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Emily Xiang is an IPilogue Writer, a Senior Fellow with the IP Innovation Clinic, and a 3L JD Candidate at Osgoode Hall Law School.

For the first time in decades, the US Supreme Court will engage with enablement in patent applications. On November 4th, 2022, the Supreme Court to review the Federal Circuit’s decision in Amgen v Sanofi, against the . Specifically, Amgen seeks to appeal a , in which the court found Amgen’s patents invalid for lack of enablement. 

The requirement of enablement in US patent law is codified in , which requires that the specification of a patent application “enable any person skilled in the art…to make and use” the invention in question. The in Amgen v Sanofi is whether this statutory requirement governs enablement (that the specification teaches those skilled in the art to “make and use” the claimed invention) or whether it must instead enable those skilled in the art “to reach the full scope of the claimed embodiments” without “undue experimentation” (characterized by substantial “time and effort”). 

In 2014, Amgen sued Sanofi for infringing on its patents concerning drugs for lowering cholesterol. The genus patents specifically cover that bind to the PCSK9 protein in the body. The patents disclose the amino acid sequences for 26 antibodies that bind to one or more of 15 residues found on the PCSK9 protein. Importantly, the claims at issue are considered , in which the antibodies are not claimed based on their structural components but rather on what they do. 

On January 3rd, 2023, many interested parties submitted to offer the Supreme Court their take on the issue to be considered. For instance, in a brief submitted by a group of , it was argued that the Federal Circuit’s standard imposes “an impossible burden” on patentees and that such a decision represents “a categorical shift in thinking away from teaching the PHOSITA and towards a precise delineation of the boundaries of the claim”. The professors further submitted that such a heightened requirement would be especially burdensome for patentees seeking to protect their innovations in the fields of chemistry and the life sciences, as “a chemical genus with any decently large number of species will never be able to satisfy the new enablement standard”. 

Other parties in support of Amgen presented some other reasons as well. In their amicus brief, the stated that the court’s reasoning “leaves patent practitioners guessing about how to advise client-inventors regarding the extent of disclosure required”. The , warned of the adverse impact that the new enablement requirement might have on the effectiveness of patent incentives for investors to contribute towards research and development, especially in the case of startups and smaller companies.

Moreover, the has filed a motion for leave to participate in oral argument, claiming a “paramount and unique institutional interest and perspective” – that is, the perspective of individuals and companies working in the chemical, pharmaceutical, and biotechnology fields. CHAL asserts that the Federal Circuit’s enablement standard potentially jeopardizes the benefits of many modern innovations and that adhering to the plain meaning of 35 USC s. 112 should continue to be the prevailing approach.

The Supreme Court’s decision regarding the enablement standard for functional claims could also have wide-reaching implications that spill over into other fields, such as technology and computer-implemented inventions. By too narrowly focusing on the “full scope of the claim” and “undue experimentation” instead of on what those skilled in the art could determine from the specification, it is unclear how broader claims for (such as those that describe the desired result to be achieved by the AI rather than its structural components or any specific software solutions) might fare in the face of such a standard. 

Amgen v Sanofi is scheduled to be heard by the US Supreme Court in the upcoming Spring Term.

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Sally Yoon is an IPilogue Writer, IP Innovation Clinic Fellow, and a 3L JD Candidate at Osgoode Hall Law School.

This past summer, Amazon made headlines when it announced an update that would make Alexa capable of , just after hearing under a minute of audio. While people are still unsure as to whether this is heartwarming or just plain creepy, AI continues to evolve, with recent developments showing its ability to not only mimic human speech but also singing.

AI-based audio technologies have been making waves worldwide. Last month, Google announced “”, which proposes “a new framework for audio generation that learns to generate realistic speech and piano music by listening to audio only”. More recently, , China’s leading music entertainment platform, demonstrated the influence of AI in music. According to Music Business Worldwide, the company has released over - one of the tracks surpassing 100 M streams. TME utilized a “patented voice synthesis technology” called “Lingyin Engine”, which the company claims can “quickly and vividly replicate singers’ voices to produce original songs of any style and language.” South Korea has been a strong player, with its most prominent AI-based audio start-up, . The company claims that its voice synthesis and real-time voice enhancement technology can create a hyper-realistic voice that is indistinguishable from real humans.

