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Modeling Legal Uncertainty in AI-Generated Content Ownership: A Bayesian Network Approach to Intellectual Property Rights Allocation.

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Bibliographic Details
Title: Modeling Legal Uncertainty in AI-Generated Content Ownership: A Bayesian Network Approach to Intellectual Property Rights Allocation.
Authors: Li Y; Faculty of Law, Monash University; yinhanli1988@outlook.com.
Source: Journal of visualized experiments : JoVE [J Vis Exp] 2026 Jun 12 (232). Date of Electronic Publication: 2026 Jun 12.
Publication Type: Journal Article; Video-Audio Media
Journal Info: Publisher: MYJoVE Corporation Country of Publication: United States NLM ID: 101313252 Publication Model: Electronic Cited Medium: Internet ISSN: 1940-087X (Electronic) Linking ISSN: 1940087X NLM ISO Abbreviation: J Vis Exp Subsets: MEDLINE
Database: MEDLINE Ultimate
FullText Text:
  Availability: 0
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Modeling Legal Uncertainty in AI-Generated Content Ownership: A Bayesian Network Approach to Intellectual Property Rights Allocation.
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  Data: <searchLink fieldCode="AU" term="%22Li+Y%22">Li Y</searchLink>; Faculty of Law, Monash University; yinhanli1988@outlook.com.
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  Data: <searchLink fieldCode="JN" term="%22101313252%22">Journal of visualized experiments : JoVE</searchLink> [J Vis Exp] 2026 Jun 12 (232). <i>Date of Electronic Publication: </i>2026 Jun 12.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=42371815
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.3791/71101
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      – Code: eng
        Text: English
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      – TitleFull: Modeling Legal Uncertainty in AI-Generated Content Ownership: A Bayesian Network Approach to Intellectual Property Rights Allocation.
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            NameFull: Li Y
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          Dates:
            – D: 12
              M: 06
              Text: 2026 Jun 12
              Type: published
              Y: 2026
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              Value: 232
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            – TitleFull: Journal of visualized experiments : JoVE
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