Bibliographic record
Abstract
It has been acknowledged that the use of strategic choices in patenting becomes more and more useful when it comes to gaining a competitive advantage. However to be able to make strategic choices, it is helpful to know which risk is accompanied with a certain choice. In this research an attempt has been made to develop an instrument capable of defining the patent intensity within industries, which gives an indication of the risk of exploiting a patented technology. In this attempt more than ninety papers were read, four concordance tables tested and days were spent on assessing the available databases. As a result the amount of patents ‘ceased’, ‘revoked’, ‘appeals’, ‘license of right’ and ‘granted’ were determined as to possible correlate with the risk of litigation and thus give a view on the patent intensity within an industry. When following the natural order of occurrence within the process of patenting a flowchart can be formed as shown in appendix 5.1. Assessing the available data found for 119 four digit IPC subclasses through regression analyses show the correlations within this flowchart, making it possible to create a general regression equation which can be used for a comparison within industries. Results for this comparison could be used to assess the patent intensity, and with it the risk within an industry and use this to make strategic choices. As a result of statistical analyses some of the initially found indicators were dropped. The values that were found within an IPC subclass covering the granted patents in combination with the amount of patent appeals and patents revoked, were used as indicators and have been analyzed through statistical assessment to create an equation capable of indicating the amount of patents being revoked when a certain amount of appeals and grants have been measured. This can then be used to measure the difference in predicted versus actual revoked patents to indicate a higher or lower risk than the mean risk. According to this difference in risk, strategic choices can then be made on changes in attitude or approach.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".