Poisoning the Next Apple? The America Invents Act and Individual Inventors
Bibliographic record
Abstract
The Leahy-Smith America Invents Act, the most significant patent law reform effort in two generations, has a dark side: It seems likely to decrease the patenting behavior of small inventors, a category which occupies special significance in American innovation history. In this paper we empirically predict the effects of the major change in the law: a shift in the patent priority rules from the United States’ traditional “first-to-invent” system to the predominant “first-to-file” system. While there has been some theoretical work on this topic, we use the Canadian experience with a similar change as a natural experiment to shed the first empirical light on the question. Our analysis uses a difference-in-difference framework to estimate the impact of the Canadian law change on small inventors. Using data on all patents granted by the Canadian Intellectual Property Office and the US Patent and Trademark Office, we find a significant drop in the fraction of patents granted to small inventors in Canada coincident with the implementation of first-to-file. We also find no measurable changes in patent quality and perform several additional analyses to rule out alternative explanations. While the net welfare impact that can be expected from a shift to first-to-file is unclear, our results do reveal that, contrary to the conventional wisdom, the March 2013 implementation of a first-to-file rule in the U.S. is likely to result in reduced patenting behavior by individual inventors.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".