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| Patent Valuation Method Based on a Combination of Feature Stitching, Label Migration, and Deep Learning |
| Zhao Xuefeng1, Hu Jinjin1, Wu Delin1, Wu Weiwei2, Sun Andong3, Zhao Tao4 |
1.School of Economics and Management, Harbin Institute of Technology, Shenzhen, Shenzhen 518000
2.School of Management, Harbin Institute of Technology, Harbin 150000
3.Shenzhen Ward Intellectual Property Agency, Shenzhen 518000
4.Shenzhen Yingfeng Intellectual Property Consulting Co., Ltd, Shenzhen 518000 |
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Abstract Patent value evaluation is of great practical significance in cracking down on abnormal applications and purifying the market environment. This study uses feature combination, label migration, and deep learning combination to construct a patent valuation method and explores actual performance based on patents in Guangdong Province from 2010 to 2020. Several sets of comparative models are introduced for experimental analysis. Our findings reveal the following conclusions. (1) Stitching together the information of bibliographic documents can construct more powerful patented research objects with technical characteristics, thereby overcoming the phenomenon that evaluation accuracy is not high owing to the insufficient reflection of the nature of patented technology. (2) We can quantify a more representative patent value from the patent law. While extending the research depth, the mismatch between traditional labels and the actual value of the patent is also resolved. (3) A patent value evaluation model with bidirectional encoder representations from transformers (BERT) and long short-term memory (LSTM) is established based on the construction principle of high-precision word vector, effectively solving the disadvantages of low evaluation accuracy caused by the lack of feature extraction ability of traditional models. This study has a strong application value and presents improvement strategies from the three aspects of research object effectiveness, label system, and model evaluation rate, providing a new tool for patent value evaluation.
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Received: 08 July 2022
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1 Wang J J, Ye F Y. Probing into the interactions between papers and patents of new CRISPR/CAS9 technology: a citation comparison[J]. Journal of Informetrics, 2021, 15(4): 101189.
2 Schwartz H M. Global secular stagnation and the rise of intellectual property monopoly[J]. Review of International Political Economy, 2022, 29(5): 1448-1476.
3 王格格, 刘树林. 国际专利分类号间的知识流动与技术间知识溢出测度——基于中国发明授权专利数据[J]. 情报学报, 2020, 39(11): 1162-1170.
4 Kuhn J M, Teodorescu M H M. The track one pilot program: who benefits from prioritized patent examination?[J]. Strategic Entrepreneurship Journal, 2021, 15(2): 185-208.
5 Feng J, Jaravel X. Crafting intellectual property rights: implications for patent assertion entities, litigation, and innovation[J]. American Economic Journal: Applied Economics, 2020, 12(1): 140-181.
6 刘夏, 黄灿, 余骁锋. 基于机器学习模型的专利质量预测初探[J]. 情报学报, 2019, 38(4): 402-410.
7 Ljungberg D, Bourelos E, McKelvey M. Academic inventors, technological profiles and patent value: an analysis of academic patents owned by swedish-based firms[J]. Industry and Innovation, 2013, 20(5): 473-487.
8 李睿, 赵峰. 届满专利与无效专利的施引特征对比及其情报学意义[J]. 情报学报, 2016, 35(6): 586-596.
9 Hou B J, Zhang Y M, Hong J, et al. New knowledge and regional entrepreneurship: the role of intellectual property protection in China[J]. Knowledge Management Research & Practice, 2021: 1-15.
10 杨思思, 戴磊, 郝屹. 专利经济价值度通用评估方法研究[J]. 情报学报, 2018, 37(1): 52-60.
11 Song X Y, Huang X H, Qing T. Intellectual property rights protection and quality upgrading: evidence from China[J]. Economic Modelling, 2021, 103: 105602.
12 Wu H C, Chen H Y, Lee K Y. Unveiling the core technology structure for companies through patent information[J]. Technological Forecasting and Social Change, 2010, 77(7): 1167-1178.
13 Lai K K, Chen H C, Chang Y H, et al. A structured MPA approach to explore technological core competence, knowledge flow, and technology development through social network patentometrics[J]. Journal of Knowledge Management, 2021, 25(2): 402-432.
14 Trappey A J C, Trappey C V, Wu J L, et al. Intelligent compilation of patent summaries using machine learning and natural language processing techniques[J]. Advanced Engineering Informatics, 2020, 43: 101027.
15 刘大勇, 孟悄然, 段文斌. 科技成果转化对经济新动能培育的影响机制——基于230个城市专利转化的观测与实证分析[J]. 管理科学学报, 2021, 24(7): 49-65.
16 Liu W D, Qiao W B, Wang Y, et al. Patent transformation opportunity to realize patent value: discussion about the conditions to be used or exchanged[J]. Information Processing & Management, 2021, 58(4): 102582.
17 李治东, 熊焰, 方曦. 基于熵权层次分析法的核心专利识别应用研究[J]. 情报学报, 2016, 35(10): 1101-1109.
18 Trappey A J C, Trappey C V, Govindarajan U H, et al. Patent value analysis using deep learning models—the case of IoT technology mining for the manufacturing industry[J]. IEEE Transactions on Engineering Management, 2021, 68(5): 1334-1346.
19 Wang J L, Fan Y, Zhang H, et al. Technology hotspot tracking: topic discovery and evolution of China’s blockchain patents based on a dynamic LDA model[J]. Symmetry, 2021, 13(3): 415.
