Digital Pills with Ingestible Sensors: Patent Landscape Analysis
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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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".