"Academic and Commercial Patent Families: Same Metrics, Different Story"
Bibliographic record
Abstract
In this paper we explore an academic entrepreneur’s patent portfolio to explore what insights can be gained via patent metrics when analyzing academic patents in search of indicators of patent breadth. In particular, we explore differences in patent breadth metrics by comparing two sub-gropus of patents: academic patents versus those with commercial or corporate interests, and parent patents versus their children within a given family. We find that some metrics, when individually taken at face value, tell a conflicted story, and that they should be interpreted with caution. Collectively, the metrics tell an interesting story in that they support an argument that academic patents are broader than commercial patents. Analysis of children patents indicate that academic parent patents are enhanced in more specific areas by their children patents and that commercial parent patents are followed by children patents that broaden the scope into other areas.
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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 | 0.008 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".