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|Title:||Understanding technology flux : analysing patent applications to understand economics activities||Authors:||Nguyen, Dinh Phuc||Keywords:||DRNTU::Engineering::Computer science and engineering::Computing methodologies::Document and text processing||Issue Date:||2016||Abstract:||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 . The NB classifier was trained and tested on a standard patents collection provided by World Intellectual Property Organization (WIPO) . 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  with data crawled from Google patents database  and U.S Patent and Trademark Office (USPTO)  . The data can then be sought, visualized and analysed with Kibana . In the end, approximately 2.7 million patents from 2005 till 2016 were crawled, indexed and made searchable via Kibana.||URI:||http://hdl.handle.net/10356/66615||Rights:||Nanyang Technological University||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Student Reports (FYP/IA/PA/PI)|
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