Bibliographic record
Abstract
The currently funding institutes such as banks are hesitated to undertake the pledging business on intellectual property, it could be due to the lacking confidence on intellectual property or the overestimation of the high uncertainty of intellectual property, the inefficiency to cash out the pledged intellectual property assets etc.. In this article, the previous cases on intellectual property securitization worldwide were studied, in addition, the issues and suggestions for the future legislation are reviewed, and the adoption of the current finance asset securitization is discussed. My personal suggestions on securitization legislation for biomedical patent are stated and specifically explained the protection mechanism. Considering the characters of biomedical patent (such as the huge investments on equipments, long research period, complicated clinical trials and procedures, strict medicinal laws on manufactures and sales certificates approval, marketing and advertisement regulations etc.), parties involved in the securitization mechanism will not risk their qualified patents for the short term cash flow, which means lower opportunities on fraud or conspiracy comparing to the financial assets securitization. The overseas fund raising and guarantee institutes were presented. Sincerely hope this article shall contribute to the future legislation on intellectual property securitization, starting with biomedical patent securitization. Key words: Intellectual property; Securitization; Fund raising; Asset transfer; Biomedical patent
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".