Patent landscape report on dental implants: A technical analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Significant research and development (R&D) has been conducted to make the best dental implants while developing various patent applications and registrations. In this study, we evaluated the current status of patents on dental implants and identified the future direction of R&D progress. MATERIALS AND METHODS: A total of 29 711 patents related to dental implants were reviewed. These were published between 1909 and 2020 and retrieved from the Derwent Innovation patent database. The patents were grouped into three categories depending on the implant components: fixture, abutment, and artificial teeth. RESULTS: The category with most patents was "abutment," and the most cited patent was "screw-type dental implant anchor." Global patenting trends over the past 20 years showed that both applicants and applications increased in the early 2010s; however, these have since been on the decline. Currently, the United States holds the largest number of patents, and Nobel Biocare Holding AG is the top assignee. Technic maturation prediction analysis showed that the current dental implant technology is in the "decline stage." CONCLUSION: Trend analysis of the dental implant patent indicates the main contributors of development are profit-oriented companies. Recent reduction in the number of new patent applications suggests the technology is in the mature declining stage. The emergence of new materials or technologies that may close the gap in clinical unmet needs would reverse the trend.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.037 | 0.032 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".