Privacy concerns at the individual and public or private organizational levels are a crucial. Its importance is highly evident nowadays, with the development of advanced technology. This study proposes a system for text mining that analyzes characteristics related to language. This factor makes it possible to generate a fictitious system while analyzing the patent within a bird’s-eye view and presenting keywords to support an idea. By mapping each patent’s information and relationship to an n-dimensional space, one can search for similar patents employing cosine similarity. Quantitative and qualitative evaluation verified the usefulness of the system.

1. [1] A. Fujii, H. Tangawa, M. Iwayama, H. Namba, M. Yamamoto, and M. Ushiyama, “Patent Information Processing: A natural language processing approache,” Corona Publishing Co., 2012 (in Japanese).
2. [2] J. Han, M. Kamber, and J. Pei, “Getting to Know Your Data,” pp. 39-82, Morgan Kaufmann, 2012.
3. [3] TRIZ, Wikipedia webpage, https://en.wikipedia.org/wiki/TRIZ [accessed May 20, 2021]
4. [4] J. Itou, T. Higashi, and J. Munemori, “Proposal and Application of Idea Generation Support System Providing Words of Low Co-occurrence Degree,” J. of Information Processing, Vol.56, No.6, pp. 1528-1540, 2015 (in Japanese).
5. [5] H. Inoue, “Analyses of Compound Structures of Groups that Produce Intellectual Property,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.2, pp. 180-187, doi: 10.20965/jaciii.2011.p0180, 2011.
6. [6] P. K. Yu, “Challenges to the Development of a Human Rights Framework for Intellectual Property,” P. L. C. Torremans (Ed.), Intellectual Property Law and Human Rights, 4th edition, pp. 89-115, Kluwer Law International, 2020.
7. [7] Y. Nishihara, J. Hibino, J. Fukumoto, and R. Yamanishi, “An Idea Generation Support System Evaluating Function’s Novelty in Product Combination,” J. of Japan Society for Fuzzy Theory and Intelligent Informatics, Vol.27, No.4, pp. 669-679, 2015 (in Japanese).
8. [8] J. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” arXiv preprint, arXiv:1810.04805, 2019.
9. [9] H. Takamura, “Introduction to Machine Learning for Natural Language Processing,” Corona Publishing Co., Tokyo, 2010 (in Japanese).
10. [10] I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to Sequence Learning with Neural Networks,” Advances in Neural Information Processing Systems 27, 28th Conf. on Neural Information Processing Systems (NIPS 2014), 2014.
11. [11] D. P. Kingma and M. Welling, “Auto-Encoding Variational Bayes,” arXiv preprint, arXiv:1312.6114, 2014.
12. [12] “Automatic extraction of technical terms (keyword) python module termextract,” The University of Tokyo, February 24, 2018, http://gensen.dl.itc.u-tokyo.ac.jp/pytermextract/ (in Japanese) [accessed October 25, 2019]
13. [13] S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Jozefowicz, and S. Bengio, “Generating Sentences from a Continuous Space,” Proc. of The 20th SIGNLL Conf. on Computational Natural Language Learning, K16-1002, 2016.
14. [14] Z. Yan and Y. Wu, “A Neural N-Gram Network for Text Classification,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.3, pp. 380-386, doi: 10.20965/jaciii.2018.p0380, 2018.
15. [15] H. Takase, H. Kawanaka, and S. Tsuruoka, “Supporting System for Quiz in Large Class – Automatic Keyword Extraction and Browsing Interface,” J. Adv. Comput. Intell. Intell. Inform., Vol.19, No.1, pp. 152-157, doi: 10.20965/jaciii.2015.p0152, 2015.
16. [16] J. Song, S. Oyama, and M. Kurihara, “Exploratory Causal Analysis of Open Data: Explanation Generation and Confounder Identification,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.1, pp. 142-155, doi: 10.20965/jaciii.2020.p0142, 2020.
17. [17] Y. Tsutsui and M. Hagiwara, “Analog Value Associative Memory Using Restricted Boltzmann Machine,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.1, pp. 60-66, doi: 10.20965/jaciii.2019.p0060, 2019.
18. [18] J. Ji, R.-C. Chen, and Q. Zhao, “Document Analysis System Based on Awareness Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.9, pp. 1230-1240, doi: 10.20965/jaciii.2011.p1230, 2011.
19. [19] K. Kumagai, I. Kobayashi, D. Mochihashi, H. Asoh, T. Nakamura, and T. Nagai, “Natural Language Generation Using Monte Carlo Tree Search,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.5, pp. 777-785, doi: 10.20965/jaciii.2018.p0777, 2018.
20. [20] J. Hu, Y. Zhang, Y. Wang, and Y.-W. Chiu, “Influencing Mechanism of Patent Alliance on Technical Innovation from Network Effects Perspective,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.4, pp. 678-685, doi: 10.20965/jaciii.2019.p0678, 2019.
21. [21] S. Sumathipala, K. Yamada, M. Unehara, and I. Suzuki, “Protein Entity Name Recognition Using Orthographic, Morphological and Proteinhood Features,” J. Adv. Comput. Intell. Intell. Inform., Vol.19, No.6, pp. 843-851, doi: 10.20965/jaciii.2015.p0843, 2015.
22. [22] H. Hassani, C. Beneki, S. Unger, M. T. Mazinani, and M. R. Yeganegi, “Text Mining in Big Data Analytics,” Big Data and Cognitive Computing, Vol.4, No.1, 2020.
23. [23] M. Alohaly, H. Takabi, and E. Blanco, “Automated Extraction of Attributes from Natural Language Attribute-based Access Control (ABAC) Policies,” Cybersecurity, Vol.2, Article No.2, 2019.