TY - GEN
T1 - A Study on the Comparison of Feature Extraction Methods for Classification of Patent Litigation
AU - Kim, Youngho
AU - Lee, Junseok
AU - Kang, Jiho
AU - Lee, Juhyun
AU - Jang, Dongsik
AU - Park, Sangsung
N1 - Funding Information:
Acknowledgements This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Republic of Korea government (MSIT) (No. NRF– 2020R1A2C1005918). This research was supported by the MOTIE (Ministry of Trade, Industry, and Energy) in Korea, under the Fostering Global Talents for Innovative Growth Program (P0008749) supervised by the Korea Institute for Advancement of Technology (KIAT).
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Patents contain detailed information about the developed technology. In addition, patents grant exclusive rights to the developed technology. For this reason, many companies use patents for technology protection. Patent litigation occurs when the operating activities of one company infringe on the scope of the patent rights of another company. When patent litigation occurs, a lot of time and money are consumed. Therefore, it is necessary to prevent patent litigation in advance. In this paper, an appropriate feature extraction method is sought when constructing a model for classifying patent litigation. Principal component analysis and Autoencoder are used to perform the proposed research. The experimental data are those registered with the USPTO as patents related to artificial intelligence. Feature extraction is performed on the quantitative indicators of the collected patents. In addition, performance is measured with various classification algorithms. As a result of the experiment, the classification performance of the method combining Autoencoder and K-Nearest neighbor was good.
AB - Patents contain detailed information about the developed technology. In addition, patents grant exclusive rights to the developed technology. For this reason, many companies use patents for technology protection. Patent litigation occurs when the operating activities of one company infringe on the scope of the patent rights of another company. When patent litigation occurs, a lot of time and money are consumed. Therefore, it is necessary to prevent patent litigation in advance. In this paper, an appropriate feature extraction method is sought when constructing a model for classifying patent litigation. Principal component analysis and Autoencoder are used to perform the proposed research. The experimental data are those registered with the USPTO as patents related to artificial intelligence. Feature extraction is performed on the quantitative indicators of the collected patents. In addition, performance is measured with various classification algorithms. As a result of the experiment, the classification performance of the method combining Autoencoder and K-Nearest neighbor was good.
KW - Classification
KW - Feature extraction
KW - Patent big data analysis
KW - Patent litigation
UR - http://www.scopus.com/inward/record.url?scp=85115662822&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-1781-2_76
DO - 10.1007/978-981-16-1781-2_76
M3 - Conference contribution
AN - SCOPUS:85115662822
SN - 9789811617805
T3 - Lecture Notes in Networks and Systems
SP - 877
EP - 884
BT - Proceedings of Sixth International Congress on Information and Communication Technology - ICICT 2021
A2 - Yang, Xin-She
A2 - Sherratt, Simon
A2 - Dey, Nilanjan
A2 - Joshi, Amit
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Congress on Information and Communication Technology, ICICT 2021
Y2 - 25 February 2021 through 26 February 2021
ER -