Trends in worldwide nanotechnology patent applications: 1991 to 2008
Bibliographic record
Abstract
Nanotechnology patent applications published during 1991-2008 have been examined using the "title-abstract" keyword search on esp@cenet "worldwide" database. The longitudinal evolution of the number of patent applications, their topics, and their respective patent families have been evaluated for 15 national patent offices covering 98% of the total global activity. The patent offices of the United States (USA), People's Republic of China (PRC), Japan, and South Korea have published the largest number of nanotechnology patent applications, and experienced significant but different growth rates after 2000. In most repositories, the largest numbers of nanotechnology patent applications originated from their own countries/regions, indicating a significant "home advantage." The top applicant institutions are from different sectors in different countries (e.g., from industry in the US and Canada patent offices, and from academe or government agencies at the PRC office). As compared to 2000, the year before the establishment of the US National Nanotechnology Initiative (NNI), numerous new invention topics appeared in 2008, in all 15 patent repositories. This is more pronounced in the USA and PRC. Patent families have increased among the 15 patent offices, particularly after 2005. Overlapping patent applications increased from none in 1991 to about 4% in 2000 and to about 27% in 2008. The largest share of equivalent nanotechnology patent applications (1,258) between two repositories was identified between the US and Japan patent offices.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.022 | 0.029 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".