Resumen
A patent is an important document issued by the government to protect inventions or product design. Inventions consist of mechanical structures, production processes, quality improvements of products, and so on. Generally, goods or appliances in everyday life are a result of an invention or product design that has been published in patent documents. A new invention contributes to the standard of living, improves productivity and quality, reduces production costs for industry, or delivers products with higher added value. Patent documents are considered to be excellent sources of knowledge in a particular field of technology, leading to inventions. Technology trend forecasting from patent documents depends on the subjective experience of experts. However, accumulated patent documents consist of a huge amount of text data, making it more difficult for those experts to gain knowledge precisely and promptly. Therefore, technology trend forecasting using objective methods is more feasible. There are many statistical methods applied to patent analysis, for example, technology overview, investment volume, and the technology life cycle. There are also data mining methods by which patent documents can be classified, such as by technical characteristics, to support business decision-making. The main contribution of this study is to apply data mining methods and social network analysis to gain knowledge in emerging technologies and find informative technology trends from patent data. We experimented with our techniques on data retrieved from the European Patent Office (EPO) website. The technique includes K-means clustering, text mining, and association rule mining methods. The patent data analyzed include the International Patent Classification (IPC) code and patent titles. Association rule mining was applied to find associative relationships among patent data, then combined with social network analysis (SNA) to further analyze technology trends. SNA provided metric measurements to explore the most influential technology as well as visualize data in various network layouts. The results showed emerging technology clusters, their meaningful patterns, and a network structure, and suggested information for the development of technologies and inventions.