Patent Prospecting: Maping of Patent Applications Filings for the Fertilizer Product as Green Technology Over the Last Decade
Bibliographic record
Abstract
This study aims to carry out patent prospecting for the fertilizer product. The justification for such a proposal lies in the relevance and application of the information that patent documents can provide about an invention. Furthermore, it considers the current scenario of Brazil’s dependence on the import of fertilizers, bearing in mind its position in the global agribusiness. To this end, the study develops a refined analysis of patent applications in the DERWENT database with code C05F of the IPC Green Inventory of the World Intellectual Property Organization (WIPO), following a time frame between 1986 and the 2010 and 2023 period. Among the main results, the study shows that the technological information in patent documents can be helpful to Brazil’s development of eco-friendly technologies. To corroborate this statement, there are 6,337 inventions with IPC code C05F that have not been filed in the country that can be analysed, studied, and used since they have not been patented in its territory. By pointing out the exponential evolution of patent filing requests with code C05F from WIPO’s IPC Green Inventory in the world, the study shows that China is the country with the highest number of patent filing requests with that code, via PCT (n=6,476 requests). On the other hand, the analysis shows that, excluding China, Brazil ranks 5th in the number of patent filings (n=45). However, compared to Russia (n=153) and South Korea (n=143), the number of requests is significantly lower. The study also shows that Brazil has filed significantly fewer patent applications for code C05F in WIPO’s IPC Green Inventory per year when compared to other countries, with less than five filings in the period, highlighting the discrepancy between the country´s need to consume the fertilizer product and its presence in global agribusiness. In conclusion, this patent prospection highlights the importance of research into environmentally sustainable technologies, especially when it comes to Brazilian agribusiness, as well as demonstrating that there is a lot of technology that can be used in Brazil at zero cost, taking into account the information contained in the patent documents worldwide.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.038 | 0.028 |
| Science and technology studies | 0.001 | 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) | 0.010 | 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".