Subjective performance of patent examiners, implicit contracts, and self‐funded patent offices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Self‐funded patent offices should be concerned with patent quality (patents should be granted to only deserving innovations) and quantity (as revenues come from fees paid by applicants). In this context, we investigate what is the impact of the self‐funded constraint on different bonus contracts and how these contracts affect the examiners' incentive to prosecute patent applications. We consider contracts in which a patent office offers bonuses on quantity quotas (explicit contract) and on quality outcome (either an implicit contract or an explicit contract based on a quality proxy). We find that a self‐funded constrained agency should make different organization choices of incentives. For a low quality proxy precision, an agency facing a tight budget operates well with implicit contracts. However, by only relaxing moderately the budget constraint, the agency might be worse off simply because this will preclude implicit contracts. Only very large patenting fees might allow the agency to compensate for the loss of implicit contracts.
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.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it