So far, these AI voice technologies have largely been publicized as an innovative way of and preserving the memories of lost loved ones. Nevertheless, companies will likely aggressively pursue these technologies for profit. In fact, according to NME, (record label of globally recognized boy band, BTS), which equates to about $44.6 million Canadian Dollars. last month, HYBE’s CEO confirmed that the company plans to “unveil new content and services to [its] fans by combining our content-creation capabilities with Supertone’s AI-based speaking and singing vocal synthesis technology.”

HYBE’s huge investment in Supertone starts to make a little more sense once we discover that the company’s “” in Q3 2022 was its Artist ‘Indirect-involvement’ revenues. BTS’s success suggests how more entertainment companies will follow HYBE’s footsteps to increase profits without the headache of coordinating any physical appearances of its artists.

The development of voice AI opens a plethora of legal questions to consider. These issues were highlighted more recently by the recent - who is given permission to use it and does the artist hold any rights to license their voice to third parties for use in other films? More specifically for , how do we determine who owns the copyright to the work? Does it make sense to look at the creators of the voice AI technologies themselves or at the source of the vocal data (the artist)? These questions clarify that the development of voice AI places our artists in a very vulnerable position — suggesting a much-needed intermission for this chaotic programme.

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Meena Alnajar is an IPilogue Senior Editor, IP Innovation Clinic Senior Fellow and 3L JD Candidate at Osgoode Hall Law School.

Photo by Buda Photography

On November 9, IP Osgoode, Reichman University and Microsoft hosted the first in-person Bracing for Impact Conference since 2019. The conference focussed on “The Future of AI for Society.” While AI is full of exciting possibilities, real-world application and integration are relatively nascent. Implementing AI technology in society requires complex interdisciplinary engagement between engineers, social scientists, application area experts, policymakers, users, and impacted communities. At the conference, an esteemed lineup of speakers across disciplines discussed the forms that interdisciplinary collaboration could take and how AI can help shape a more just, equitable, healthy, and sustainable future.

Spot was one of many unforgettable guests at IP Osgoode’s Bracing for Impact Conference. A four-legged metal friend, Spot is an inspection robot from Boston Dynamics that explores hazardous spaces to capture digital images and generate mapping data. Currently, Spot is used by , Boston Dynamics’ North American partner. Spot is tasked with gathering data in hazardous spaces, such as construction sites and mines. The hope is that Spot can reduce harm and liability that stem from working in dangerous spaces. Spot has pre-assigned walking routes over huge expanses. While walking, Spot can take images through a mounted camera. We watched Spot scan a symbol resembling a QR code on the wall before completing its pathway. Spot also avoided obstacles, such as a large box in its way, by walking over the obstacle, walking backwards, or walking around it. MFE Inspection Solutions has other inspection drones that create 3D maps, but Spot was the four-legged star of the conference, with the flexible limbs necessary to enter these spaces and promote workplace safety.

The Bracing for Impact Conference closed with two impactful announcements: the launch of 91��ɫ’s Center for AI and Society (CAIS) and the IP Osgoode David Vaver Medal of Excellence in Intellectual Property Law Award Ceremony.

Launch of the 91��ɫ Centre for Artificial Intelligence & Society

Photo by Buda Photography

, Vice President of Research and Innovation at 91��ɫ, introduced the Centre for AI and Society. Given that 91��ɫ has an edge in its application of AI in its academic programs, this Centre is dedicated to addressing AI’s ethical issues to ensure AI is applied to society’s benefit. Directors Professor , Associate Professor at Osgoode, and Professor , Professor and 91��ɫ Research Chair in human and computer vision, lead the centre. The Centre is the positive result of 91��ɫ’s AI Task Force, whose report “Fostering the Future of AI” demonstrated that an organized research unit was needed to examine AI’s impacts on society. The Centre brings members from seven faculties together to understand AI’s potential, mitigate AI’s risks, and make AI use more inclusive. The Centre is currently partnered with Microsoft and is a collaborative mission that will hopefully expand to include more members to investigate how AI can empower society.

