Antibody Examination Practice at the Canadian Patent Office: Immune to Change?
Bibliographic record
Abstract
Patent applications in the antibody arts garner a level of scrutiny during prosecution unlike those in any other field. Objections employing highly standardized language are now routinely met under the aegis of lack of support, enablement, or utility defects. Prospective patentees in this art area are currently being shortchanged; in return for their public disclosures, applicants often achieve claims with the potential for very narrow interpretation. Paradoxically, would-be infringers are provided with clear instructions on how to practise variants of the invention and potentially circumvent a narrow construction of the claims. Three types of objections are discussed herein: those denying claims to (1) antibodies that deviate from an intact set of CDR sequences, (2) antibody subtypes that have not been exemplified, and (3) antibodies defined by competitive binding. This article reviews the objections in view of relevant jurisprudence and against the historical backdrop of technical advances.
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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 | 0.033 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.026 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 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".