A Budding Theory of Willful Patent Infringement: Orange Books, Colored Pills, and Greener Verdicts
Bibliographic record
Abstract
The rules of engagement in the brand-name versus generic-drug war are rapidly changing. Brand-name manufacturers face increasing competition from Canadian manufacturers of generic drugs, online drug companies, and Wal-Mart® Super Centers deciding to cash in by turning a piece of the generic prescription drug business into a huge marketing campaign with offerings of generic drugs for four dollar prescriptions. Other discount drug providers are likely to follow suit in hopes of boosting customer traffic and sales of their generic drugs. Now, more than ever before, attorneys representing owners of pharmaceutical patents need to be creative with their damages theories to maximize recovery and help their clients recoup the investments in research and development necessary to bring new and innovative drugs to the marketplace. This article suggests a novel theory of willful infringement to assist a patent owner in recovering treble damages and attorneys’ fees.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 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".