Do Universities Lead the Patenting Race for AI-based Inventions Tackling Grand Challenges?
Bibliographic record
Abstract
University patenting plays a crucial role in technology development. Trajtenberg, Henderson and Jaffe (1997)'s seminal work on a US-based dataset showed that university patents imply significantly more spillovers than industry patents due to a greater reliance on science and, as shown by other literature, a significantly greater knowledge disclosure. Surprisingly, only few studies dig into the differences between university and corporate patenting. In this paper, we therefore focus on the role of University patenting in the case of emerging technologies using the example of green-AI technologies – inventions that include artificial intelligence to solve some of the Grand Challenges of the 21st century. It is an interesting case as universities are encouraged to contribute more to such technologies and companies have invested heavily in fundamental AI research. Based on an analysis of 11,502 patent families, we show that overall, Trajtenberg et al. (1997)'s findings hold in the case of this emerging field. However, we demonstrate that for these initial results to hold, the technology field needs to reach sufficient maturity and university contributions are much less in the actual state of technology emergence (i.e., within the first 15 years). We also show that the role of universities is contingent on geographical factors: it only holds for Chinese university patenting activities. Our results have substantial implications for theory and practices, notably regarding the conditions for policymakers to encourage universities to patent in an emerging phase of technology development as well as for the specific case of green-AI technologies that contribute to tackling Grand Challenges.
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.
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 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".