Injunctive Relief in the EU – Intellectual Property and Competition Law at the Remedies Stage
Bibliographic record
Abstract
In dealing with applications for injunctive relief by the holders of FRAND-encumbered SEPs in the course of protracted licensing negotiations, any legal system faces the challenge of reaching the proper balance between predictability for stakeholders and differentiation between possible scenarios (tough negotiations, holdup, holdout or exclusion). In the EU, that challenge fell to be addressed first under the various national laws concerning remedies for intellectual property violations, as partially harmonized by Directive 2004/48. The outcome was not optimal. After German courts introduced competition law in the equation in Orange Book, the European Commission felt compelled to intervene with a different approach in Motorola and Samsung, leading to a reference to the CJEU in Huawei v ZTE. That ruling sets out an elaborate choreography that SEP holder and implementer must respect, in order to avoid breaching Article 102 TFEU or avert injunctive relief, respectively. Huawei represents a satisfactory compromise in practice, but its theoretical foundation in competition law is not solid. Subsequent case-law has unmoored Huawei from competition law and is turning it into a stand-alone lex specialis for injunctions in FRAND cases. In the longer run, legislative intervention might be preferable to de facto harmonization via competition law.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".