Beyond IP Innovation Law: The Bigger Picture

Intellectual property is a powerful driver of innovation—but it is not the only one. In Beyond IP Innovation Law: The Bigger Picture, posted as a preprint and forthcoming in the European Media, IP & IT Law Review (MR-Int), Mauritz Kop argues that a serious innovation policy for the Fourth Industrial Revolution must reach past exclusive rights toward a fuller toolkit: prizes, grants, antitrust, commons-based production, open innovation, and a vital public domain.

Sustainable innovation law beyond IP

The essay frames "sustainable innovation law" as the interface between creativity, technology, society, and law—combining information law, antitrust, consumer protection, and fundamental rights with AI, machine learning, big data, quantum computing, CRISPR-Cas9, and virtual reality. Its test is normative: innovation counts as sustainable only when it is ethical and social, economically beneficial, conducive to well-being, and supportive of the environment. Once IP loses its monopoly on the policy imagination, a longer menu of incentive mechanisms—competitions, subsidies, tort law, market regulation, R&D tax incentives—comes into view, and choosing among them becomes the real task of the lawmaker.

Why AI can do without IP incentives

Applied to artificial intelligence, the argument is pointed: the classical justifications for IP are weak when applied to AI, and AI "can do without IP incentives," with narrow exceptions such as a medical AI system whose costly clinical trials might warrant patents or, equally, public subsidy. Human authorship and inventorship remain the normative anchor, and machine output that crosses an "autonomy threshold" should fall into the public domain under a model Kop calls Res Publicae ex Machina. The essay also presses for broad text-and-data-mining freedom—even an articulated right to process data for machine learning—so that training datasets, a prerequisite for supervised learning, do not become an IP chokepoint. This complements his theoretical and empirical work on quantum computing and intellectual property law.

A horizontal-vertical innovation architecture

Because incentives and risks vary by sector and by technology, the paper proposes a horizontal-vertical design: horizontal core rules for all 4IR technologies, plus vertical, risk-based regimes organized around a "pyramid of criticality" from low risk at the base to existential risk at the top. The calibration is physics-aware—an open posture may suit AI, while quantum technology warrants more ab initio control given its potential anthropogenic risks, a precautionary tilt Kop develops further in his work on ethics in the quantum age. Written against the European Commission's April 2021 draft AI Regulation, the essay reads that proposal as a "North Star" and urges that safety norms, interoperability standards, and the Trustworthy AI doctrine be embedded directly into the design of technology, monitored through life-cycle impact assessments. The bigger picture, in short, is an innovation law built for purpose—not the reflexive extension of twentieth-century IP.

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Mauritz Kop Presents Machine Learning & EU Data Sharing Practices at IPSC 2020, Stanford Law School

IPSC 2020 at Stanford: Mauritz Kop presented Machine Learning & EU Data Sharing Practices at the Intellectual Property Scholars Conference — the works-in-progress forum where IP scholarship is stress-tested before journals see it. The 2020 edition, hosted by Stanford Law School, ran as virtual panels from July 15 through August 5 in the first pandemic summer.

Training data under four regimes at once

Machine learning is hungry, and in Europe its raw material sits under copyright, database rights, trade secrets and the GDPR simultaneously. The paper mapped that intersection — including the text-and-data-mining exceptions of the DSM directive — and asked which data-sharing arrangements actually let lawful European AI development proceed at scale.

An argument for coordination

Where exclusive rights and data-protection rules overlap without coordination, they tax exactly the data flows the EU's own artificial intelligence strategy depends on. That modernization argument, workshopped before a predominantly American IP audience with a different copyright baseline and fair-use culture, had to hold up under comparative fire — which is precisely what the IPSC format is for.

Part of the Stanford research agenda

The presentation belonged to Kop's research line at the Stanford-Vienna Transatlantic Technology Law Forum, which he had joined earlier that year — see Mauritz Kop becomes TTLF Fellow at Stanford University. The paper is preserved in the permanent Stanford RQT collection at the Stanford Law Library, and its data-protection companion piece appeared in the Harvard Journal of Law & Technology's digest — two halves of one question about Europe's machine-learning data rules.

A format built for critique

Short presentations, dense Q&A, no published proceedings: IPSC exists purely to make drafts better before journals see them. For interdisciplinary work spanning artificial intelligence, data governance and IP doctrine, an audience of doctrinalists, economists and technologists probes each weak point in turn — and a European paper before an American room must hold up under a different copyright baseline and fair-use culture besides.

Why it still matters

The training-data questions posed in that 2020 draft — who may train on what, and on which terms — have since moved to the center of AI regulation on both sides of the Atlantic. Opt-out patchworks under the text-and-data-mining exceptions, the GDPR's reach into model pipelines, the competitive pull of jurisdictions with cleaner data rules: each was on the table at that virtual Stanford panel before it reached the regulators' agenda. The workshop critique of that summer became part of the foundation the later debates built on — which is exactly what a works-in-progress conference is supposed to produce.