Bibliographic record
Abstract
In this comprehensive new study, we evaluate all substantive decisionsrendered by any court in every patent case filed in 2008 and 2009 —decisions made between 2009 and 2013. We assess the outcome of litigationby technology and industry. We relate the outcomes of those cases to a hostof variables, including variables related to the parties, the patents, andthe courts in which those cases were litigated.We find dramatic differences in the outcomes of patent litigation by bothtechnology and industry. For example, owners of patents in thepharmaceutical industry fare much better in dispositive litigation rulingsthan do owners of patents in the computer & electronics industry, andchemistry patents have much greater success in litigation than theirsoftware or biotech counterparts. Our results provide an important windowinto both patent litigation and the industry-specific battles over patentreform. And they suggest that the traditional narrative ofindustry-specific patent disputes, which pits the IT industries against thelife sciences, is incomplete.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.014 |
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 teacher head, 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".