Coordination of Legal Protection of Algorithms and Intellectual Property System
Bibliographic record
Abstract
In the context of the intelligent revolution, the algorithm is increasingly becoming an important tool for assisting decision-making and regulating order. Because of the professionalism and opacity of the algorithm, a series of challenges of legal rules and legal order will occur if there is no market access mechanism and post-mortem supervision. Based on the analysis of the intellectual property protection of the algorithm and the essence of the intelligent society, this paper reveals that the algorithm is the endogenous power of the intelligent society. The intellectual property protection of the algorithm is in line with the value needs of the essence of the intelligent society, which is the necessary system for the rapid development of the intelligent society in the future. The existing algorithm protection methods include copyright, trade secrets, and patent rights. The current coverage is not wide enough, the protection effect is weak, and it is easy to trigger new social problems, which can hinder the protection of social benefits and the promotion of technological progress. The authors believe that the patent law “public change protection” mechanism can not only alleviate the contradiction between “algorithm power” and public interest but also stimulate the development of algorithm technology. An algorithm is a technical solution, and it is also a rule of thinking. The algorithm has the characteristics of technical solutions and thinking rules, which is different from pure thought rules and can produce “changes in the physical state”. Therefore, it should be protected as an object of the patent law. It is necessary to determine the patent-ability standard of the algorithm as soon as possible. The algorithm acts as a new type of object protected by the patent law directly, and at the same time, it sets the algorithm value evaluation mechanism. Finally, through the system construction of algorithm protection, the intellectual property law can be used to promote the innovation of algorithms, so that the algorithm can be developed in a more rational, ethical and legal direction to boost the rapid development of intelligent society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".