Intellectual Property in Quantum Computing and Market Power: A Theoretical Discussion and Empirical Analysis (Oxford University Press)

Delighted to see our article ‘Intellectual Property in Quantum Computing and Market Power: A Theoretical Discussion and Empirical Analysis’ -co-authored with my talented friends Prof. Mateo Aboy, PhD, SJD, FIT and Prof. Timo Minssen- published in the Journal of Intellectual Property Law & Practice (Oxford University Press), the flagship IP peer-reviewed OUP Journal, edited by Prof. Eleonora Rosati. Thanks to the JIPLP team for excellent editorial support! Our article: https://academic.oup.com/jiplp/article/17/8/613/6646536

This piece is the sisterpaper of our Max Planck @ Springer Nature published article titled ‘Mapping the Patent Landscape of Quantum Technologies: Patenting Trends, Innovation and Policy Implications’, which we wrote in parallel. The IIC quantum-patent study can be found here: https://link.springer.com/article/10.1007/s40319-022-01209-3. Our teamwork was absolutely gratifying and we hope it will inform strategic, evidence based transatlantic policy making.

IP and Antitrust Law

Please find a short synopsis of our work below:

We are on the verge of a technological revolution associated with quantum technologies, including quantum computing and quantum/artificial intelligence hybrids. Its complexity and global significance are creating potential innovation distortions, which could not have been foreseen when current IP and antitrust systems where developed.

Potential IP Overprotection

Using quantitative methods, we investigated our hypothesis that IP overprotection requires a reform of existing IP regimes for quantum tech, to avoid or repair IP thickets, fragmented exclusionary rights and anticommons concerns, lost opportunity costs, and an unwanted concentration of market power.

Perhaps counter-intuitively, we found that there appear to be (at least so far) no such overprotection problems in the real-world quantum computing field to the extent that their consequences would hinder exponential innovation in this specific branch of applied quantum technology, as more and more quantum patent information enters the public domain.

Patents versus Trade Secrets and State Secrets

However, developments taking place in secrecy, either by trade secrets or state secrets, remains the Achilles heel of our empirical approach, as information about these innovations is not represented by our dataset, and thus cannot be observed, replicated or generalized.

Interplay between IP and Antitrust Law: Open or Closed Innovation Systems

Policy makers should urgently answer questions regarding pushing for open or closed innovation systems including the interplay between IP and antitrust law, taking into account dilemma’s pertaining to equal/equitable access to benefits, risk control, ethics, and overall societal impact. Crucially, intellectual property in quantum technology has a national safety and (cyber)security dimension, often beyond the IP toolkit.

Meer lezen

Artificial Intelligence, Data processing, Intellectual Property, Antitrust, Competition Law, Empirical researchMauritz Kop19 juli 2022AI, Intellectueel Eigendom, Data, Antitrust, fair competition

Machine Learning & EU Data Sharing Practices

Stanford - Vienna Transatlantic Technology Law Forum, Transatlantic Antitrust and IPR Developments, Stanford University, Issue No. 1/2020

New multidisciplinary research article: ‘Machine Learning & EU Data Sharing Practices’.

Download the article here: Kop_Machine Learning and EU Data Sharing Practices-Stanford University

In short, the article connects the dots between intellectual property (IP) on data, data ownership and data protection (GDPR and FFD), in an easy to understand manner. It also provides AI and Data policy and regulatory recommendations to the EU legislature.

As we all know, machine learning & data science can help accelerate many aspects of the development of drugs, antibody prophylaxis, serology tests and vaccines.

Supervised machine learning needs annotated training datasets

Data sharing is a prerequisite for a successful Transatlantic AI ecosystem. Hand-labelled, annotated training datasets (corpora) are a sine qua non for supervised machine learning. But what about intellectual property (IP) and data protection?

Data that represent IP subject matter are protected by IP rights. Unlicensed (or uncleared) use of machine learning input data potentially results in an avalanche of copyright (reproduction right) and database right (extraction right) infringements. The article offers three solutions that address the input (training) data copyright clearance problem and create breathing room for AI developers.

The article contends that introducing an absolute data property right or a (neighbouring) data producer right for augmented machine learning training corpora or other classes of data is not opportune.

Legal reform and data-driven economy

In an era of exponential innovation, it is urgent and opportune that both the TSD, the CDSM and the DD shall be reformed by the EU Commission with the data-driven economy in mind.

Freedom of expression and information, public domain, competition law

Implementing a sui generis system of protection for AI-generated Creations & Inventions is -in most industrial sectors- not necessary since machines do not need incentives to create or invent. Where incentives are needed, IP alternatives exist. Autonomously generated non-personal data should fall into the public domain. The article argues that strengthening and articulation of competition law is more opportune than extending IP rights.

Data protection and privacy

More and more datasets consist of both personal and non-personal machine generated data. Both the General Data Protection Regulation (GDPR) and the Regulation on the free flow of non-personal data (FFD) apply to these ‘mixed datasets’.

Besides the legal dimensions, the article describes the technical dimensions of data in machine learning and federated learning.

Modalities of future AI-regulation

Society should actively shape technology for good. The alternative is that other societies, with different social norms and democratic standards, impose their values on us through the design of their technology. With built-in public values, including Privacy by Design that safeguards data protection, data security and data access rights, the federated learning model is consistent with Human-Centered AI and the European Trustworthy AI paradigm.