IP Osgoode David Vaver Medal of Excellence in Intellectual Property Law Award Ceremony

Photo by Buda Photography

For the first time since 2018, the IP Osgoode David Vaver Medal of Excellence in Intellectual Property Law was presented in person. This award recognizes students who are involved with and committed to IP law during their time at Osgoode. Toronto-based artist and designer for the Royal Canadian Mint designed the medal. The Honourable Justice Rothstein and Professor Vaver presented the award. Justice Rothstein remarked, “medals and awards are named after people for a reason; this one carries Professor Vaver’s name … he is one of the foremost academics of our generation.” Justice Rothstein highlighted Professor Vaver’s achievements, such as being cited 23 times by the Supreme Court of Canada, an impressive feat considering how few IP cases are argued at that level.

Each recipient offered reflections on the award and hopes to engage with AI in their future career. The 2019 recipient, Aviv Gaon, remarked that the medal reflects the highlights of his rich academic journey. The 2020 recipient, Ian Stedman, pointed out that AI has tremendous potential to help those with rare diseases. The 2021 recipient, Ryan Wong, hopes to exude some of Professor Vaver’s qualities, such as his rigour and spirit. Wong urged us to be ready to tackle complicated issues as we approach the inevitable: the inclusion of AI in our society. This year’s recipient, Bonnie Hassanzadeh, told the audience that IP Osgoode helped her find purpose and commitment in her law student experience and she looks forward to the future of AI and precision medicine.

Professor Vaver concluded the ceremony with a reminder: In IP law, you cannot be a hedgehog, a creature who knows only to roll into a ball. Rather, we must be foxes, remaining flexible and knowing many things deeply. This award serves as a reminder to be creative and invested in new ideas, as these skills are essential in the practice of IP law.

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Pankhuri Malik is an Osgoode LLM Graduate, IPilogue Writer and IP Innovation Clinic Fellow.

Photo by Buda Photography

On November 9, IP Osgoode, Reichman University and Microsoft hosted the first in-person Bracing for Impact Conference since 2019. The conference focused on “The Future of AI for Society.” While AI is full of exciting possibilities, real-world application and integration are relatively nascent. Implementing AI technology in society requires complex interdisciplinary engagement between engineers, social scientists, application area experts, policymakers, users, and impacted communities. At the conference, an esteemed lineup of speakers across disciplines discussed the forms that interdisciplinary collaboration could take and how AI can help shape a more just, equitable, healthy, and sustainable future.

91��ɫ and IP Osgoode have been frontrunners in conversations surrounding AI since 2016, before it was cool. The Panel 2 discussion - “AI for the Future of Legal Practice – Self-Regulation, Access to Justice and the Importance of Legal Data” - is a prime example of the forward-thinking nature of the organizations, which seek to use their diverse and interdisciplinary structure to have well-rounded conversations about incorporating AI in legal practice.

The Panel discussed:

1. AI in law school curriculums;
2. AI as an aid to Access to Justice; and
3. The interplay between AI and Data.

Chaired by Osgoode Prof. Jonathon Penney, Panel 2 featured Sari Graben (Associate Dean at Lincoln Alexander School of Law, Toronto Metropolitan University), Nye Thomas (Executive Director, Law Commission of Ontario), Professor D’Agostino (who needs no introduction) and Ryan Wong (Osgoode Hall Law School alum and Associate at Smart & Biggar, Toronto).

Incorporation of AI in law school curriculums

Bringing her knowledge and experience in devising law school curricula to the table, discussed the delicate balance that must be achieved between law and technology in academics. She highlighted that due to AI’s evolving nature, the rapid pace of innovation in the space, and the lack of conversation between legal and technological experts, AI is a difficult subject to teach in law schools. Sari elucidated the need for innovative and critical thinking when approaching AI’s interplay with law.

Broadly, Sari discussed that to incorporate AI into the practice of law, we must first recognize that human involvement in legal decision-making traverses just a series of rules that must be applied uniformly to a situation in an automated manner.

Sari stated that human sensibilities, the feeling of being “heard,” and the trust placed by the public in a human authority figure who makes rational decisions are irreplaceable in legal practice. Using AI to crystallize a set of rules depersonalizes the law and isolates persons from the human element of justice.

