Rapid Patent Quality Evaluation Method Based on Big Data Analysis: Chinese Invention Patentsas Sample
Bibliographic record
Abstract
High-quality patents have high technical value and market competitive advantage. Faced with the huge number of patent data, how to rapidly and efficiently identify the quality of patents from the patent announcement is a crucial research issue at present. Therefore, it is reasonable to predict that, big data based techniques will be the best method to exploit this kind of data. The patents authorized by CNIPA (China National Intellectual Property Administration) are taken as the research object. This study chooses several types of patent evaluation indicators and uses EWM (The Entropy Weight Method) to calculate the weight of each indicator. The study determines a correction coefficient to enhance the usability and provides the final quality score of each patent. The evaluating formula is provided. In this study, easily accessible patent indicators are used, which makes it easier to evaluate the quality of patents. By this method, rapidly evaluating the patent quality only by its basic announcement data is feasible, which solves the limitation that laborious access to advanced indicators.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".