Bibliographic record
Abstract
With development of technology and expansion of open innovation, number of patent filings has grown sharply over past few years. In particular, growth in filings and pending applications at five major intellectual property offices (IP5), namely KIPO, SIPO, JPO, USPTO, and EPO, has necessitated further interoffice work-sharing. The number of applications that can be utilized by a second office is currently estimated to be around 250 000 per year. Thus, enhanced work-sharing among IP5 offices could significantly increase examination capacity. The IP5 Heads Meeting at Jeju in October 2008 emphasized quality and timeliness as two prerequisites for work-sharing among IP5 offices. The offices consequently acknowledged enormous benefits of work-sharing and agreed to implement ten foundation projects aimed at addressing those issues of quality and timeliness. The vision expressed at heads meeting was the elimination of unnecessary duplication of work among offices, enhancement of patent examination efficiency and quality, and guarantee of stability of patent rights. In light of that vision, this paper explores benefits and advantages of following work-sharing mechanisms: PCT, PPH, SHARE, JP-FIRST, UPP, Triway, and New Route. It also looks at historic development of IP5 framework of cooperation and implementation of ten foundation projects.In addition, paper examines and compares features of two other interoffice cooperative schemes set up for mutual exploitation of examination results: namely ASEAN Patent Examination Co-operation and Vancouver Group Offices, which consist of IP offices of UK, Canada, and Australia. Another initiative that aims to enhance quality of examinations is Peer-to-Patent project being piloted in US, Australia, Japan, and Korea. This project enables a patent office to utilize expertise of qualified members of community when assessing patent applications. Members of public are invited to identify and retrieve prior art that can be used to assess novelty and inventive step of specified patent applications. The paper focuses on various features of Peer-to-Patent projects being piloted in different jurisdictions and concludes that participation of intellectual property users and communication among examiners are crucial for enhancing quality of patent examinations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.025 |
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".