Bibliographic record
Abstract
Mr. Burk illustrates that federal courts have diverged along industry-specific paths when deciding patent cases. Burk highlights courts' disparate treatment of the biotechnology and computer software industries within the uniform patent statute. Due to industries' differing requirements for innovation and development, Professor Burk argues that the currently general patent statute and its incentive to innovate may be improved by tailoring it to specific industries. Burk creates a dialogue on what kinds of statutory schemes promote innovation. Citing the Supreme Court's statement in Diamond v. Chakrabarty that the patent statute is meant to cover anything under the sun made by man, Burk explains that courts are responsible for applying the patent statute to all new kinds of technology that Congress could not have fully anticipated when it passed the statute. Burk compares the U.S. approach to that of Canada, where certain new technologies will not be covered by its patent statute unless Parliament affirmatively legislates the matters. Essentially, Burk asks, what statutory scheme best promotes innovation? The author agrees with Chakrabarty in that the judiciary is the best place for this industry-specific tailoring to take place. Courts already use different policy levers in patent law, such as the obviousness and disclosure standards, to tailor to each industry. Professor Burk suggests that courts make more calculated uses of these tools to better promote innovation.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.034 |
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; both teacher heads agree on what is shown here.
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".