TY - GEN
T1 - A probabilistic approach to identifying technology vacuum
T2 - Portland International Center for Management of Engineering and Technology - Technology Management for Global Economic Growth, PICMET '10
AU - Son, Changho
AU - Suh, Yongyoon
AU - Lee, Youjen
AU - Park, Yongtae
PY - 2010
Y1 - 2010
N2 - A patent map has long been considered as a useful tool to identify technology vacuum defined as an unexplored area of technologies that may deserve intensive investigation for future new technology development. However, previous studies for identifying technology vacuum on the patent map have been subjected to intuitive and manual identification of technology vacuum. In this context, this paper proposes a generative topographic mapping (GTM)-based patent map which aims to identify technology vacuum automatically. Since GTM is a probabilistic approach to map a low-dimensional latent space onto the multidimensional data space and vice versa, it contributes to the automatic identification of technology vacuum. This study consists of three stages. Firstly, text mining is executed to transform patent documents into keyword vectors as structured data. Secondly, the GTM is employed to develop the patent map with extracted keyword vectors and discover patent vacuums which are expressed as blank areas in the map. Lastly, technology vacuums are identified by inversely mapping patent vacuums in latent space into new vectors in data space. The procedure of the proposed approach is described in detail by employing a patent database.
AB - A patent map has long been considered as a useful tool to identify technology vacuum defined as an unexplored area of technologies that may deserve intensive investigation for future new technology development. However, previous studies for identifying technology vacuum on the patent map have been subjected to intuitive and manual identification of technology vacuum. In this context, this paper proposes a generative topographic mapping (GTM)-based patent map which aims to identify technology vacuum automatically. Since GTM is a probabilistic approach to map a low-dimensional latent space onto the multidimensional data space and vice versa, it contributes to the automatic identification of technology vacuum. This study consists of three stages. Firstly, text mining is executed to transform patent documents into keyword vectors as structured data. Secondly, the GTM is employed to develop the patent map with extracted keyword vectors and discover patent vacuums which are expressed as blank areas in the map. Lastly, technology vacuums are identified by inversely mapping patent vacuums in latent space into new vectors in data space. The procedure of the proposed approach is described in detail by employing a patent database.
UR - http://www.scopus.com/inward/record.url?scp=78549261781&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:78549261781
SN - 1890843229
SN - 9781890843229
T3 - PICMET '10 - Portland International Center for Management of Engineering and Technology, Proceedings - Technology Management for Global Economic Growth
SP - 2127
EP - 2134
BT - PICMET '10 - Portland International Center for Management of Engineering and Technology, Proceedings - Technology Management for Global Economic Growth
Y2 - 18 July 2010 through 22 July 2010
ER -