One of the most important imperatives in IP management is the alignment of IP management with the business model of the respective business. In an ideal case, the alignment of IP management with the business may use the S-curve characteristics of innovation, which the business relies on or which may affect the business under consideration. Using innovation S-curves allows to understand how critical innovations and thus the businesses built around these innovations evolve with time.
The innovation S-curve has been adapted and applied to trends in R&D activities. Intellectual Property databases have been analyzed in the field of IoT. While keyword searches in IP databases may mask real-world trends due to buzz word effects, class-based searches look promising in this respect. While conventional approaches analyze cumulative patent data, for the case of IoT also the annual patent rates seem to be in excellent agreement with S-curve characteristics for relevant jurisdictions. This holds true for both the annual application rate and the annual grant rate in relevant patent classes.
The ability to model approximately three decades of patent activities with two S-curves only, provides a promising tool for trend identification and trend prognosis in emerging technologies and may be used for trend scouting of R&D trends in an early stage, so that IP management may specifically align with these trends in the respective business.
In different jurisdictions, slight differences both in the time evolution of annual patent applications and annual patent grants can be observed. One promising hypothesis for explaining those differences may be the different approaches towards computer-implemented inventions in different jurisdictions. Remarkably, however, the prominent Alice decision in the United States leaves no dominant footprint, neither in the annual application rate nor in the annual grant rate, respectively.
A more short-term goal is of course given by a more complete mapping of IoT-related technologies onto the available classification scheme in order to identify several emerging trends and safely attribute them to individual technologies. An additional short-term goal is an appropriate content attribution to the S-curves observed. Of course, it would be of interest to further investigate the interplay of individual patent classes and the associated observed trends in the annual patent activities.
This research project is conducted by MIPLM graduate Dr. Björn Möller and supervised by Prof. Dr. Alexander Wurzer and Dr. Thibaud Lelong both CEIPI.
Dr. Björn Möller is a physicist by training with a background in semiconductor nanostructures, mathematics and quantum information technology. Björn worked in academia and received his training in Intellectual Property in a law firm, the German Patent and Trademark Office and the German Federal Patent Court. He has been admitted as a European Patent Attorney and works as a Senior IP Counsel with Siemens AG in Munich.
Here is a description of the research project: Business Aligement of IP for the Internet of Things in an early R&D phase