Trends in Ophthalmological Patents, 2005–2020
Bibliographic record
Abstract
Purpose:Technological development drives the optimization of therapeutics in ophthalmology, but quantifiable and systematic review of such innovation is lacking. To fill this gap, we characterize trends in ophthalmology-related patents in the United States from 2005 to 2020. Methods:Publicly available patent data from the US Patent and Trademark Office was analyzed with the R programming language. Ophthalmology-related patents were identified with a keyword search of their titles and claims text. Temporal trends were assessed with the Mann–Kendall trend test (α = 0.05, two-sided). Results:Of 4.5 million collected patents, some 21,000 (0.5%) were ophthalmology related. The number of annually granted ophthalmology patents increased over time (Mann–Kendall test: z = 4.91; P < 0.001), from 619 patents released in 2005 to 2,019 patents in 2020. Patent counts also increased over time for all ophthalmic subspecialties except oculoplastics, with steepest rises in retina (z = 4.91; P < 0.001) and cornea (z = 4.64; P < 0.001). The most cited patents were in biocompatible intraocular implants and implantable controlled-release drug delivery systems, which underscores particular advancement in therapeutic efficacy and safety in devices used in the treatment and management of common yet debilitating eye conditions. Conclusion:This exploratory analysis reveals hotspots for ophthalmology-related innovation in the United States that may predict current and future growth trends in device development and pharmacologic advancement in ophthalmology, paving the way for more diverse and effective treatment options for preserving vision.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".