Digital Pills with Ingestible Sensors: Patent Landscape Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The modern healthcare system is directly related to the development of digital health tools and solutions. Pills with digital sensors represent a highly innovative class of new pharmaceuticals. The aim of this work was to analyze the patent landscape and to systematize the main trends in patent protection of digital pills with ingestible sensors worldwide; accordingly, to identify the patenting leaders as well as the main prevailing areas of therapy for patent protection, and the future perspectives in the field. In July 2022, a search was conducted using Internet databases, such as the EPO, USPTO, FDA and the Lens database. The patent landscape analysis shows an increase in the number of patents related to digital pills with ingestible sensors for mobile clinical monitoring, smart drug delivery, and endoscopy diagnostics. The leaders in the number of patents issued are the United States, the European Patent Office, Canada, Australia, and China. The following main areas of patenting digital pills with ingestible sensors were identified: treatment in the field of mental health; HIV/AIDS; pain control; cardiovascular diseases; diabetes; gastroenterology (including hepatitis C); oncology; tuberculosis; and transplantology. The development of scientific and practical approaches towards the implementation of effective and safe digital pills will improve treatment outcomes, increase compliance, reduce hospital stays, provide mobile clinical monitoring, have a positive impact on treatment costs and will contribute to increased patient safety.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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 it