on Empirical Methods in Natural Language Processing, pages 1203-1213, Austin, Texas, November 2016. Association for Computational Linguistics. doi: 10.18653/v1/D16-1128. URL https://aclanthology.org/D16-1128.
[11] Ralf Krestel, Renukswamy Chikkamath, Christoph Hewel, and Julian Risch. A Survey on Deep Learning for Patent Analysis. World Patent Information, 65:102035, 2021.
[12] Leah S Larkey. A Patent Search and Classification System. In Proceedings of the Fourth ACM Conference on Digital libraries, pages 179-187, 1999.
[13] Xiao-Lei Chu, Chao Ma, Jing Li, Bao-Liang Lu, Masao Utiyama, and Hitoshi Isahara. LargeScale Patent Classification with Min-Max Modular Support Vector Machines. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pages 3973-3980. IEEE, 2008.
[14] F. Derieux, M. Bobeica, Delphine Pois, and Jean-Pierre Raysz. Combining Semantics and Statistics for Patent Classification. In CLEF, 2010.
[15] Tung Tran and Ramakanth Kavuluru. Supervised Approaches to Assign Cooperative Patent Classification (CPC) Codes to Patents. In International Conference on Mining Intelligence and Knowledge Exploration, pages 22-34. Springer, 2017.
[16] J. C. Gómez. Analysis of the Effect of Data Properties in Automated Patent Classification. Scientometrics, 121:1239 - 1268, 2019.
[17] Mattyws F Grawe, Claudia A Martins, and Andreia G Bonfante. Automated Patent Classification Using Word Embedding. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pages 408-411. IEEE, 2017.
[18] Huiming Zhu, Chunhui He, Yang Fang, Bin Ge, Meng Xing, and Weidong Xiao. Patent Automatic Classification Based on Symmetric Hierarchical Convolution Neural Network. Symmetry, 12(2):186, 2020.
[19] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is All You Need. In Proceedings of the 31st International Conference on Neural Information Processing Systems, pages 6000–6010, 2017.
[20] Jieh-Sheng Lee and J. Hsiang. Patent Classification by Fine-Tuning BERT Language Model. World Patent Information, 61:101965, 2020.
[21] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics. doi: 10.18653/v1/N19-1423. URL https://www.aclweb.org/anthology/N19-1423.
[22] Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontonon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al. Big Bird: Transformers for Longer Sequences. arXiv preprint arXiv:2007.14062, 2020.
[23] Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692, 2019.
[24] Utku Acikalin, Tolga Caskurlu, Gerard Hoberg, and Gordon M. Phillips. Intellectual Property Protection Lost: The Impact on Competition and Acquisitions. SSRN Electronic Journal, 2022. ISSN 1556-5068. doi: 10.2139/ssrn.4023622. URL https://www.ssrn.com/abstract=4023622.
[25] USPTO. U.S. Patent Statistics Chart Calendar Years 1963 - 2020, 2021. URL https://www.uspto.gov/web/offices/ac/ido/oeip/taf/us_stat.htm.