A Study on the Development of a Core Patent Classification Model
Using Improved Patent Performance Indicators

Kang, Jiho; Kim, Youngho; Lee, Junseok; Park, Sangsung

A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators

Youngho KIM, (Machine Learning Big Data Institute, Korea University, Seoul, Korea)

Sangsung PARK, (Department of Big Data Statistics, Cheongju University, Chungbuk, Korea)

Junseok LEE, (MICUBE Solution, Seoul, Korea)

Jiho KANG (Machine Learning Big Data Institute, Korea University, Seoul, Korea)

International Journal of Intelligent Systems and Applications in Engineering

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Yıl: 2022 Cilt: 10 Sayı: 1 Sayfa Aralığı: 1 - 8 Metin Dili: İngilizce İndeks Tarihi: 12-06-2022

A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators

Öz:

A patent contains various information about a developed technology and is a form of Big data that receives millions of applications worldwide each year. Recently, there has been an increase in research that analyzes such patent Big data for use in R&D strategy establishment. Among these studies, a core patent classification is recognized as important because it can be used for a variety of management information. In the past, the core patent classification was performed qualitatively by some experts, but it was expensive and time consuming. To complement qualitative methods, quantitative methods using statistics and machine learning are being studied. Existing proposed methods utilize the quantitative indicators specified in the patent. However, quantitative indicators have different values for each elementary technology. If this characteristic is not reflected, an incorrect analysis result is produced. In addition, various values such as rights, technology scalability sustainable development, etc., must be considered in order to effectively classify core patents. In this paper, we propose an effective core patent classification model using improved patent performance indicators. The proposed model applies text mining and clustering to patent Big data to identify elementary technology and calculate improved patent performance indicators that reflect various values. Furthermore, a core patent classification model is constructed by learning various classification algorithms. In order to examine the practical applicability of the proposed model, experiments are conducted with patents registered in the USPTO. As a result of the experiment, the accuracy of three models trained with patent-improved performance indicators was high. Among them, k-nearest neighbors demonstrated the highest performance.

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[1] K. Witkowski, “Internet of things, big data, industry 4.0 – innovative solutions in logistics and supply chains management,” Procedia Eng., vol. 182, pp. 763–769, 2017.
[2] World Intellectual Property Organization, “World intellectual property indicators 2019,” WIPO., vol. 941E, no. 19, pp. 1–224, 2019.
[3] L. Zhang, L. Li and T. Li, “Patent mining: a survey,” SIGKDD Explor. Newsl., vol. 6, no. 2, pp. 1–19, 2015.
[4] H. Kim, “A novel methodology for extracting core technology and patent by ip mining,” M.S. thesis, Dept. Ind. Manag. Eng., Korea Univ., SU, Korea, 2016.
[5] J. Lee, J. LEE, G. Kim, S. Park and D. Jang, “Establishment of strategy for management of technology using data mining technique,” J. Korean Inst. Intell. Syst., vol. 25, no. 2, pp. 126–132, 2015.
[6] J. Wu, P. Chang, C. Tsao and C. Fan, “A patent quality analysis and classification system using self-organizing maps with support vector machie,” Appl. Soft Comput., vol. 41, pp. 305–316, 2016.
[7] H. Woo, J. Kwak and C. Lim, “A study on patent evaluation model based on bayesian approach of the structural equation modl,” Korean J. Appl. Statist., vol. 30, no. 6, pp. 901–916, 2017.
[8] C. Wang, C. Chiang and S. Lin, “Network structure of innovation: can brokerage or closure predict patent quality?,” Scientometrics, vol. 84, pp. 735–748, 2010.
[9] H. Wu, H. Chen and K. Lee, “Unveiling the core technology structure for companies through patent information,” Technol. Forecasting Soc. Change, vol. 77, no. 7, pp. 1167–1178, 2010.
[10] A. J. C. Trappey, C. V. Trappey, C. Wu and C. Lin, “A patent analysis for innovative technology and product development,” Adv. Eng. Inform., vol. 26, no. 1, pp. 26–34, 2012.
[11] T. Cho and H. Shih, “Patent citation network analysis of core and emerging technologies in Taiwan: 1997-2008,” Scientometrics, vol. 89, pp. 795, 2011.
[12] S. Chang, “Key technologies and development trends of 5g optical networks,” Appl. Sci., vol. 9, no. 22, 2019.
[13] M. Lee and W. Su, “Search for the developing trends by patent analysis: a case study of lithum-ion battery electrolytes,” Appl. Sci., vol. 10, no. 3, 2020.
[14] S. Chang, “Patent analysis of the critical technology network of semiconductor optical amplifiers,” Appl. Sci., vol. 10, no. 4, 2020.
[15] R&D Intellectual Property Information System, SU, Korea. Patent analysis methodology for making of technology roadmap. [Online]. Available: http://www.ripis.or.kr/U_Pds.do?method=m011&ntcbd_mng_seq= 12&wrt_seq=336
[16] R&D Intellectual Property Information System, SU, Korea. Guidelines for utilizing patent performance indicators. [Online]. Available: http://rndip.or.kr/U_Pds.do?method=m011&ntcbd_mng_seq=12& wrt_seq=345
[17] R. Lu, H. Zhu, X. Liu, J. K. Liu and J. Shao, “Toward efficient and privacy-preserving computing in big data era,” IEEE Netw., vol. 28,no. 4, pp. 46–50, 2014.
[18] W. S. Lee, E. J. Han and S. Y. Sohn, “Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents,” Technol. Forecasting Soc. Change, vol. 100, pp. 317–329, 2015.
[19] W. Seo, N. Kim and S. Choi, “Big data framework for analysing patents to support strategic r&d planning,” presented at DASC. PiCom. DataCom. CyberSciTech., Auckland, New Zealand, Aug. 8- 12, 2016.
[20] A. Segev, C. Jung and S. Jung, “Analysis of technology trends based on big data,” presented at 2013 BIGDATA-CONGRESS, Santa Clara, USA, Jun. 27-Jul. 2, 2013.
[21] A. H. Khoury and R. Bekkerman, “Automatic discovery of prior art: big data to the rescue of the patent system,” J. Marshall Rev. Intell. Prop. L., vol. 16, pp. 45–65, 2016.
[22] P. Qu, J. Zhang, C. Yao and W. Zeng, “Identifying long tail term from large-scale candidate pairs for big data-oriented patent analysis,” Concurr. Comp-Pract. E., vol. 28, pp. 4194–4208, 2016.
[23] A. Pilkington, R. Dyerson and O. Tissier, “The electric vehicle:: patent data as indicators of technological development,” World Pat. Inf., vol. 24, no. 1, pp. 5–12, 2002.
[24] J. Shlens, “A tutorial on principal component analysis,” arXiv, preprint arXiv:1404.1100, 2014.
[25] Y. Chao and C. Wu, “Principal component-based weighted indices and a framework to evaluate indices: results from the medical expenditure panel survey 1996 to 2011,” PLoS One, vol. 12, no. 9, e0183997, 2017.
[26] T. Mikolov, K. Chen, G. Corrado and J. Dean, “Efficient estimation of word representations in vector space,” arXiv, preprint arXiv:1301.3781, 2013.
[27] X. Rong, “Word2vec parameter learning explained,” arXiv, preprint arXiv:1411.2738, 2016.
[28] Q. V. Le and T. Mikolov, “Distributed representations of sentences and documents,” arXiv, preprint arXiv:1405.4053, 2014.
[29] A. Sharma, “A survey on different text clustering techniques for patent analysis,” IJERT., vol. 1, no. 9, pp. 1–4, 2012.
[30] J. E. Speich and J. Rosen, “Medical robotics,” in Encyclopedia of Biomaterials and Biomedical Engineering, G. Wnek, G. Bowlin, Ed. Boca Raton, FL, USA: CRC Press, 2008, pp. 983–993.
[31] H. F. M. Van der Loos, D. J. Reinkensmeyer and E. Guglielmelli, “Rehabilitation and Health Care Robotics,” in Springer Handbook of Robotics, B. Siciliano, O. Khatib, Ed. Cham, ZG, Switzerland: Springer, 2016, pp. 1685–1728.

