Temporal Trends in the United States Patent Landscape: Innovation in Cardiology Across Industry and Academia
Bibliographic record
Abstract
Background: Novel approaches to diagnostics and therapeutics in medical care reflect the scientific community's evolving understanding of disease states and their clinical implications. Marketable and valuable innovations are generally patented for protection of intellectual property. Here, we explore the landscape of cardiology-related patents in the United States. Methods: All United States patents granted between 2005 and 2020 were included in this study. Keywords filtering was used to identify patents related to cardiovascular medicine. Statistical inference was conducted with the Mann-Kendall trend and analysis of variance tests. The results in this report are entirely reproducible with Python and R scripts available in a publicly accessible repository. Results: Of the 4,453,733 patents issued by the USPTO between 2005 and 2020, 31,048 (0.7%) were identified as cardiology-related patents. We identified the top 10 institutions within the for-profit and not-for-profit categories that were assigned the most cardiology-related patents in this time period. Distributions of number of patents per inventor were heavily right-skewed, with a small number of inventors responsible for a large number of patents each. Patents in the cardiac imaging subgroup took the longest to gain approval after submission (median delay: 3.6 years). Conclusions: By studying the patent universe, we are able to identify underexplored areas within cardiovascular medicine. Obstacles such as long delays between patent application and approval can hamper innovation within a field. As a next step, we aim to use these results to predict the next area within cardiovascular medicine to undergo explosive research and innovation.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".