Nearness and Farness of IPR Laws in the Age of Advanced Technologies (AI, ML, DL, Big Data & Beyond).

Nearness and Farness of IPR Laws in the Age of Advanced Technologies (AI, ML, DL, Big Data & Beyond).

Emerging technologies such as Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Big Data (BD), and autonomous digital systems are reshaping global industries at a speed that traditional legal frameworks struggle to match. Intellectual Property Rights (IPR) regimes designed over decades based on human authorship, territoriality, and tangible outputs now face unprecedented conceptual and operational challenges.

The relationship between modern IPR laws and advanced technologies can be characterized through two lenses: “nearness” where laws remain relevant and adaptable and “farness” where significant gaps, mismatches, or doctrinal limitations persist.

1. The “Nearness” of IPR Laws to Advanced Technologies

Despite rapid technological shifts, several principles of IPR remain surprisingly resilient. These offer a degree of nearness and adaptability to the new digital landscape.

1.1 Copyright Still Protects Human-Created Works Built on AI Tools

Current laws protect works created by humans, even if AI assists in the creative process.
As long as a human makes intellectual choices planning, selection, arrangement the resulting output qualifies for protection.
This ensures:

• protection of human creativity enhanced by AI,
• stability for creative industries using AI as a tool (e.g., music mastering, film VFX, digital design).

Thus, copyright remains “near” enough to address hybrid creations.

1.2 Patents Continue to Apply to AI-Enabled Inventions

AI, ML, and DL systems often contribute to research and development, but patents still comfortably protect:

• AI-enabled devices,
• ML-driven diagnostics,
• algorithms embodied in hardware,
• process improvements implemented through software.

As long as there is human contribution, inventive step, and industrial applicability, patent doctrines still function.

1.3 Trade Secrets Align Naturally with Big Data Systems

Big Data technologies rely on massive datasets, proprietary algorithms, and trained models.
These can be protected as trade secrets, provided:

• reasonable secrecy measures exist,
• commercial value arises from secrecy.

Because companies often prefer non-disclosure over patenting (to avoid disclosure), Big Data and “black box” models fit neatly within existing trade secret law.

1.4 Trademarks Still Protect Brand Identity in Digital Markets

Even in AI-driven marketplaces or automated e-commerce ecosystems, trademark law continues to regulate:

• brand confusion,
• deceptive practices,
• misuse of names in AI-generated content,
• impersonation by bots.

Trademark doctrine remains robust and technologically adaptable.

2. The “Farness” of IPR Laws from Advanced Technologies

Despite areas of alignment, several fundamental mismatches exist where current laws appear distant, outdated, or conceptually challenged.

**2.1 “Who is the Author?” — AI-Generated Works Fall into a Legal Vacuum

Most legislations require human authorship. However, generative AI systems now create:

• images,
• literature,
• design prototypes,
• software code,
• music,
• 3D models.

The law struggles with questions like:

• Should AI-generated outputs be copyrightable?
• Who owns them—the developer, the user, or nobody?
• Can AI ever be an “author”?

The distance on this issue is stark: technology advances daily, while legislation lags behind.

2.2 Patent Law Faces Challenges When AI Invents

AI systems are increasingly autonomous: they optimize molecules, design materials, or generate engineering solutions without human direction.

This raises major concerns:

• Can an AI be named as an inventor?
• Should AI-assisted inventions still require a human inventive step?
• How to evaluate non-obviousness when AI processes millions of permutations?

Most patent office’s reject AI inventorship, creating a substantial “farness” gap between innovation reality and legal doctrine.

2.3 Big Data Conflicts with Databases and Copyright

Big Data systems require extensive data ingestion, including:

• publicly available data,
• scraped content,
• metadata,
• social media text,
• digitized books and images.

IPR concerns include:

• copyright breach in training datasets,
• database rights conflicts,
• moral rights issues,
• privacy/IP overlap.

Existing laws were not built for mass automated ingestion, producing a significant mismatch.

2.4 Machine Learning Models and Patent Eligibility Issues

ML/DL models often face challenges under patent law because they are viewed as:

• abstract mathematical methods,
• algorithms without technical effect,
• non-patentable subject matter in some jurisdictions.

This leads to uncertainty for developers, who must navigate different interpretations across jurisdictions.

2.5 Ownership Problems in Autonomous Decision Systems

Smart systems autonomously:

• curate content,
• trade securities,
• generate designs,
• optimize architectural plans,
• produce derivative works.

When an autonomous system “creates,” the question becomes:
Who owns the output?
The user?
The trainer?
The dataset owner?
The software provider?

IPR law currently has no consistent answer.

2.6 Territoriality vs. Borderless Digital Technologies

IPR rights remain territorial, but AI and BD systems are inherently global.
A dataset may be generated in Country A, processed in Country B, trained in Country C, and deployed worldwide.
But:

• Which jurisdiction’s IP laws apply?
• How are infringements enforced?
• How do courts address cross-border algorithmic outputs?

The mismatch between territorial laws and borderless technologies is one of the biggest gaps today.

3. Striking the Balance: The Evolving Middle Ground

Globally, regulatory bodies and courts are beginning to acknowledge these challenges. Emerging responses include:

• AI-specific copyright guidelines,
• policy papers on AI inventorship,
• data licensing frameworks,
• contractual governance models,
• sui generis rights for training datasets,
• liability standards for autonomous decision-making.

Many countries are moving toward a “rights + regulation + responsibility” model to bridge the nearness and farness gap.

4. Conclusion

IPR laws are neither wholly outdated nor fully prepared for the age of AI and Big Data.
They remain “near” enough to protect human creativity, technological inventions, and proprietary information. But they are “far” from addressing the deeper issues introduced by autonomous systems, mass data processing, and machine-generated content.

As technologies evolve faster than regulatory systems, the world stands at a crucial crossroads:

• adapt existing IPR doctrines,
• create new statutory categories, or
• redesign the IP ecosystem for a future where humans and machines co-create.

The answer will define the next generation of innovation governance.

***