A Remedial Benefit-Based Approach to the Innocent User Problem in the Patenting of Higher Life Forms
Bibliographic record
Abstract
One of the key controversies surrounding the patenting of higher life forms concerns the problem of the innocent user. Higher life forms are more likely to escape from their inventor than are more traditional types of invention, and, in consequence, a farmer may, through no fault of her own, find patented crops growing on her land. There is a strong intuition that the farmer who unintentionally grew that patented crop should not be liable for infringement, and it has been suggested that a substantive exception from liability is required to deal with the innocent user problem. In this paper, I argue that there is an alternative, remedial, approach to the problem. On the proper application of existing patent law remedies, the patentee will not have any remedy against the farmer in these circumstances. Damages are zero, as the patentee has suffered no loss; the farmer cannot be made to account for her profits, as she has gained no benefit from the infringement; and the farmer will not be enjoined from an action which she cannot control.
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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.029 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 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".