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Record W4236681520 · doi:10.31235/osf.io/2znvb

Our Divided Patent System

2016· preprint· en· W4236681520 on OpenAlexaff
Mark A. Lemley

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsCentennial College
Fundersnot available
KeywordsPatent trollBusinessPatent lawOutcome (game theory)Patent infringementIntellectual propertyLawEconomicsPolitical science

Abstract

fetched live from OpenAlex

In this comprehensive new study, we evaluate all substantive decisionsrendered by any court in every patent case filed in 2008 and 2009 —decisions made between 2009 and 2013. We assess the outcome of litigationby technology and industry. We relate the outcomes of those cases to a hostof variables, including variables related to the parties, the patents, andthe courts in which those cases were litigated.We find dramatic differences in the outcomes of patent litigation by bothtechnology and industry. For example, owners of patents in thepharmaceutical industry fare much better in dispositive litigation rulingsthan do owners of patents in the computer & electronics industry, andchemistry patents have much greater success in litigation than theirsoftware or biotech counterparts. Our results provide an important windowinto both patent litigation and the industry-specific battles over patentreform. And they suggest that the traditional narrative ofindustry-specific patent disputes, which pits the IT industries against thelife sciences, is incomplete.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.066
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.007
Science and technology studies0.0040.004
Scholarly communication0.0110.012
Open science0.0020.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0660.009

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 head0.276
GPT teacher head0.241
Teacher spread0.034 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2016
Admission routes1
Has abstractyes

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Same topicIntellectual Property and PatentsFrench-language works237,207