Intellectual property in entomology: Analysis and perspective on recent trends in global patent publications
Bibliographic record
Abstract
Intellectual property (IP) is an important consideration for entomological research and provides a means to capture value from new discoveries. Herein, we describe an analysis of more than 26 000 patent publications from 2007-2017 related to the field of entomology. These patents were divided among 8000 patent assignees; however, only 5% of the assignees had ≥10 patents. Corporations accounted for the largest share of patents (59%), with individuals (20%), academic institutions (17%) and government organizations (4%) making up the remaining segments. From 2007-2017 the number of entomological patents increased by 400%, with the largest number being from China. However, unlike patents from Europe, Japan or the US, which target a range of countries, the Chinese patents almost exclusively focus on China. Among the array of subjects covered are transgenic insects and plants, repellents, recombinant insect cells, with the highest proportion of patents focused on insecticides (39%), followed by insecticide mixtures (27%) and formulations (21%). The top 30 patent assignees included companies/institutions from China (18), Europe (3), Japan (6) and the US (3). Among the top 12 entities, IP from the US assignees was distributed across insecticides, mixtures and insecticidal traits while those from China were more focused on mixtures. However, given expanding IP numbers from China it is expected that in the future there will be a greater impact on new insecticides and related technologies. © 2020 Society of Chemical Industry.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.034 | 0.061 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".