Third-party submissions for patent applications pending before the USPTO
Bibliographic record
Abstract
James Wagner is a barrister, solicitor and Canadian trade mark agent, and runs ‘The IP Shop’ in Vancouver, Canada. Betty Wu is a recent graduate of Nottingham's LL.B. Senior Status (with Honours) program currently completing her Canadian legal accreditation (NCA) examinations. Following implementation of changes to 37 CFR 1.290 under the American Invents Act which went into effect on 16 September 2012, any party can now submit prior art on recently published US patent applications. Notably, the submission process is online, pseudo-anonymous, and free. This development provides a significant incentive for businesses selling goods or services into the US market to monitor their competitor's patent filings. Guidance is given for the identification of applications for which a third party submission may be warranted, and an approach which may be used for preparing and filing the submission.
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 | 0.034 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.012 | 0.006 |
| Insufficient payload (model declined to judge) | 0.172 | 0.166 |
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".