Online Dispute Resolution in the Field of Intellectual Property: Russian and Foreign Experience
Bibliographic record
Abstract
In the modern world, taking into account scientific and technological progress and the need for innovative development, many states are improving their legislation and approaches to regulating relations in the field of intellectual property by modernizing the justice system. With the use of Internet technologies, it becomes possible to carry out various settlement procedures, both judicial and extrajudicial. Due to the intangible nature of intellectual property objects, online dispute resolution in this area increases the level of protection of rights, expands access to justice, and the effectiveness of resolving disputes. In addition, the use of the blockchain distributed registry system allows you to create a platform for storing information related to the circulation of intellectual property. The experience of Russia, China, Thailand and Canada in the field of online resolution of disputes arising from infringement of intellectual property rights is studied. It is noted that a common understanding of the protection of copyright, as well as objects of industrial property, based on the provisions of fundamental international treaties, as well as modern technologies, including those built on the basis of artificial intelligence, with their correct structured application, will allow resolving such disputes promptly in pre-trial order, thereby unloading the judicial system. At the same time, their clear correlation with the provisions of international agreements is necessary.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".