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Record W3123572990 · doi:10.1111/jems.12393

An economic model of patent exhaustion

2020· article· en· W3123572990 on OpenAlexaff
Olena Ivus, Edwin L.‐C. Lai, Ted M. Sichelman

Bibliographic record

VenueJournal of Economics & Management Strategy · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsQueen's University
Fundersnot available
KeywordsLicenseTransaction costBusinessValuation (finance)Database transactionIncentiveDownstream (manufacturing)CommerceMicroeconomicsIndustrial organizationEconomicsFinanceLawMarketing

Abstract

fetched live from OpenAlex

Abstract The doctrine of patent exhaustion implies that the authorized sale of patented goods “exhausts” the patent rights in the goods sold and precludes additional license fees from downstream buyers. Courts have considered absolute exhaustion, in which the patent owner forfeits all rights upon an authorized sale, and presumptive exhaustion, in which the patent owner may opt‐out of exhaustion via contract. This paper offers the first economic model of domestic patent exhaustion that incorporates transaction costs in licensing downstream buyers and considers how the shift from absolute to presumptive exhaustion affects social welfare. We show that when transaction costs are high, the patent owner has no incentive to individually license downstream users, and absolute and presumptive exhaustion regimes are equivalent. But when transaction costs are at the intermediate level, the patent owner engages in mixed licensing, individually licensing high‐valuation buyers and uniformly licensing low‐valuation buyers. Presumptive exhaustion is socially optimal when social benefits from buyer‐specific pricing outweigh social costs from transaction cost frictions in individualized licensing, which requires sufficiently low transaction costs.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.252
GPT teacher head0.235
Teacher spread0.017 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2020
Admission routes1
Has abstractyes

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