Examining the Importance of Scientific Cooperation in the Number of Citations Received by Patents Issued in Persian Gulf Countries
Bibliographic record
Abstract
Purpose: The aim of this study was to identify the relationship between scientific cooperation in and citations to the international patents of the Persian Gulf countries issued in the United States Patent and Trademark Office (USPTO) database. Method: This research was conducted with a descriptive method and a scientometric approach. The statistical population of the research consisted of 8182 patents issued in the USPTO database. In the second stage of the research, the titles of patents were searched in the Web of Science (WoS) citation database to analyze their citations. In order to obtain the number of citations from scientific documents to each patent, the title of each patent was searched in the Web of Science Core Collection in the section entitled “Cited Reference Search”. Then, the number of citations of scientific documents to patents was collected. The Microsoft Excel software was used to analyze the amount of cooperation in patent licenses. Findings: The results showed that only 841 granted patents out of 8182 patents registered in the Persian Gulf countries have been cited by scientific documents in WoS and received a total of 2496 citations. The research findings related to the number of patent licenses in the Persian Gulf countries based on the Cooperative Patent Classification (CPC) subjects show that the highest number of patent licenses is related to the category (G = Physics) with a number of 1717 patents; and the lowest number of patent licenses concerns to the category (D = textiles and paper) with 24 licenses. The highest number of patents is for Saudi Arabia with 5469 patents and the lowest number of patent licenses is for Iraq with 22 patents. The findings also indicated that the highest number of single inventor patents is for Saudi Arabia in the category G (Physics) with 288 patents, and the lowest number of patents based on the subject categories is for Iraq with 11 patents. Also, the highest rate of patents granted with more than one inventor is for Saudi Arabia in the subject category of chemistry and materials engineering (C) with a number of 4114 patents, and again the lowest rate of patents granted is for Iraq with a number of 11 patents. Conclusion: Based on the findings of the research, it can be concluded that only a small number of patents registered by the Persian Gulf countries in the USPTO database have received citations from the scientific documents indexed in the Web of Science (WoS) database. The results indicate that the highest level of patent cooperation within the Persian Gulf countries belongs to Saudi Arabia with 12583 inventors and the lowest level of patent cooperation belongs to Iraq with 30 inventors. In addition, the highest number of patent cooperation with countries outside the Persian Gulf belongs to the United States of America with 2198 inventors, England with 384 inventors, Germany with 333 inventors, Canada with 219 inventors, India with 203 inventors, and France with 171 inventors. According to the data obtained regarding the amount of citations received by single-inventor patents and multiple-inventor patents, using the Kruskal-Wallis test, the significance level of the data showed a less than five percent difference. Therefore, the difference in ranks is significant, and the test results indicate that multi-inventor patents have received more citations than single-inventor patents in subject categories.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".