Patent-Eligible Invention Requirement Under the European Patent Convention and its Implications on Creations Involving Artificial Intelligence

Artificial Intelligence and its subfield, Machine Learning are areas of computer science; thus, they rely on algorithms, models, computer programs and software applicable in numerous areas. Since respective creations involve resources and shift from hardware to software, there is an incentive to protect them legally. Due to their dual nature, the algorithms, models, computer programs, and software might be too “technical” to avail copyright protection but not “technical” enough for a patent. Whereas trade secret protection might not be sufficient means of protection in all cases.The article explores the issues and, as its main argument, builds further on the academic proposals on the sui generis mechanism. It also suggests certification as the potential approach to avail the desired protection instead of diluting the existing protection frameworks. An alternative would be to lie on the complete availability or trade secret protection, none of which would be an adequate balance

IMPLICATIONS FOR THE INVENTIVE STEP UNDER THE EUROPEAN PATENT CONVENTION RELATED TO THE INCREASING APPLICATION OF ARTIFICIAL INTELLIGENCE AND CERTIFICATION AS A SUI GENERIS PROTECTION MECHANISM FOR CREATIONS INVOLVING ARTIFICIAL INTELLIGENCE

The purpose of this article is to observe whether the European Patent Convention avails protection for creations involving Machine Learning regarding its current and future development. The article analyzes in more detail the notion of compliance with the requirement of an inventive step under the European Patent Convention when using Machine Learning becomes routine. The article concludes that, due to the specifics of Machine Learning, comprehensive protection for creations involving it would require conceptual amendments to the European Patent Convention. The author argues that instead of fundamentally amending the European Patent Convention, certification as a sui generis protection mechanism for creations involving Machine Learning could be a potential solution. The article further builds on and develops current academic proposals, providing an overview of the wider framework. The paper relies on the descriptive, analytical, historical and comparative legal methods to substantiate the main argument

Certification as a Remedy for Recognition of the Role of AI in the Inventive Process

CC BY 4.0Artificial Intelligence and its subfield Machine Learning have considerable potential to improve the welfare of humans. Due to the specifics of Artificial Intelligence and its enhancing capabilities, there is an increasing incentive to innovate if the role of Artificial Intelligence in the inventive process is recognized not solely as a tool under the patent legal framework. Nonetheless, since the concept of an “inventor” is traditionally attributed to natural persons, there is no consensus on whether the mentioned term should be interpreted as a living instrument. This article focuses on interpreting the concept of an “inventor” under the patent legal framework. It outlines the potential approaches to address an incentive to innovate if the role of Artificial Intelligence in the inventive process not only as a tool is reflected. The main argument developed in the article is that proposals to amend the patent legal framework to address the issue might not be as preferred as introducing the certification system instead

Algorithmic Explainability and the Sufficient-Disclosure Requirement under the European Patent Convention

Artificial intelligence and its subsector machine learning differs from traditional programming. For this reason, coupled with its potential benefits to society in many arenas, it has been articulated as one of the key priorities in the European Union. Such characteristics specific to artificial intelligence as models with increased accuracy and generalisation power may accentuate issues of algorithmic explainability that can defy patentability. Accordingly, the article focuses on the legal requirements related to the ‘sufficient disclosure’ criterion under the legal framework for patents as one facet of deciding on the patentability of the invention, and it addresses potential solutions for overcoming issues of algorithmic explainability. The author argues that solutions introducing a system involving deposit of the algorithm, training data, or both might not be as effective a mechanism for tackling those issues as instead implementing a recognised certification system