The Left Shark, Thrones, Sculptures and Unprintable Triangle: 3d Printing & its Intersections with IP
Bibliographic record
Abstract
This article explores how existing intellectual property (“IP”) law affects the rights of consumers who embrace 3D printing, by examining points of intersection between IP law and the technology. These intersections have yet to receive critical discussion in the academic literature, judicial decisions, or legislation. It won’t be long before legislatures, judges and policy-makers are called upon to regulate aspects of 3D printing activities and sort out the many issues that 3D printing gives rise to. The resolution of the issues that arise will affect the accessibility of the technology and determine the limits to the rights of manufacturers to control and to enforce their IP rights in the use of the technology. The discussion starts with a brief description of the steps and processes in 3D printing activity. The section that follows explores how the different activities in 3D printing intersect with IP laws, with reference to three recent incidents regarding use of other technologies: the “Left Shark,” a backup dancer that ended up stealing the spotlight at this year’s Super Bowl half-time show; iron throne from HBO’s series Game of Thrones; and the Penrose triangle, an optical illusion that cannot exist in normal three-dimensional Euclidean space. This article concludes with recommendations as to possible approaches on how best to balance the rights of consumers, innovators, and other stakeholders in dealing with conflicts over IP rights that relate to 3D printing. In this respect, it is proposed that realizing the full potential of 3D printing technology requires an express recognition of user’s rights in respect to certain activities in 3D printing. Such recognition will ensure greater access for everyone to culture, knowledge, information, and education in the use of the technology.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.046 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".