Open AccessIdentifying Automotive Industry Trends: Data Mining from Intellectual Property Databases
Author(s) -
Roland Attila Csizmazia
Publication year - 2021
Publication title -
journal of business and management studies
Language(s) - English
Resource type - Journals
ISSN - 2709-0876
DOI - 10.32996/jbms.2021.3.2.12
Subject(s) - automotive industry , intellectual property , patent visualisation , competition (biology) , process (computing) , computer science , data science , social network analysis , database , patent analysis , competitive advantage , business , marketing , engineering , world wide web , social media , ecology , biology , aerospace engineering , operating system
Patents protect the patent holders in that the patented process, design and invention can only be used or sold by the holder exclusively. Therefore, manufacturers use patents to gain a competitive edge against the competition. A patent analysis discovers which car parts are considered the most for future development in the automotive sector. The purpose of this paper is to identify and analyze major trends and the potential implementation of patents. Furthermore, the research may reveal in detail the common R&D trends within a certain industry, differences among the major representative manufacturers and support to identify feasible future strategies for lagers. The patent analysis will be launched with the data collection from a patent database. To avoid the extensive computing time in R, only each patent document's abstract is deployed for the research. After data cleansing, the term frequency-inverse document frequency algorithm is used to find the keywords in the patent abstracts. To visualize results, the social network analysis is conducted. It identifies trends and relationships among the mapped keywords. The discovered major keywords constitute the graphs of the most important parts related to each other and are considered for the future.