Mapping global innovation: analysing patents

The project was organised into three distinct but related subprojects that examined the origins, development and change in the global structure of knowledge networks. The first subproject employed contemporary database management techniques and technologies to create and manage what is possibly the largest knowledge network available. The second subproject applied information-retrieval algorithms and innovative modelling techniques to identify the subnetwork that links Switzerland to the global structure of knowledge networks. The third subproject employed results from the latest generation of statistical models for social networks to scale up currently available models and apply them to networks of any size.

Patent citations represent the link that any given patent has to other patents. As such, patent citations constitute a trail left by the process of knowledge production. Reframing and understanding patent citation networks helps to shed light on the global production and transformation of knowledge.

We intended to construct the largest and most complete knowledge network currently available. We tried to do so by merging separate patent data sets provided by the Organisation for Economic Co-operation and Development (OECD) with information on corporate entities to link patents to company-specific information. We used the new data sets to develop and test innovative information retrieval techniques and network analysis models. We then applied these techniques and analytical models to large data sets with complex and evolving network structures.

Switzerland consistently ranks first (most recently in 2021) in the Global Innovation Index published by the World Intellectual Property Organization and other leading entities. Understanding how knowledge is produced, combined and transformed is key to sustaining Switzerland’s global leadership in innovation.

A novel approach for the early identification of “influential” patents was developed. Identifying top patents in a particular technology category can help companies gain an overview of important innovations in their field of concern. It can also benefit governments in deciding various policies such as funding particular technology areas. The new approach developed in this project proved to be both qualitatively and quantitatively better than existing state-of-the-art approaches for the identification of milestone patents.

In addition, new algorithms were developed to “scale up” a widely used sophisticated statistical model of social (and other, including citation) networks. This allows the method to be applied to networks far larger than previously possible. These new algorithms were implemented in open-source software and applied to large patent citation networks. This study revealed, using a model that deals with the effects of multiple factors such as patent recency and overlapping technology categorisation, that country of origin is associated with how frequently a patent is cited. In particular, this was true for Switzerland, which is known to be a prolific innovation hub.

Another result of this project was the creation of a Patent Search tool integrated into a text editor. This system can effectively be used for searching Swiss patents, as it has support for multi-lingual search.

The Global Structure of Knowledge Networks: Data, Models and Empirical Results