A Design-Around for the United States Design Patent System: What Can the United States Learn from the United Kingdom and Canada in the Aftermath of Samsung v. Apple?
Bibliographic record
Abstract
The recent Samsung v. Apple design patent litigation has generated substantial discussion of the United States’ design patent system’s weaknesses, particularly with respect to technologically complex products. In late 2016, the United States Supreme Court acknowledged shortcomings of the United States’ design patent system as applied to multicomponent products, but did not provide a concrete test to address these issues. As the Supreme Court’s decision leaves the lower courts without clear guidance to fashion a test, they would benefit from examining industrial design systems abroad for direction. Industrial design systems in other countries, including the United Kingdom and Canada, have not faced negative publicity comparable to that of the United States. Lower courts might thus benefit from a comparative analysis of these nations’ systems. Although the three design protection systems share many similarities, some significant differences exist in how courts determine industrial design infringement and damages awards. To mend its own design patent system, the United States should grant judges discretion to determine proper damages awards following a fact-specific inquiry considering the value that the appropriated design contributes to the infringing product.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.020 |
| Scholarly communication | 0.029 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.021 | 0.018 |
| 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".