Patent Application Trends of Induced Pluripotent Stem Cell Technologies in the United States, Japanese, and European Applications
Bibliographic record
Abstract
Patent application trends were investigated for induced pluripotent stem cell (iPSC) technologies, particularly disease-specific cell technologies related to iPSCs, in the U.S., Japanese, and European applications during 2017. The number of patent applications for iPSC technologies was 1516 in the United States, 895 in Japan, and 420 in Europe, with 5% of applications for disease-specific cell technologies. In contrast, the percentages of patent applications for iPSC preparation and differentiation technologies were 17% and 23%, respectively. Patent applications for disease-specific cell technologies were classified into four technical fields and 14 disorder groups. In the technical fields, patent applications for genetically engineered cell technologies were prominent, accounting for 63%, 50%, and 65% of the U.S., Japanese, and European applications for 11, 8, and 7 disorder groups, respectively. In the disorder groups, the percentages of patent applications for neurological disorders were 40%, 32%, and 40% of the U.S., Japanese, and European applications, respectively, which were filed in four technical fields in the U.S. and Japanese applications. The U.S. patent applications for disease-specific cell technologies were filed by applicants in the United States, Japan, France, Belgium, Italy, Korea, and Canada; however, patent applications filed by those in Belgium, Italy, and Canada were not found in the Japanese and European applications. The percentages of patent applications filed by the U.S. applicants were 72%, 55%, and 65% of the U.S., Japanese, and European applications, respectively. Most patent applications filed by the U.S. applicants were in the field of genetically engineered cells for 11 disorder groups, which mostly included neurological and blood disorders. Japanese applicants mainly filed patent applications for drug screening technologies; subjects included five disorder groups, particularly neurological and bone/articular disorders. French applicants filed patent applications for neurological disorders in the field of genetically engineered cells and drug screening technologies. Korean applicants filed patent applications for patient-derived cell technologies for neurological, metabolic, and chromosomal/genetic disorders. In conclusion, more than half of patent applications were for genetically engineered cells for 11 disorders, most of which were filed by U.S. applicants.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".