Keeping these considerations in mind, Sari discussed that these challenges might be overcome by connecting technology and law, such that technically qualified and capable people become integral for implementing AI in law and in the use of law to regulate AI. Only dedicated persons actively working towards advancing the field can build a longstanding relationship between the two.

AI as an aid to Access to Justice

discussed the incorporation of AI in mitigating Canada’s prevailing Access to Justice crisis. To mitigate the challenges of a slow, expensive, and opaque system characterized by racial bias and unequal means to access the judiciary, Nye recommended promoting conversations around trustworthy and legal AI. Nye stated that AI-related policy needs to be developed as AI must be incorporated into the due process of law to increase transparency, reduce costs and implement a more uniform justice system.

Nye proposed that a primary tool for this would be the regulation of AI. He highlighted that not all systems that fall within the definition of AI impact society. Nye recommended devising a system to identify impactful AI and developing a regulatory system to monitor and implement it to ensure equal access and transparency for the public.

AI and data protection and ownership

Osgoode’s own discussed the need for different academic fields to come together for an interdisciplinary approach to AI. Prof. D’Agostino stressed the need for the university to lead the debate and conversation around interdisciplinary AI since different departments within the university are already working on various aspects of AI related innovation.

Prof. D’Agostino discussed the need to investigate AI-related data ownership. Through the IP Innovation Clinic, IP Osgoode has undercut 2 million dollars in IP-related services. And while offering pro-bono services to start-ups and individuals looking to grow their business through IP development, Prof. D’Agostino and her team have created a bank of commonly asked questions in the field of IP and developed the AI-driven .

, an Osgoode Alum and Associate at prominent IP Boutique firm Smart & Biggar, demonstrated how the ChatBot’s bank of intent-based questions provides free and instantaneous IP innovation information without the need for human intervention.

Key takeaways

Since its inception, AI has been received with scepticism. While some warn of it replacing humans, others are optimistic about the scope of innovation AI provides. This panel expressed optimism and presented a picture of AI technology combatting prevalent issues in legal practice.

In the debate about whether technology is new to the law or just another challenge that the law must overcome, the panellists gave me the impression that technology will be the law’s best friend in the coming years. 

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Photo by Buda Photography

Jasmine Yu is a Senior Editor and a 2L JD/MBA Candidate at the University of Toronto.

Nancy Chen is an IPilogue Writer and a 2L JD/MBA Candidate at the University of Toronto.

On November 9, IP Osgoode, Reichman University and Microsoft hosted the first in-person Bracing for Impact Conference since 2019. The conference focused on “The Future of AI for Society.” While AI is full of exciting possibilities, real-world application and integration are relatively nascent. Implementing AI technology in society requires complex interdisciplinary engagement between engineers, social scientists, application area experts, policymakers, users, and impacted communities. At the conference, an esteemed lineup of speakers across disciplines discussed the forms that interdisciplinary collaboration could take and how AI can help shape a more just, equitable, healthy, and sustainable future.

sought to contextualize the promise of AI for the future of urban development and was chaired by Hon. Maurizio Bevilacqua, the Mayor of Vaughan. As an elected Mayor, Hon. Bevilacqua put this panel in the context of AI serving the purpose of improving lives — a goal of the of which he and Professor Pina D’Agostino, Founder & Director of IP Osgoode and Bracing for Impact Conference Chair, were a part. The task force identified Smart City opportunities for improving the city through innovation, communication technology, and mobility management — using initiatives to improve road safety, reduce traffic congestion, and encourage residents to participate in active transportation.

Professor Zachary Spicer: Smart Cities – A Unique Challenge

is an Associate Professor at the School of Public Policy and Administration at 91��ɫ. He examined Municipal governments’ capacity for Smart City Development and AI adoption, focusing on the constraints of resources, scale, and provincial legislation.

Professor Spicer viewed that while Smart City technology can provide various benefits, such as the opportunity to maximize budgets and create efficiencies, they also bring a host of novel challenges. For instance, in the context of applying AI to transportation, Professor Spicer emphasized the importance of considering the potential skills and engagement gaps when procuring Smart City technology within municipalities in Canada. We must ensure that the relevant personnel must have the necessary understanding, skills and resources related to AI technology and data governance.

