Guidance for reconciling patent rights and disclosure of findings at scientific meetings
Bibliographic record
Abstract
Open collaboration and sharing of information among scientists at scientific meetings can foster innovation and discovery. However, such sharing can be at odds with potential patenting and commercialization objectives. This tension may be mitigated if certain procedures are followed in the context of scientific meetings. The article first discusses what makes a scientific finding patentable and then sets out four specific patent issues for scientists to consider before attending a scientific meeting and sharing their research. Finally, it provides recommendations on how scientists can best protect their intellectual property rights while sharing information at scientific meetings.
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 | 0.350 | 0.493 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.034 | 0.036 |
| Open science | 0.014 | 0.015 |
| Research integrity | 0.097 | 0.033 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".