requires a detailed understanding of:

  • the invention;
  • how it relates to the previous state of the art;
  • the inventor's business objectives; and
  • the competitive landscape.

For further information on this topic please contact Patrick M Roszell, Alice Tseng or Graham Hood at Smart & Biggar by telephone (+1 416 593 5514) or email ([email protected], [email protected] or [email protected]). The Smart & Biggar website can be accessed at www.smartbiggar.ca.

Neil Padgett, lawyer and patent agent, participated in the roundtable discussion on which this article is based.

Endnotes

(1) This article is part of a series based on the roundtable discussion "In-house counsel primer: Managing IP and compliance risks in artificial intelligence and a digital world". For other articles in the series, see: