Demythologizing PHOSITA: Applying the Non-Obviousness Requirement under Canadian Patent Law to Keep Knowledge in the Public Domain & Foster Innovation
Bibliographic record
Abstract
The Supreme Court of Canada recently revised the doctrine of non-obviousness in a pharmaceutical “selection patent” case, Apotex Inc. v. Sanofi-Synthelabo Canada Inc. Although cognizant of changes to the same doctrine in the United States and the United Kingdom, a critical flaw in how the doctrine is being applied in Canada escaped the Court’s attention. Using content analysis methodology, this article shows that Canadian courts frequently fail to characterize the “person having ordinary skill in the art” (PHOSITA) for the purpose of the obviousness inquiry. The article argues that this surprisingly common analytical mistake betrays a deep misunderstanding of innovation, one which assumes that actors consult patents to learn about scientific developments, devalues the importance of the public domain, and ignores the industry-specific nature of innovation. The article also describes the historical evolution of the non-obviousness test, identifies factors that undermine PHOSITA’s characterization, and develops a multi-layered prescription to remedy the problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.036 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".