Advances in data-driven digital innovations during Industrial Revolution 4.0 are the foundation for this patent discussion. In a shifting technological paradigm, I argue for an approach that considers the broader theoretical perspectives on innovation and the place of the term invention within that perspective. This research could inform the assessment of a patent for Machine Learning algorithms in Artificial Intelligence. For instance, inventions may have elements termed abstract (yet innovative) and not previously within the purview of patent law. Emergent algorithms do not necessarily align with existing patent guidance rather algorithms are nuanced, increasing support for a refined approach.
In this thesis, I discuss the term algorithm and how a novel combination of elements or a cooperating set of essential and non-essential elements, can result in a patentable result. For instance, a patentable end can include an algorithm as part of an application, whether it is integrated with a functional physical component such as a computer, whether it includes sophisticated calculations with a tangible end, or whether parameters adjust for speed or utility. I plan to reconsider the term algorithm in my arguments by exploring some challenges to the section 27(8) of the Patent Act, “What may not be patented,” including, that “no patent shall be granted for any mere scientific principle or abstract theorem.” The role of the algorithm in the proposed invention can be determinative of patent eligibility.
There are three lines of evidence used in this thesis. First, the thesis uses theoretical perspectives in innovation, some close to a century old. These are surprisingly relevant in the digital era. I illustrate the importance of considering these perspectives in innovation when identifying key contributing factors in a patent framework. For instance, I use innovation perspectives, including cluster theory, to inform the development of an approach to the patentable subject matter and the obviousness standard in AI software inventions. This approach highlights applications of emerging algorithmic technologies and considers the evolving nature of math beyond the basic algorithm and as a part of a physical machine or manufacture that is important in this emerging technological context.
As part of the second line of evidence, I review how the existing Canadian Federal & Supreme Court cases inform patent assessments for algorithms found in emerging technologies such as Artificial Intelligence. I explore the historical understanding of patent eligibility in software, professional skills, and business methods and apply cases that use relevant inventions from a different discipline. As such, I reflect upon the differing judicial perspectives that could influence achieving patent-eligible subject matter in the software space and, by extension how these decisions would hold in current times. Further to patent eligibility, I review the patentability requirements for novelty, utility, and non-obviousness.
As part of the third line of evidence, I reflect on why I collected the interview data and justify why it contributes to a better understanding of the thesis issues and overall narrative. Next, I provide detail and explain why certain questions formed a part of the interview and how the responses helped to synthesize the respective chapters of the thesis. The questions focus on patent drafting, impressions of the key cases, innovation, and the in-depth expertise of the experts on these topics. Finally, I provide recommendations for how the patent office and the courts could explore areas for further inquiry and action.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/44397 |
| Date | 16 December 2022 |
| Creators | Power, Cheryl Denise |
| Contributors | Scassa, Teresa |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
Page generated in 0.0018 seconds