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Record W2992167429

Licensee Patent Challenges

2014· article· de· W2992167429 on OpenAlex
Alan D. Miller, Michal S. Gal

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueYale journal on regulation · 2014
Typearticle
Languagede
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsWestern University
Fundersnot available
KeywordsLicenseeBusinessPolitical scienceLawLicense
DOInot available

Abstract

fetched live from OpenAlex

We analyze contractual clauses which limit the ability of licensees to challenge patents at the basis of their licensing agreements. In particular, we study no-contest clauses, which prohibit licensees from contesting the validity of the patent, and challenge-penalty clauses, which penalize licensees for doing so. We develop a model that we use to compare three legal regimes: "No Restriction, " in which the patent holder is given complete contractual freedom, "Partial Restriction, " in which no-contest clauses are forbidden but challenge penalties are allowed, and "Total Restriction," in which neither no-contest nor challenge penalty clauses are enforced. We show that No Restriction is unlikely to be optimal, and further, we provide necessary and sufficient conditions under which Total Restriction is optimal. The rule we suggest differs significantly from the one currently applied by most courts.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.066
GPT teacher head0.285
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it