Dr. Vera Roberts: Marginalized Communities and AI

is the Senior Manager of Research, Consulting and Projects at the Inclusive Design Research Centre (IDRC) of OCAD University. She advocated for people with disabilities, identifying that this marginalized community is often excluded from the AI system development process and therefore inadequately represented. 

Dr. Roberts explained that because AI systems are machines, we tend to view these systems as operating on pure logic and immune to human biases. However, she stresses that we must keep in mind that AI systems learn from human data, which can be flawed. We should shift our focus to examining biases within the actual input data training AI systems and whether they accurately represent marginalized groups. Currently, AI systems are largely trained on data from “normal people,” limiting their applicability to people with disabilities. When data is included on people with disabilities, Dr. Roberts comments that it usually only includes the fact that they are different from the standard population. The IDRC has several ongoing projects, such as , targeting these issues and creating more inclusive AI systems. 

Mr. Keith Hemingway: Bringing AI to Utilities

Keith Hemingway is the Head of Advanced Planning at the In his opinion, the biggest change in the AI space right now is the increased accessibility to data that was previously protected and hidden away. As the utilities industry moves towards e-mobility and the electrification of transit and heating, companies need to turn towards AI for new schemes and frameworks to implement these changes. 

However, the use of AI raises new issues concerning data privacy. For example, to increase efficiency in resolving outages, Mr. Hemingway brings up the possibility of using drones to visually scan pole lines to identify the outage-causing fault. In this scenario, there runs a risk of capturing more footage than necessary – instead of just seeing the faulty insulator, the drone might accidentally capture someone’s backyard, thus infringing that individual’s privacy. Ultimately, it boils down to what exactly constitutes data and how utility companies can balance using AI to improve electrical systems for the public good while respecting data privacy boundaries. 

Professor Guy Seidman: Bracing for the Impact of Autonomous Vehicles

is a Professor of Law at the Harry Radzyner Law School of Reichman University. He was extremely passionate about the impending arrival of Autonomous Vehicles (AVs), their impacts on our daily lives, and their potential legal ramifications. Professor Seidman recognized that mass electric AV adoption can have benefits such as traffic accident reduction, improved air quality, and freed up urban space from a reduced need for parking spaces (assuming that AVs need not be parked). However, Professor Seidman also identified several barriers to mass AV adoption, including technological feasibility and transition difficulties, wherein different demographics have a differing willingness to trust AVs — the more educated tend to be more accepting of AVs.

Professor Seidman does not anticipate complex legal solutions to questions of accident liability when AVs are involved. Rather, he was optimistic that tort and insurance law will naturally evolve to deal with such issues. He viewed that the more significant discussions revolve around public policy around social and economic ramifications of AV adoption. Finally, Professor Seidman also suggested that we should hesitate to eliminate Traffic Law entirely as AVs become more prevalent, as it is arguably the widest form of legal education. Convincingly, Professor Seidman ended the discussion by concluding that these impending issues must be considered now, so that we are bracing for the impact of incoming AI innovation.

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Gregory Hong is an IPilogue Writer and a 1L JD candidate at Osgoode Hall Law School.

This past summer, I had the privilege, as my final act as a graduate student, to attend a major magnetic resonance imaging (MRI) conference in London, UK (ISMRM). At this conference, GE Healthcare used its plenary session to . The other major MRI manufacturers, and , also have AI suites. Computed tomography (CT) had joined the AI party even earlier than MRI, with , , and products. The widespread adoption of AI in medical imaging products is significant because it is one of the first commercial applications of AI in healthcare.

What are MRI and CT?

MRI and CT are the workhorses of most hospitals’ radiology departments. CT and MRI both allow for a 3D image to be taken of internal anatomy, making them invaluable for diagnosing many diseases. Unfortunately, they both have at least one critical downside. CT is an extension of x-ray and thus exposes patients to ionizing radiation, with a CT image often depositing more than 10x the effective radiation dose of an x-ray image. MRI is lauded for, among other benefits, avoiding this radiation; however, MRI is both expensive to run and comparatively very time-consuming.

How does AI come into play?

