Ipr In AI-Assisted Digital Asset Management Robots.
IPR in AI-Assisted Digital Asset Management Robots
1. Introduction
AI-assisted Digital Asset Management (DAM) robots are autonomous systems that:
Catalog, tag, and organize digital assets like images, videos, music, and documents
Apply AI for automatic metadata generation, copyright detection, and usage tracking
Suggest licensing or redistribution strategies
Can autonomously create derivative content
IPR relevance:
Copyright: Original content management, derivative works, automated tagging
Patent: AI algorithms, automation techniques, cloud-based DAM processes
Trade secrets: Proprietary classification algorithms
Ownership issues: Who owns AI-generated metadata, derivative works, or AI-curated databases?
Key legal questions:
Can AI be the author of digital content or metadata?
Are AI-generated derivative works protected under copyright?
Who owns outputs created or curated by DAM robots?
Are AI algorithms patentable?
How to handle infringement if AI uses copyrighted content?
CORE IPR AREAS IN AI DAM ROBOTS
Copyright
Metadata, automated tagging, derivative digital works
AI cannot be legal author → human authorship required
AI may infringe copyright if it reproduces copyrighted content
Patents
AI algorithms for automatic classification, recommendation engines
Hardware + software integration (cloud + local processing)
Autonomous content recognition and distribution
Trade Secrets
Proprietary AI models
Classification and recommendation engines
Training datasets
Database Rights
AI-curated databases may have protection under database rights (EU)
Collection and arrangement by AI may not qualify if no human creativity
CASE LAWS
Here are 7 key cases relevant to AI-assisted DAM robots:
CASE 1: Naruto v. Slater (Monkey Selfie Case)
Facts:
A monkey took selfies with a camera left unattended.
Copyright claimed for the photographs.
Issue:
Can a non-human entity hold copyright?
Judgment:
Only human beings can be authors.
Non-human creators cannot claim copyright.
Relevance to AI DAM Robots:
AI-generated metadata, auto-tagged content, or derivative works cannot be copyrighted by AI alone.
Ownership must be assigned to human or organization.
Legal Principle:
Copyright requires human intellectual effort.
CASE 2: Feist Publications v. Rural Telephone Service (US, 1991)
Facts:
Compilation of telephone directories.
Issue: whether facts and arrangement are copyrightable.
Judgment:
Facts themselves are not copyrightable.
Only creative selection or arrangement qualifies.
Relevance to AI DAM Robots:
AI-curated digital libraries may not have copyright on mere cataloging.
Human involvement in selection or tagging increases protection.
Legal Principle:
Originality requires creative input, not mere mechanical arrangement.
CASE 3: Authors Guild v. Google (Google Books, US, 2015)
Facts:
Google digitized millions of books to create searchable database.
Claimed fair use; authors claimed copyright infringement.
Judgment:
Court allowed use for transformative purpose.
Large-scale copying for indexing and search was fair use.
Relevance to AI DAM Robots:
AI DAM robots using copyrighted content to generate metadata or indexing may be fair use if:
Transformative
Does not substitute for the original
Automatic tagging, AI-generated previews may be permitted under fair use doctrine.
Legal Principle:
Transformative use may allow limited use of copyrighted works without infringement.
CASE 4: Naruto v. Slater Applied in AI (AI Authorship Cases like DABUS)
Facts:
DABUS AI generated inventions autonomously.
Judgment:
Only humans can be recognized as inventors.
AI is considered a tool, not an author or inventor.
Relevance to AI DAM Robots:
AI-generated metadata or content suggestions cannot hold IP rights independently.
Human supervisors or programmers are legal owners.
CASE 5: Bridgeman Art Library v. Corel Corp (US, 1999)
Facts:
Photographs of public domain artworks.
Issue: whether exact reproductions are copyrightable.
Judgment:
Exact photographic reproductions of public domain works cannot have copyright because no originality is involved.
Relevance to AI DAM Robots:
AI capturing digital copies of public domain images or videos cannot claim copyright.
Human creative input is necessary to make derivative works protectable.
Legal Principle:
Originality and human creativity are required for copyright.
CASE 6: University of London Press v. University Tutorial Press (UK, 1916)
Facts:
Copyright claimed in examination papers.
Judgment:
Original intellectual effort in creating content is copyrightable.
Human effort required for protection.
Relevance to AI DAM Robots:
If humans curate, tag, or annotate content suggested by AI, copyright can vest in humans or the institution.
Pure AI-generated cataloging may not qualify.
CASE 7: R.G. Anand v. Delux Films (India, 1978)
Facts:
Alleged copyright infringement in a film adaptation.
Judgment:
Ideas are free; expression is protected.
Substantial similarity test applied.
Relevance to AI DAM Robots:
AI-generated content may risk infringement if it copies expression, not just ideas.
Organizations must monitor AI outputs to avoid copying expression from copyrighted works.
PATENT AND TECH CASES
Case: Diamond v. Diehr (US, 1981)
Software controlling a physical process (rubber curing) is patentable.
Relevance: AI DAM robots combining software + robotic hardware for content management or distribution are patentable.
Case: Alice Corp. v. CLS Bank (US, 2014)
Abstract algorithms alone are not patentable.
Relevance: AI classification or recommendation must be tied to specific technical processes to be patentable.
KEY TAKEAWAYS FOR AI DAM ROBOTS
Authorship: AI cannot be legal author. Human involvement is essential for copyright.
Derivative Works: AI-curated or AI-tagged content may only be protected if humans contribute creatively.
Patentability: AI + physical systems (robots managing digital storage, cloud infrastructure, or autonomous sorting) are patentable; abstract algorithms alone are not.
Ownership: Typically vests in the human programmer, employer, or institution using AI.
Liability: AI itself has no legal liability; humans or organizations are responsible.
Fair Use / Transformative Use: Automated indexing, tagging, or classification may fall under fair use if transformative and non-substitutive.
RELATED Blog
comments