Bibliographic record
Abstract
Diagnostic testing helps caregivers and patients understand a patient's condition, predict future outcomes, select appropriate treatments, and determine whether treatment is working. Improvements in diagnostic testing are essential to bringing about the long-heralded promise of personalized medicine. Yet it seems increasingly clear that most important advances in this type of medical technology lie outside the boundaries of patent-eligible subject matter. The clarity of this conclusion has been obscured by ambiguity in the recent decisions of the Supreme Court concerning patent eligibility. Since its 2010 decision in Bilski v. Kappos, the Court has followed a discipline of limiting judicial exclusions from the statutory categories of patentable subject matter to a finite list repeatedly articulated in the Court's own prior decisions for "laws of nature, physical phenomena, and abstract ideas," while declining to embrace other judicial exclusions that were never expressed in Supreme Court opinions. The result has been a series of decisions that, while upending a quarter century of lower court decisions and administrative practice, purport to be a straightforward application of ordinary principles of stare decisis. As the implications of these decisions are worked out, the Court's robust understanding of the exclusions for laws of nature and abstract ideas seems to leave little room for patent protection for diagnostics. This Article reviews recent decisions on patent-eligibility from the Supreme Court and the Federal Circuit to demonstrate the obstacles to patenting diagnostic methods under emerging law. Although the courts have used different analytical approaches in recent cases, the bottom line is consistent: diagnostic applications are not patent eligible. I then consider what the absence of patents might mean for the future of innovation in diagnostic testing.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".