The primary goal of AI in MRI and CT applications is mitigating the downsides – radiation dose in CT, and scan time in MRI. In both cases, this goal is achieved by “training” an AI through machine learning – or, more specifically, deep learning algorithms – by feeding it an enormous amount of data consisting of previously acquired images. Trained AI allows MRI and CT to acquire less data as the AI is used to fill in the data shortfall – almost analogous to the Hollywood idea of zooming in on a pixelated picture and seeing a clear image. Acquiring less data means less views in CT, leading to less radiation dose and shorter MRI scan times. The resulting AI-enhanced images are used for diagnostic purposes in the same way that conventionally acquired images are.

Why does it matter?

Directly related to healthcare, Canadian , and any improvements to MRI and CT will aid in alleviating that pileup to some extent. It is also significant that radiologists and medical physicists approve of AI in diagnostic imaging. There may not be any group in the medical field more qualified to have at least some grasp of the (disclaimer: I do not fully understand the title of this thesis). It also represents one of the first applications of AI that directly affects medical decisions, which may open the door for other AI applications in healthcare. Lastly, using AI in a commercially available product is interesting on its own – the pathway toward deploying AI in such a high-stakes application may be a useful example for future AI-based products.

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John Lemieux is a Partner in the Corporate and Commercial Group at Dentons Canada LLP. This article was written as a requirement for Prof. Pina D'Agostine & Dr. Aviv Gaon's "Selected Topics in Privacy and Cybersecurity Law" course with Osgoode Professional Development.

Access to justice in Canada is an acute issue requiring urgent action not only from governments but from the legal practitioners in this country charged with an obligation to “improve justice and to continuously create the good.”[1] There are a multitude of avenues for reforming the Canadian justice system in order to improve access to justice, and among these is the integration of artificial intelligence (AI) into our dispute resolution processes in order to decide legal disputes in an efficient, cost effective and expeditious manner.

It is not proposed that AI would be used in every instance to resolve a legal dispute. Rather, AI could be utilized to assist the significant number of Canadians who are unclear as to their legal rights and of the view that seeking redress through the formal justice system will be too costly and time consuming.[2] More specifically, AI can be deployed to help the growing number of self-represented litigants navigate the justice system, and also assist low-income households explore avenues of recourse they may not have pursued without this type of technological assistance.[3]

Darin Thompson proposes the adoption of a basic AI technology, with the simple goal of helping individuals he refers to as “non-expert users” to manage disputes and the justice system in general more effectively.[4] Thompson conceptually describes what he refers to as a Justice Pathway Expert System (JPES), which he imagines as an AI touchpoint for non-expert users needing to engage with the justice system.[5] The design of the JPES is that of an ‘intelligent questionnaire’ interface.[6] The AI system will prompt the non-expert user with a series of questions corresponding to a battery of prepared answers. As the non-expert user works through the questions, the JPES begins doling out information and recommendations that can be acted upon. Thompson’s description of the process is that of (1) an initial problem diagnosis, (2) the delivery of specific information germane to the diagnosed problem, (3) the provision of recommendations for tools or resources that the non-expert user can access and utilize to help consider methodologies to best resolve the problem, and finally (4) a ‘streaming and triage’ functionality that can help guide the non-expert user towards the perceived best resolution process to pursue, whether that be as simple as a mediated settlement discussion between disputing parties or the commencement of a court action.[7]

As noted above, the JPES is not conceptualized as an AI that could be utilized to assist with complex matters. Indeed, it is generally accepted that for the time being AI is unlikely to replace human adjudicators in anything but simple legal matters.[8] The value of the JPES concept is that it could be a meaningful resource for individuals without the necessary expertise or financial resources required to retain and instruct legal counsel to consider and map out a dispute resolution pathway ultimately promoting and enhancing access to justice in Canada.

[1] Trevor C.W. Farrow, “What is Access to Justice?” (2014) 51 Osgoode Hall L.J. 957 at 983.

[2] Ibid., at 965. 

[3] John Zeleznikow, “Can Artificial Intelligence and Online Dispute Resolution Enhance Efficiency and Effectiveness in Courts” (2017) 8 International Journal for Court Administration 30 at 30.

[4] Darin Thompson, “Creating New Pathways to Justice Using Simple Artificial Intelligence and Online Dispute Resolution” (2015) 2 IJODR 4 at 5.

