Monetary and Implicit Incentives of Patent Examiners
Bibliographic record
Abstract
Often accused of granting questionable patents, examiners might lack proper incentives to carefully scrutinize patent applications. We analyze their examination and granting behavior in the presence of different incentive schemes that reward examiners based on rejected and/or accepted patents. Our findings suggest that, for a given probability of random audit by the PTO, a dual regime (based on both accepted and rejected patents) does not provide more incentive than a salary based on rejected patents. An optimal probability of random audit chosen by the PTO is often too high compared to the first best, while the examiner chooses a suboptimal examination effort's level. Lastly, we study the effect of career concerns on the granting behavior of examiners. We find that monetary and implicit incentives induce patent examiners to intensify their search effort. Furthermore, a marginal increase of the random audit might reduce examiners’ effort in the presence of career concerns.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".