Patents Used in Patent Office Rejections as Indicators of Value
Bibliographic record
Abstract
ABSTRACT This paper introduces a novel approach to measure a patent's economic value by examining whether the patent's disclosure leads to rejection of another pending US patent application. This approach considers the use of the patent by the US Patent and Trademark Office (USPTO) in office action rejections on the grounds of novelty or obviousness, as well as its citation as an X or Y reference in a European Patent Office (EPO) search report, which provides analogous information. Unlike conventional citation metrics widely employed by economists, the novel metric is arguably more closely tied to private value, as it is centered on the examiner's rejection of another patent application based on the patent in question. As this metric is not directly influenced by potential strategic behavior by patent lawyers or patent applicants, it is more directly tied to private value. This study evaluates how patents used in novelty and obviousness rejections, and comparable EPO information, correspond to common measures of private value—specifically patent renewal, the assertion of a patent in litigation, the number of independent patent claims, and classification in multiple technological subject matters. We examine rejection data over a defined time period for US patents issued from 1999 to 2007 and then link value data to these patents. A similar analysis is also conducted for EPO search reports. Our findings reveal that rejection uses, as well as X and Y EPO search citations, independently exhibit positive correlations with all of these value measurements. Moreover, when information from both the USPTO and EPO concerning a given patent is combined, further insights about that patent's value are obtained. We also find that rejection uses provide unique insights into additional measures of private value such as the patent being listed in the Orange Book or as a Standard Essential Patent. Accordingly, these rejection use metrics provide another independent tool for evaluating a patent's value.
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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 | 0.009 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".