20 Huang Z X, Xie Z P. A patent keywords extraction method using TextRank model with prior public knowledge[J]. Complex & Intelligent Systems, 2022, 8(1): 1-12.
21 Chung P, Sohn S Y. Early detection of valuable patents using a deep learning model: case of semiconductor industry[J]. Technological Forecasting and Social Change, 2020, 158: 120146.
22 Zhu H M, He C H, Fang Y, et al. Patent automatic classification based on symmetric hierarchical convolution neural network[J]. Symmetry, 2020, 12(2): 186.
23 Wu H Q, Shen G Q, Lin X, et al. A transformer-based deep learning model for recognizing communication-oriented entities from patents of ICT in construction[J]. Automation in Construction, 2021, 125: 103608.
24 Ni X, Samet A, Cavallucci D. Similarity-based approach for inventive design solutions assistance[J]. Journal of Intelligent Manufacturing, 2022, 33(6): 1681-1698.
25 李睿, 周维, 容军凤, 等. 高价值企业专利的被引特征分析——以世界500强企业专利为例[J]. 情报学报, 2015, 34(9): 899-911.
26 Nagler M, Sorg S. The disciplinary effect of post-grant review-causal evidence from European patent opposition[J]. Research Policy, 2020, 49(3): 103915.
27 Jeon D, Ahn J M, Kim J, et al. A doc2vec and local outlier factor approach to measuring the novelty of patents[J]. Technological Forecasting and Social Change, 2022, 174: 121294.
28 陈亮. 面向专利分析的Patent Classification LDA模型[J]. 情报学报, 2016, 35(8): 864-874.
29 专利审查指南[EB/OL]. [2022-07-01]. http://www.cypatent.com/cn/sczn.htm.
30 李雨峰. 论专利公开与排他利益的动态平衡[J]. 知识产权, 2019, 29(9): 3-10.
31 Yu L P, Duan Y L, Fan T T. Innovation performance of new products in China’s high-technology industry[J]. International Journal of Production Economics, 2020, 219: 204-215.
32 Di Gennaro G, Buonanno A, Palmieri F A N. Considerations about learning word2vec[J]. The Journal of Supercomputing, 2021, 77(11): 12320-12335.
33 王玲, 李文昌, 赵梦. 不同类型专利权人的专利失效影响因素研究[J]. 科技管理研究, 2021, 41(19): 149-154.
34 Marco A C, Sarnoff J D, de Grazia C A W. Patent claims and patent scope[J]. Research Policy, 2019, 48(9): 103790.
35 Mossinghoff G J, Kuo V S. Post-grant review of patents: enhancing the quality of the fuel of interest[J]. Idea, 2003, 43: 83.
36 Rai A K. Improving (software) patent quality through the administrative process[J]. Houston Law Review, 2013, 51(2): 503-543.
37 Novelli E. An examination of the antecedents and implications of patent scope[J]. Research Policy, 2015, 44(2): 493-507.
38 Kim Y K, Park S T. Patent litigation research trends and trend analysis[J]. Journal of Computational and Theoretical Nanoscience, 2021, 18(5): 1485-1489.
39 U.S. Patent and Trademark Office, U.S. Department of Commerce. Patent litigation and USPTO trials: implications for patent examination quality[R]. Alexandria: United States Patent and Trademark Office, 2015.
40 Sun F, Liu J, Wu J, et al. BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer[C]// Proceedings of the 28th ACM International Conference on Information and Knowledge Management. New York: ACM Press, 2019: 1441-1450.
41 Wan C X, Li B. Financial causal sentence recognition based on BERT-CNN text classification[J]. The Journal of Supercomputing, 2022, 78(5): 6503-6527.
42 Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates, 2017: 6000-6010.
43 Sherstinsky A. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network[J]. Physica D: Nonlinear Phenomena, 2020, 404: 132306.
44 Nguyen H D, Tran K P, Thomassey S, et al. Forecasting and anomaly detection approaches using LSTM and LSTM autoencoder techniques with the applications in supply chain management[J]. International Journal of Information Management, 2021, 57: 102282.
45 Patil A, Viquerat J, Larcher A, et al. Robust deep learning for emulating turbulent viscosities[J]. Physics of Fluids, 2021, 33(10): 105118.
46 Zaki G, Gudla P R, Lee K, et al. A deep learning pipeline for nucleus segmentation[J]. Cytometry Part A, 2020, 97(12): 1248-1264.
47 Wang X, Wang K, Lian S G. A survey on face data augmentation for the training of deep neural networks[J]. Neural Computing and Applications, 2020, 32(19): 15503-15531.
48 Moon T, Son J E. Knowledge transfer for adapting pre-trained deep neural models to predict different greenhouse environments based on a low quantity of data[J]. Computers and Electronics in Agriculture, 2021, 185: 106136.
49 Veugelers R, Wang J. Scientific novelty and technological impact[J]. Research Policy, 2019, 48(6): 1362-1372.
50 国家知识产权局. 国内专利授权年度状况(2019年)[R/OL]// 2019知识产权统计年报. [2022-07-01]. https://www.cnipa.gov.cn/tjxx/jianbao/year2019/b/b2.html.
51 Zimmer L, Lindauer M, Hutter F. Auto-pytorch: multi-fidelity MetaLearning for efficient and robust AutoDL[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(9): 3079-3090.
52 Paszke A, Gross S, Massa F, et al. PyTorch: an imperative style, high-performance deep learning library[C]// Proceedings of the 32nd Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2020: 7994-8005. |
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