Vote Dole by Kit Peters (CC BY-NC-SA 2.0)

Vote Dole by Kit Peters (CC BY-NC-SA 2.0)

Committees / News

Toward a Canadian Knowledge Transfer Strategy: My Appearance Before the Standing Committee on Industry, Science and Technology

The House of Commons Standing Committee on Industry, Science and Technology recently launched a study on intellectual property and tech transfer, which it hopes will feed into the government’s national IP strategy. I appeared before the committee yesterday, which provided an opportunity to provide a perspective that shifted away from encouraging greater university patenting and instead emphasized that the real goal should be knowledge transfer, not just tech transfer. I noted that knowledge transfer certainly incorporates tech transfer but it also includes research papers, data trials, educational materials, and highly qualified students and personnel.  My opening remarks also highlighted potential strategic reforms including emphasizing open access, crafting an anti-IP abuse statute, and expanding fair dealing with additional categories or adopting fair use provisions.  The ensuing discussion touched on a wide range of issues, including patent and copyright trolls.  My opening remarks are posted below.

Appearance before the House of Commons Standing Committee on Industry, Science and Technology, June 1, 2017

Good morning. My name is Michael Geist.  I am a law professor at the University of Ottawa, where I hold the Canada Research Chair in Internet and E-commerce Law. My areas of specialty include digital policy, intellectual property, and privacy. I have appeared many times before this committee on IP issues and as always, I appear in a personal capacity representing only my own views.

I’d like to start by welcoming this committee’s study on an important aspect of IP. However, I respectfully suggest that the name of the study gets it wrong. I understand that the notion of “tech transfer” has taken hold in some discussions on how Canada can shift innovative research from Canadian campuses to exciting new commercialization opportunities. However, I’d like to suggest that the real goal is not tech transfer, but knowledge transfer.

Knowledge transfer encompasses a far broader set of policy goals that seek to take the knowledge that emerges from within our labs and classrooms and bring it out to the public – whether for commercialization, better public policies, or a more informed and engaged public. Knowledge transfer certainly includes tech transfer but it also includes research papers, data trials, educational materials, and highly qualified students and personnel. Simply put, if the target is just IP and tech transfer, we miss out of many of the benefits that come from innovative post-secondary research and run the risk of establishing the wrong incentives within our policy frameworks.

Further, the potential emphasis on the U.S. Bayh-Dole approach is misplaced. As you heard from department officials, there is little evidence that the policies governing who owns IP rights have an overriding impact on the success of tech transfer as measured by the volume of patents and licenses.

This should come as little surprise to anyone who has spent time on campuses with academic researchers.  The metrics of success in the academic environment – publications, grants, tenure, chairs, successful students – have little correlation with commercialization.  Even for those with commercial interests, those are often achieved through consulting arrangements or other mechanisms where the business expertise is left to business people.

I would argue that the emphasis on university-based patenting is misplaced. It can have a corrosive effect on universities, who forego important, publicly-funded research in favour of potential licensing or patenting opportunities.  With properly funded institutions, there is no need to chase licensing dollars. Instead, the cutting edge research ends up in the hands of businesses who can better leverage it for commercialization opportunities.  This should not be viewed as lost revenue for universities or their researchers, but rather as a better return on the public’s investment in post-secondary research.

From an IP strategy perspective, I’d like to focus on two broad issues.

Open Access Publishing

If the currency of academics is publishing – not patents – then the challenge is how to ensure that the published research ends up as broadly distributed as possible. While it has captured limited attention outside of educational circles, the Internet has facilitated the emergence of open access publishing of research, transforming the multi-billion dollar academic publishing industry and making millions of articles freely accessible to a global audience. The move toward open access means that global research is far more accessible to everyone – scientists, researchers, businesses, and the general public.

The three federal research granting institutions – CIHR, NSERC, and SSHRC – have adopted open access mandates that requires recipients of federal funding to make their published work available under open access.

This helps foster greater collaboration between researchers and the business community with improved access leading to commercialization opportunities that might otherwise be missed. Further, openly available articles are already being incorporated into teaching materials, thereby replacing conventional textbooks and removing the need for copyright permissions and fees.

As for government strategies, open access mandates should only be the beginning.  Moving toward open trial data and open book publishing are the next steps in linking significant public funding to enhancing public access to their investment.

IP Legal Barriers

Given that Canada already meets or exceeds international standards on IP, a key concern is to address the abuse of IP rights that may inhibit innovation. The Canadian government could address the issue through an anti-IP abuse law.

There is no shortage of policy possibilities. For example, in the patent arena, countering patent trolls could include a prohibition against legal demands that are intentionally ambiguous or designed to induce a settlement without considering the merits of the claim. Other reforms could include requiring public disclosure of the demand letters, reforming the Competition Act to give the Competition Bureau the power to target anti-competitive activity by patent trolls, and giving courts the power to issue injunctions to stop patent trolls from forum shopping.
There is also a need to address IP barriers that may limit the ability to take research from labs into the commercial world. For example, the federal government placed a big bet in this year’s budget on becoming a world leader in artificial intelligence (AI). Yet  restrictive copyright rules may hamper the ability of companies and researchers to test and ultimately bring new AI services to market.

What does copyright have to do with AI?

Making machines smart – whether engaging in automated translation, big data analytics, or new search capabilities – is dependent upon the data being fed into the system. Machines learn by scanning, reading, listening or viewing human created works. The better the input, the better the output.

Copyright law crops up because restrictive rules may limit the data sets that can used for machine learning purposes, resulting in fewer pictures to scan, videos to watch or text to analyze. Given the absence of a clear rule to permit machine learning in Canadian copyright law (often called a text and data mining exception), our legal framework trails behind other countries that have reduced risks associated with using data sets in AI activities.

There are two ways to overcome the copyright AI barrier. First, Canada could emulate the U.S. fair use model by making the current list of fair dealing purposes illustrative rather than exhaustive. The U.S. exception is open to any purpose, as striking a fair balance depends upon the use of the work, not the purpose of the copying. Since machine learning does not harm the primary purposes of the original work, most text and data mining will qualify as fair use.

Second, other countries have tried to address the issue by creating a specific exception for text and data mining or computer informational analysis. For example, Britain’s exception allows copies of works to be made without permission of the copyright owner for the purposes of automated analytical techniques to analyze text and data for patterns, trends, and other information.

I look forward to your questions.