A Patent Analysis to Identify Emergent Topics and Convergence Fields: A Case Study of Chitosan

Identifying emerging technology trends from patents helps to understand the status of the technology commercialization or utilization. It could provide research insights leading to advanced technological innovations that stimulate socially responsible research to address human dietary and medical needs. However, few studies have investigated emerging chitosan applications using patents. In this study, we report the application of a patent bibliometric predictive intelligence (PBPI) model to identify emergent topics and technology convergence related to chitosan applications from patents in the International Patent Classification system. Text mining was used to extract patterns from 5001 patents and each term was assigned an emergent score, following which we traced growth patterns, examined relationships between IPCs, emergent topics, and patents using correlation analysis and principal component analysis, and conducted matrix and cluster mapping analysis to understand industrial applications and explore patterns of technological convergence. Five major terms emerged in association with ascending and newly emergent topics over the last 13 years: “shelf life,” “antibacterial,” “good safety,” “absorbing water,” and “auxiliary materials.” These topics were closely linked with research in the biomedical and food production and preservation industries. A network analysis indicated that “antibacterial” terms exhibited the highest degree of convergence, followed by “shelf life.” These findings can inform strategies to determine new directions for chitosan research.

The nature of ICT in technology convergence: A knowledge-based network analysis

This study aims to understand the nature of information and communication technology in technology convergence. We form a knowledge network by applying social network theories to Korean patent data collected from the European Patent Organization. A knowledge network consists of nodes representing technology sectors identified by their International Patent Classification codes and edges that link International Patent Classification codes when they appear concurrently in a patent. We test the proposed hypotheses using four indices (degree centrality, E-I index, entropy index, and clustering coefficient). The results show that information and communication technology is easily attached but tends to converge with similar technology and has the greatest influence on technology convergence over other technologies. This study is expected to help practitioners and policymakers understand the structure and interaction mechanisms of technology from a systematic perspective and improve national-level technology policies.