Understanding technology flux : analysing patent applications to understand economics activities - Nguyen, Dinh Phuc (2016)
Patents have become important intellectual properties of every company and may reflect market trend if analysed carefully. The number of patent applications is rapidly increasing worldwide, creating a great demand for a computer-assisted classification system to reduce human effort in categorizing new patent applications. In the first half of this project, a simple yet effective system was developed that helps categorize new patent applications using International Patent Classification (IPC) taxonomy. The system used Naïve Bayes (NB) classifier for its simplicity and effectiveness [1]. The NB classifier was trained and tested on a standard patents collection provided by World Intellectual Property Organization (WIPO) [2]. The highest accuracy achieved were 43.73% predicting the exact category in one guess and 65.19% predicting the exact category within 3 guesses. Patent analysis is an important step for every company that wants to understand market trend and economic landscape. In the second half of this project, a search engine was built upon Elasticsearch [3] with data crawled from Google patents database [4] and U.S Patent and Trademark Office (USPTO) [5] . The data can then be sought, visualized and analysed with Kibana [6]. In the end, approximately 2.7 million patents from 2005 till 2016 were crawled, indexed and made searchable via Kibana.