[5] Ibid., at 9.

[6] Ibid., at 16.

[7] Ibid.

[8] Rachel E. Stern et. al., “Automating Fairness? Artificial Intelligence in the Chinese Courts” (2021) 59 Colum. J. Transnat’l. L. 515 at 517. 

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Mariela Gutierrez Olivares is an IP Innovation Clinic Fellow and a 2L JD Candidate at Osgoode Hall Law School. This blog was prepared as a requirement for the Directed Reading: IP Innovation Program course, taught by Prof. Pina D’Agostino.

Artificial Intelligence (AI) technologies prevail all around us. Lawmakers are playing catch up by addressing what non-human authorship means for intellectual property as we know it.

Current AI and machine learning uses range from the  to the  for people with disabilities. More controversial areas of AI include its use in �����Ի���.

Causing just as much controversy is whether AI can be an author. Can AI produce original works? If so, should those works be protected by copyright? And who would own that copyright? 

Response from Copyright-granting bodies

These are questions that the Canadian Intellectual Property Office (CIPO) has been grappling with along with its counterparts in other jurisdictions (as others have previously written on and ). CIPO has reached a conclusion after they  to a piece of art  (along with a human co-author).

By comparison, the US Copyright Office Review Board has  that obtaining copyright protection , citing that copyright law only protects those works that “are founded in the creative powers of the mind.”

In the EU, a 2009  established a test that describes originality as requiring an “author’s own intellectual creation.” Though the EU Court of Justice has not yet had a test case to define authorship further, there is a strong indicator in  of the need for a physical person. 

The Growing Powers of AI

Despite the mixed reactions from the governing bodies that have the power to issue or deny copyright protection to AI-authored works, the fact is that AI tools that can generate original works have arrived. AI tools that  (one has even ),�� works (including news articles), and  already exist. Countless more AI creations may be underway. 

The Legal Conundrum

The legal debate of who or what gets to be an author () will continue. Copyright law has been around since the 18th century, and the evolution of technology has allowed humans to reach new heights of creativity for mass consumption.

Much of the debate centers on the notion that only works born from a human mind (where computers and software were used merely as tools) are worthy of copyright protection. Yet, proponents of AI authorship have drawn an analogy between the novelty of non-human authors and the once novel notion of corporate personhood.  have suggested the introduction of an  to protect “authorless” AI-generated works.

Even if, eventually, AI-generated works are protected by copyright, uncertainty remains as to . Traditionally, the first owner of a copyright is the author, and other entities may be secondary owners (i.e., an author’s employer or publisher). For now, at least in Canada, ownership will be assigned to the person who arranged to create the work and not the AI that created the work itself.

Implications for Businesses Leading the Way for AI-generated Creations

What does the current ambivalent landscape mean for those looking to protect works authored by AI? The debate is far from over, and we will undoubtedly continue to see jurisprudence about AI-generated works. Businesses that have developed AI tools capable of generating original works with a view to monetizing these creations are in legal limbo.

For now, it seems developers of AI tools have two options. The first and arguably safer route is to designate the person overseeing the AI technology as author or co-author of its creation. This could help more rapidly monetize AI-Generated works, as courts determine whether AI-generated work can be copyright-protected and how ownership will be assigned. The second and riskier option is to continue innovating and help spearhead the evolution of the law or else hold closely their potentially copyrightable assets. Should AI creations be deemed eligible for copyright protection in the future, those who have already developed AI technologies may be positioned to exploit intellectual assets early on. 

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Emily Xiang is an IPilogue Writer, the President of the Intellectual Property Society of Osgoode, and a 2L JD candidate at Osgoode Hall Law School.

This article is part of a series covering the 5th Annual IP Data & Research Conference, hosted by the Canadian Intellectual Property Office and the Centre for International Governance Innovation.

On Thursday, March 24th, 2022, the Canadian Intellectual Property Office (CIPO) and the Centre for International Governance Innovation (CIGI) hosted their 5th Annual IP Data & Research Conference. For their third themed session, “IP Metrics”, experts were invited to speak about the ways they have been observing global IP trends, making IP data more accessible, and measuring the impact of IP on economic growth in Canada.

