Understanding the Trends in Blockchain Domain Through an Unsupervised Systematic Patent Analysis

Abstract

Patent analysis is crucial for technology monitoring, forecasting, and assessment, and facilitates entrepreneurs and different stakeholder groups to develop forward-looking technologies and business strategies. However, the speed and scale in the development of disruptive technologies, such as blockchain, present a challenge for analysts and experts. In this article, we propose an unsupervised systematic patent analysis framework that applies a mixture of cosine-based and density-based outlier analysis to the patent space. A sample of 13 393 blockchain-related patents published between January 2014 and June 2020 is used to test the proposed framework. Specifically, this framework merges cosine and density-based outlier detection methodologies to improve the identification of outliers within clusters of patents. The identified outliers are visualized through an age-outlier technology-opportunity analysis map that represents the different levels of novelty existing in each cluster of the patent sample. The map facilitates companies to better target their R&D efforts and maximize the return of technology investments. Benchmark results show that the proposed outlier detection method improves recall, precision, and f1 score. In addition, the results show that the cluster with a higher percentage of outliers represents the Internet of Things applications of blockchain technology.