Bibliographic record
Abstract
Abstract This chapter explores the concept of grace periods across various jurisdictions and explains that Australia provides a 12-month grace period for inadvertent disclosures, including specific rules for divisional applications. It explains that Canada originally allowed a two-year period under the 'old' Patent Act but now limits grace to one year for inventor-derived disclosures, with uncertainties regarding experimental use. The chapter describes China’s six-month grace period for disclosures at government-recognised exhibitions, academic meetings, or unauthorised disclosures, and India's 12-month period following authorised exhibitions or academic presentations, also recognising reasonable public trials. It states that Japan offers a six-month grace period for specific disclosures, requiring strict procedural compliance, and that the United States grants a one-year grace period for public disclosures, aiming to encourage prompt patent filings. Finally, the chapter outlines Europe's strict novelty rules, permitting exceptions only for evident abuse or exhibition disclosures, and details national variations in Germany, Italy, the Netherlands, and the United Kingdom.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.098 | 0.051 |
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".