An "Obvious" Proposal - Using An Industry Sensitive Doctrine of Obviousness to Govern the Scope of Gene Patents After Association for Molecular Pathology v. USPTO
Bibliographic record
Abstract
Currently there are approximately 20,000 valid gene patents in the United States. The debate regarding biotechnology and patent law has reached a pinnacle over the patentability of genes. Biotech is fighting a patentability war on two fronts. The Court of Appeals for the Federal Circuit cannot agree regarding the touchstone of patentability for genes; two branches of the Executive are at odds over whether gene sequences qualify under 35 U.S.C. §101. Recent U.S. Supreme Court and Federal Circuit jurisprudence also undermine the patentability of genes as obvious. This thesis argues that the patentable subject matter debate fails to adequately address the goals of patent policy in fostering innovation. Looking to Canadian and U.K. jurisprudence, it is possible to hone an approach to obviousness that addresses the ethical and research concerns in the patentable subject matter debate while fostering investment and patent protection for non-obvious biotech discoveries.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".