Cinq décisions d’intérêt en matière de brevets non pharmaceutiques en 2019
Bibliographic record
Abstract
RESUME Le present article comporte un resume de cinq decisions d’interet rendues par les cours federales en 2019 en matiere de brevets non pharmaceutiques. Bien qu’aucun changement majeur n’ait ete introduit par les decisions rendues en brevets non pharmaceutiques en 2019, celles qui seront abordees illustrent neanmoins, dans certains cas, l’application de principes connus en jurisprudence canadienne, la resolution de questions relativement nouvelles ou encore l’evolution de regles deja existantes. ABSTRACT This article presents a summary of five notable decisions relating to non-pharmaceutical patents that were rendered by the federal courts in 2019. Although these judgments did not introduce any major changes to Canadian patent law, they nevertheless illustrate the application of longstanding jurisprudential principles, the resolution of relatively new questions and the evolution of existing rules.
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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".