Unjustified Threats of Patent Infringement Proceedings
Bibliographic record
Abstract
Abstract This chapter outlines various jurisdictions that handle unjustified threats of patent infringement and the legal remedies available to aggrieved parties. It notes that in Australia and India, a party threatened with baseless infringement claims may seek a declaration, injunction, and damages. In China, although the law does not explicitly refer to unjustified threats, affected parties can request legal action or pursue a declaratory judgment of non-infringement. The chapter explains that in Canada there is no explicit provision addressing unjustified threats under the Competition Act, but Section 32 allows for remedies in cases where intellectual property rights are abused to unduly limit trade or raise prices. Japan’s Guidelines address unfair trade practices, such as using the threat of injunctions or refusing licences to hinder fair competition, reinforcing how such practices may violate the Antimonopoly Act.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".