Intellectual Property in the Age of Self-Learning Systems

(Neural-IP)

Deep Neural Networks (DNN) are currently the key technology for autonomous vehicles, algorithmic trading, or decision-making systems in human resource, legal, and other robotic process automation scenarios. A key characteristic of these systems is their evolving nature - they are self-learning unlike traditional software, which is programmed. This, however, raises new challenges to known concepts such as intellectual property (IP) management. This project aims to re-think the current definition of IP in the context of deep neural networks. It further investigates how original work can be understood in the realm of continuously trained deep neural networks.

 

This is a collaborative project between Prof. Oliver Gassmann's Chair of Innovation Management at the Institute of Technology Management and Prof. Damian Borth's Chair of Artificial Intelligence and Machine Learning (AI:ML) at the Institute of Computer Science. The project runs from August 2019 until the end of October 2020.

Team

OGassmann
Prof. Dr. Oliver Gassmann
Professor of Innovation Management

Director ITEM

Chair for Innovation Management

NHaefner
Prof. Dr. Naomi Haefner
Assistant Professor of Technology Management