Where do Canadians Patent? Implications for Canada’s Patent Regime

Joel Blit, Professor of Economics at the University of Waterloo and CIGI Senior Fellow, kicked off the session. Blit examined the countries in which Canadian investors filed patent applications and sought to determine the extent to which the Canadian patent regime fosters domestic innovation. He found that Canadians were increasingly filing patents abroad, with more Canadians filing in at least one other country each year. Results also showed that patents filed exclusively in the US related to more advanced fields of computer sciences and technologies, while Canada-exclusive patents focused more on special-purpose machinery and the resources and energy sectors. Canadian patents also tended to belong to individual inventors rather than larger assignees, involved fewer inventors, and were cited less frequently, making them relatively less valuable in the global market for innovation.

Blit puts forward several potential explanations. One is that the Canadian patent system is providing less incentive over time for protecting domestic innovations. Another explanation is that Canadian patents are too strong, meaning it may be preferable to “weaken” them by setting higher examination standards, limiting patentable subject matter, or reducing the scope of issuable patents. That Canadians are increasingly patenting abroad could mean that Canadian inventors are becoming increasingly sophisticated, yet it could also mean that Canadian innovations and ideas are more frequently bought up by multinationals. Either way, the current Canadian patents regime seems to play a relatively minor role in promoting domestic innovation worldwide.

Identifying Artificial Intelligence (AI) Invention: A Novel AI Patent Dataset

Nicholas A. Pairolero, Economist in the Office of the Chief Economist at the United States Patent and Trademark Office (USPTO). delivered the second presentation of the session. Pairolero’s team sought to make data on AI more accessible to the public by developing a novel dataset that identified AI tech components in over 13.2 million USPTO patents and pre-grant publications.

After first determining a definition of AI, Pairolero and his team searched through USPTO’s patents using an automated machine learning (ML) model that differentiated between patent documents that did and did not contain any AI component technology. In the evaluation stage, expert AI examiners evaluated each document for AI component technology. Compared to more traditional, query-based approaches, the ML approach resulted in relatively lower precision (as a much larger number of documents were identified as containing AI), but a much higher recall (higher probability of correctly identifying AI). Moreover, both machines and humans seemed to struggle with classification at the boundaries of the various AI component technologies. However, results indicated that the ML approach achieved state-of-the-art overall performance relative to a variety of existing benchmarks from academic and policy literature, holding much promise for the future of automated processing in expediting the transmission of publicly available data.

Missions, Mandates and Metrics: What are the Right Metrics for Academic Technology Transfer?

The session concluded with a pair of presentations by Mike Szarka, Director of Research Partnerships at the University of Waterloo, and Natalie Raffoul, IP Lawyer and Managing Partner at Brion Raffoul LLP. Szarka began by suggesting that most Technology Transfer Offices (TTOs) focused on some combination of a) maximizing gross revenue and licensing income generally; b) focusing on the few projects that would maximize profits; c) maximizing knowledge mobilization and research impact; d) maximizing local economic growth, and e) maximizing client satisfaction and prioritizing the needs of faculty and students. Szarka’s surveying of TTO directors across the country demonstrated that knowledge mobilization, economic development, and service to academic communities ranked much higher in the minds of the respondents than revenue generation, indicating that commonplace TTO metrics focused on royalties do not reflect the true priorities and missions of most TTOs.

Raffoul identified several alternative metrics focused on “the betterment of Canadian society”. Average reported business expenditures invested into research and development () and have been low in Canada compared to the global stage. The greater concern is whether Canadians are owning their ideas and subsequently having the opportunity to commercialize those ideas downstream (instead of assigning their rights over to foreign firms). Raffoul suggested that TTOs ought to track the number of patents they are licensing/optioning/transferring to Canadian headquartered firms compared to foreign ones, along with the revenue generated from those licenses/options/transfers and any research collaborations with those firms. For company-sponsored academic research, co-ownership of patents ought to be held up to co-authorship of papers and publications, in order to correlate evidence of knowledge creation with the ultimate ownership and control of that knowledge.

Conclusion

Though there is much work to be done for Canadian innovators and owners to remain competitive in the global market, the most recent advancements in research and technology prove that Canada is well-positioned to identify shortcomings and well-equipped to tackle them.

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