Patent Parasites: Non-Inventors Patenting Existing Open-Source Inventions in the 3-D Printing Technology Space
Bibliographic record
Abstract
Open-source 3-D printing has played a pivotal role in revolutionizing the additive manufacturing (AM) landscape by making distributed manufacturing economic, democratizing access, and fostering far more rapid innovation than antiquated proprietary systems. Unfortunately, some 3-D printing manufacturing companies began deviating from open-source principles and violating licenses for the detriment of the community. To determine if a pattern has emerged of companies patenting clearly open-source innovations, this study presents three case studies from the three primary regions of open-source 3-D printing development (EU, U.S., and China) as well as three aspects of 3-D printing technology (AM materials, an open-source 3-D printer, and core open-source 3-D printing concepts used in most 3-D printers). The results of this review have shown that non-inventing entities, called patent parasites, are patenting open-source inventions already well-established in the open-source community and, in the most egregious cases, commercialized by one (or several) firm(s) at the time of the patent filing. Patent parasites are able to patent open-source innovations by using a different language, vague patent titles, and broad claims that encompass enormous swaths of widely diffused open-source innovation space. This practice poses a severe threat to innovation, and several approaches to irradicate the threat are discussed.
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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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".