Effective Identification of Technological Opportunities for Radical Inventions Using International Patent Classification: Application of Patent Data Mining
Bibliographic record
Abstract
Identifying technological opportunities early on is critical for the development of radical inventions (RIs). Patents are recognized as one of the most reliable resources for identifying technological opportunities. In line with this, this study aimed to suggest a novel approach for the identification of technological opportunities for RIs, based on the International Patent Classification (IPC), whose design knowledge has not been fully utilized. In this approach, technological opportunities for RIs are identified by measuring the value of technological novelty (VON) of each technology manifested in a patent set, and the value of difficulty (VOD) of each R&D theme contained in the patent set. Specifically, VONs are calculated through a novel map of technological changes over time, based on structured data from the patent set; and VODs are determined using natural language processing, K-means cluster analysis, and complex network analysis of the unstructured data from the patent set. The feasibility and operability of the proposed approach are verified in a case study of unmanned aerial vehicles. The proposed approach can help designers maximize the use of designs and expert knowledge in patent libraries, to formulate technical strategies for RIs.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".