Cinq décisions d’intérêt en matière de brevets non pharmaceutiques en 2020
Bibliographic record
Abstract
Le present article se veut une recension de quelques decisions, en matiere de brevets non pharmaceutiques, rendues par les tribunaux canadiens en 2020. Il est a souhaiter que la lecture de ce texte suscite l’interet des passionnes du droit de la propriete intellectuelle. Cinq decisions ont ainsi ete selectionnees et seront survolees plus en detail dans cet article. La premiere (Choueifaty, 2020 CF 83) porte sur la modification des pratiques d’examen de l’OPIC, notamment en ce qui concerne les demandes de brevet pour, entre autres, les methodes de mises en oeuvre par ordinateur. La seconde (Richard Packaging, 2020 CF 1161) concerne le traitement d’information confidentielle et l’importance de rediger des ententes de confidentialite avec clarte. La troisieme (Salt Canada, 2020 CAF 127) s’interesse a l’interpretation de contrats entre particuliers par la Cour federale. La quatrieme (Georgetown Rail Equipment, 2020 CF 64) traite de la presomption de validite d’un brevet. Enfi n, la cinquieme (Flatwork Technologies, 2020 CF 997) traite de la validite d’un brevet dans le cadre d’une demande de jugement sommaire.
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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".