Bibliographic record
Abstract
Litigation plague does become a major worry for investors, assignees, inventors and related personnels, even holding a quality patent may not secure enough to be free from patent litigation. As long as the patented technology involoved in considerable profits, competitors will try every possible measure to take over the market, sales order or technology, sometimes aiming to merge or probing core technology, moreover for marketing awareness or brand promotion. Accusing patent infringement through complicated technical data or wordings, patent invalid through anticipation by 35 U.S.C. § 102 or obviouness by 35 U.S.C. § 103, or based on details such as priority dates, publicizing dates, references, filing dates,…etc. Inequidable conducts are new fashions with various tactics like attcking missing lables on embosiments, unsupported spcification , obvious to try, experiments details, chemical structure’s similarity upon biological efficacy, similarity between dehydrated from and un-dehydrated from, formulation or excipient differences, even a bit late filing information disclosure statement (IDS) for new references, crime fraud exception to the attorney-client privilege, are common tactis in intellectual property disputs. The counteractions will be described in details with cases. Keyword: infringement; doctrine of equivalence; patent invalid; patent anticipation; patent obviousness
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".