Key Takeaways from “The Intersection of University Inventions and Recent Cases at the Patent Trial And Appeal Board (PTAB)”
Bibliographic record
Abstract
This article provides a brief summary of the discussion, presentation, and knowledge shared during the Eight Annual Meeting of the National Academy of Inventors held in Houston, Texas, in April 2019. The panel, “The Intersection of University Inventions and Recent Cases at the Patent Trial and Appeal Board (PTAB),” focused on university inventions and the PTAB, with perspectives from a university technology transfer office, a PTAB administrative patent judge, and attorneys practicing before the United States Patent and Trademark Office (USPTO) and in Federal District Court. The panel was led by Andrew Maas of Louisiana State University and included Kalyan Deshpande of the USPTO; Kyle Friesen of Shook, Hardy & Bacon; and Jay Yates of Patterson + Sheridan. The panel busted myths and provided helpful information and perspective on the PTAB.
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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 | 0.051 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.034 |
| Scholarly communication | 0.031 | 0.044 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.015 | 0.040 |
| Insufficient payload (model declined to judge) | 0.010 | 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".