Bibliographic record
Abstract
Patents are not published as quickly as scientific articles because at least 18 months of examination of the invention must elapse between its application and the public record of its grant in the form of a patent document. For this reason, the largest number of patents published so far cover the previous coronaviruses that affect humans, such as Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome, of which there are patents for diagnostic techniques, treatments and even vaccines. There are fewer patents related to the current pandemic caused by COVID-19 due to the short time that has elapsed and the high virology of the disease. The objective of this research was to analyze the behavior of the requested and granted inventions on COVID-19 that have been registered in the United States International Patent Office. The study by its nature used a mixed approach to research where qualitative and quantitative cutting methods were articulated in a systemic way that allowed the complementation of the different metric analyzes. The results showed that the greatest technological power is clustered in the headlines of the United States, followed by Iran, Israel, Bulgaria, Canada and the United Kingdom, and that the number of investigations on COVID-19 are focused on techniques for its identification and diagnosis, in: computer systems;peptides;radio-therapy;data recognition;computational models;mutation and genetic engineering;colorimetry;digital electrical data processing;among other topics that make up the innovative technological vanguard that exist today in the world on COVID-19. © 2021, Centro Nacional de Informacion de Ciencias Medicas. All rights reserved.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".