Identifying Patent Risks in Technological Competition: A Patent Analysis of Artificial Intelligence Industry
Bibliographic record
Abstract
Identifying patent risks is a crucial way to assist technological innovation actor especially latecomers to confront with technological competition. The patent documents contained enormous and rich technical information to indicate patent risks and gain competitive advantage for both public and private sector entities in their marketplace. In this paper, we consider patent documents as objective data source and search them from INNOGRAPHY, a professional business database of intellectual property. The patent risks in technological competition were explored from the perspective of overseas patent layout, competition position of top assignees and top assignees holding high-strength patent based on patent analysis. The effectiveness of the approach is verified with the case study on artificial intelligence industry. The results showed that the U.K, Canada, Germany, the U.S. and Japan attached great importance to overseas patent layout. Regarding to the competition position of top assignees, assignees from the U.S., Japan, China and Korea were major competitors in artificial intelligence industry. The U.S. was the leader of artificial intelligence industry, holding 252 high strength patents. Although China had an obvious advantage over the number of patents compared to the U.S., Japan and other countries or regions in the field of artificial intelligence industry, China should be focused on expanding overseas patent layout and enhancing its competition position and high strength patents. This paper offers valuable contribution to the understanding of patent risks in technological competition in the artificial intelligence industry, and can be applied to various other technology industries and served as a starting point for developing more general models.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".