Clades, classifications, and claims: evolution of organisms and their nomenclature in life science patents
Bibliographic record
Abstract
Organisms are complex biological entities that are less easily defined by specific structures or sequences than molecules, nucleic acids, and antibodies. Nor are they necessarily fixed in time or reproducible by repeatable methods, given their capacity for replication and mutation. Like organisms themselves, names and classifications also change over time as scientists better understand extant biodiversity. Taxonomy is the field of evolutionary biology concerned with classifying, naming, and identifying organisms, while phylogenetics concerns organisms' evolutionary history and relatedness. Here, I review the challenges evolution poses for patentees, using the examples of evolving influenza viruses and bacterial classifications, and Federal Circuit decisions relevant each issue. I conclude that careful consideration of organisms' evolutionary histories and the systematics underlying their classification in specification drafting allows patentees to: (i) mitigate the impact of scientific disagreement (such as the 'species problem' in microbiology) in claim construction; (ii) limit the effects of changing classifications on infringement analysis; (iii) describe generic categories of related organisms to encompass later-arising ones; and (iv) bolster compliance with the written description and enablement requirements of 35 U.S.C. § 112(a). Accordingly, patentees might address the challenges that evolutionary uncertainty poses for organism-centered patents by embracing these areas of evolutionary biology.
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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 | 0.032 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".