Identifying dynamic knowledge flow patterns of business method patents with a hidden Markov model

As an alternative to the conventional R&D innovation, business models are becoming an important locus of lucrative innovation. Due to the rise of the internet economy, business model innovation today often involves technological innovation, and this can be evidenced by business method (BM) patents. Of several mechanisms that stimulate business model innovation, the role of BM patents is probably most noteworthy. To understand how BM patents play their roles in business model innovation, we need to observe the long-term knowledge flow process. Therefore, we aim to identify dynamic patterns of knowledge flows driven by BM patents using a hidden Markov model (HMM) and patent citation data as an input. An HMM is a popular statistical tool for modelling a wide range of time series data. Since it does not have any general theoretical limit in regard to statistical pattern classification, an HMM is capable of characterizing various temporal patterns. A case study is conducted with the BM patents in 16 USPTO subclasses related to secure transactions. After patterns of the individual subclasses are generated, they are grouped into four major patterns through clustering analysis and their characteristics are closely examined. Our analysis reveals that the BM patents for secure transaction in general play increasingly important roles in advancement of business models, facilitating the transfer of knowledge, and thus can provide valuable insights in formulating more effective strategies or policies for business model innovation.