Patent Mining on the Use of Antioxidant Phytochemicals in the Technological Development for the Prevention and Treatment of Periodontitis
Bibliographic record
Abstract
Periodontal disease is an inflammatory condition characterized by an aberrant immune response against a dysbiotic dental biofilm, with oxidative stress performing an essential role in its pathogenesis. This paper presents a patent mining, performed in the Orbit Intelligence patent database, related to antioxidant phytochemicals in the technological developments that are working to prevent and treat periodontal disease. To access the documents, the descriptors "PERIODONTAL" and "ANTIOXIDANT" were typed in the title, abstract, and claim search fields. A total of 322 patents demonstrate the growing interest in researching natural antioxidants for scientific and technological purposes. The top ten countries regarding the number of family patents produced were the United States, the European Office, Japan, South Korea, China, India, Mexico, Denmark, Canada, and Great Britain. The most cited compounds were vitamin C, green tea, quercetin, melatonin, lycopene, resveratrol, and curcumin. These compounds have been used for the technological development of gels, membranes, dentifrices, chewing gum, orally disintegrating film, mouthwash, mouth spray, and mouth massage cream and exhibit the ability to neutralize free radicals and reduce oxidative stress, a critical factor in the development and progression of periodontal diseases. The patent documents have shown that using antioxidant compounds in conjunction with traditional periodontal treatments is a promising area of interest in periodontal therapy.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".