APA	Kim Y, Park S, Lee J, Kang J (2022). A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators. , 1 - 8.
Chicago	Kim Youngho,Park Sangsung,Lee Junseok,Kang Jiho A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators. (2022): 1 - 8.
MLA	Kim Youngho,Park Sangsung,Lee Junseok,Kang Jiho A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators. , 2022, ss.1 - 8.
AMA	Kim Y,Park S,Lee J,Kang J A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators. . 2022; 1 - 8.
Vancouver	Kim Y,Park S,Lee J,Kang J A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators. . 2022; 1 - 8.
IEEE	Kim Y,Park S,Lee J,Kang J "A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators." , ss.1 - 8, 2022.
ISNAD	Kim, Youngho vd. "A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators". (2022), 1-8.

APA	Kim Y, Park S, Lee J, Kang J (2022). A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 1 - 8.
Chicago	Kim Youngho,Park Sangsung,Lee Junseok,Kang Jiho A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators. International Journal of Intelligent Systems and Applications in Engineering 10, no.1 (2022): 1 - 8.
MLA	Kim Youngho,Park Sangsung,Lee Junseok,Kang Jiho A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators. International Journal of Intelligent Systems and Applications in Engineering, vol.10, no.1, 2022, ss.1 - 8.
AMA	Kim Y,Park S,Lee J,Kang J A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators. International Journal of Intelligent Systems and Applications in Engineering. 2022; 10(1): 1 - 8.
Vancouver	Kim Y,Park S,Lee J,Kang J A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators. International Journal of Intelligent Systems and Applications in Engineering. 2022; 10(1): 1 - 8.
IEEE	Kim Y,Park S,Lee J,Kang J "A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators." International Journal of Intelligent Systems and Applications in Engineering, 10, ss.1 - 8, 2022.
ISNAD	Kim, Youngho vd. "A Study on the Development of a Core Patent Classification Model Using Improved Patent Performance Indicators". International Journal of Intelligent Systems and Applications in Engineering 10/1 (2022), 1-8.