Bibliographic record
Abstract
Can an appellate court alter substantive law without writing an opinion? We attempt to answer that question by conducting a novel empirical investigation into how the Federal Circuit has implemented the Supreme Court’s 2014 ruling in Alice v. CLS Bank, the most recent in a series of Supreme Court decisions strengthening patent law’s patentable subject matter requirement. Our dataset includes each one of the Federal Circuit’s more than 100 decisions on patentable subject matter in the three years since Alice, including affirmances issued without an opinion under Federal Circuit Rule 36. Including those no-opinion affirmances, the Federal Circuit has found the patent to be invalid in more than ninety percent of its decisions. The court’s precedential opinions, however, tell a different story: nearly a quarter of them favor the patentee by rejecting challenges to patent validity. This difference is due largely to one remarkable fact: Although the court has issued over fifty Rule 36 affirmances finding the asserted patent to be invalid, it has not issued a single Rule 36 affirmance when finding in favor of a patentee. Rather, it has written an opinion in every one of those cases. As a result, the Federal Circuit’s precedential opinions provide an inaccurate picture of how disputes over patentable subject matter are actually resolved. Those opinions suggest that any given patent has a decent chance of surviving an eligibility challenge at the Federal Circuit. But, in reality, very few patents do. Our findings suggest that, by saying nothing, a court can indeed affect substantive law, or at least the perception of it. This has interesting implications both for the on-going debate over the legality of Rule 36 and, more broadly, for understanding the differences between the law on the books and the actual experience of litigants.
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 | 0.031 | 0.166 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".