|
|
|
| Review of Patent Mining Methods in Technology Opportunity Discovery |
| Wei Tingting, Feng Danyu, Song Shiling, Zhang Jiantao |
| College of Mathematical Sciences and Information, South China Agricultural University, Guangzhou 510642 |
|
|
|
|
Abstract In the context of the ongoing technological revolution, research on technology opportunity discovery has received widespread attention. Technology opportunity discovery aims to discover new technology trends and infer possible technology forms and development points in the field; this is critical for technological innovation and industrial development. This paper presents a systematic review of the current situation of patent mining methods in technology opportunity discovery, summarizes the representative research of five underlying common analysis methods, clarifies the relationship between the analysis methods and research content, and provides a reference basis for the technology selection of subsequent research and practices in this field. It is shown that the methodological tools used for technology opportunity discovery have not yet been targeted for innovations in method application, despite their following of deep learning developments. Finally, ideas for improvement are proposed from three perspectives: the data, method application, and evaluation system.
|
|
Received: 17 September 2022
|
|
|
|
1 Song K, Kim K S, Lee S. Discovering new technology opportunities based on patents: text-mining and F-term analysis[J]. Technovation, 2017, 60/61: 1-14.
2 Ma T T, Zhou X, Liu J, et al. Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies[J]. Technological Forecasting and Social Change, 2021, 173: 121159.
3 任海英, 王倩. 技术机会发现方法的研究现状、趋势和问题[J]. 情报杂志, 2020, 39(4): 51-59.
4 伊惠芳, 刘细文, 龙艺璇. 技术创新全视角下技术机会发现研究进展[J]. 图书情报工作, 2021, 65(7): 132-142.
5 苏娜平, 谭宗颖. 技术机会分析方法研究综述与展望[J]. 情报理论与实践, 2020, 43(11): 179-186.
6 Lee C Y. A review of data analytics in technological forecasting[J]. Technological Forecasting and Social Change, 2021, 166: 120646.
7 李慧, 孟玮. 专利视角下的美国空军核心技术演化分析[J]. 情报理论与实践, 2021, 44(2): 41-49.
8 Yoon B, Park Y. A text-mining-based patent network: analytical tool for high-technology trend[J]. The Journal of High Technology Management Research, 2004, 15(1): 37-50.
9 Kim M, Park Y, Yoon J. Generating patent development maps for technology monitoring using semantic patent-topic analysis[J]. Computers & Industrial Engineering, 2016, 98: 289-299.
10 吴菲菲, 陈肖微, 黄鲁成, 等. 基于语义相似度的技术多主题演化路径识别方法研究[J]. 情报杂志, 2018, 37(5): 91-96.
11 Lee C Y, Jeon D, Ahn J M, et al. Navigating a product landscape for technology opportunity analysis: a word2vec approach using an integrated patent-product database[J]. Technovation, 2020, 96/97: 102140.
12 饶齐, 王裴岩, 张桂平. 面向中文专利SAO结构抽取的文本特征比较研究[J]. 北京大学学报(自然科学版), 2015, 51(2): 349-356.
13 Hu J, Li S B, Yao Y, et al. Patent keyword extraction algorithm based on distributed representation for patent classification[J]. Entropy, 2018, 20(2): 104.
14 Kim T S, Sohn S Y. Machine-learning-based deep semantic analysis approach for forecasting new technology convergence[J]. Technological Forecasting and Social Change, 2020, 157: 120095.
15 Korobkin D, Fomenkov S, Kravets A, et al. Methods of statistical and semantic patent analysis[C]// Proceedings of the Conference on Creativity in Intelligent Technologies and Data Science. Cham: Springer, 2017: 48-61.
16 Chen L, Xu S, Zhu L J, et al. A deep learning based method for extracting semantic information from patent documents[J]. Scientometrics, 2020, 125(1): 289-312.
17 王秀红, 高敏. 基于BERT-LDA的关键技术识别方法及其实证研究——以农业机器人为例[J]. 图书情报工作, 2021, 65(22): 114-125.
18 赵京胜, 宋梦雪, 高祥, 等. 自然语言处理中的文本表示研究[J]. 软件学报, 2022, 33(1): 102-128.
19 Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space[OL]. (2013-09-07). https://arxiv.org/pdf/1301.3781.pdf.
20 Le Q, Mikolov T. Distributed representations of sentences and documents[C]// Proceedings of the 31st International Conference on International Conference on Machine Learning. JMLR.org, 2014: II-1188-II-1196.
21 Pennington J, Socher R, Manning C. GloVe: global vectors for word representation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2014: 1532-1543.
22 Devlin J, Chang M W, Lee K, et al. Bert: pre-training of deep bidirectional transformers for language understanding[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2018: 4171-4186.
23 Zhu C, Motohashi K. Identifying the technology convergence using patent text information: a graph convolutional networks (GCN)-based approach[J]. Technological Forecasting and Social Change, 2022, 176: 121477.
24 Kim S, Park I, Yoon B. SAO2vec: development of an algorithm for embedding the subject-action-object (SAO) structure using doc2vec[J]. PLoS One, 2020, 15(2): e0227930.
25 Krestel R, Chikkamath R, Hewel C, et al. A survey on deep learning for patent analysis[J]. World Patent Information, 2021, 65: 102035.
26 林原, 张乐, 丁堃, 等. 融合专利表示的技术路线图构建研究[J]. 情报杂志, 2021, 40(10): 15-20.
27 马建红, 姬帅, 刘硕. 面向专利的主题短语提取[J]. 计算机工程与设计, 2019, 40(5): 1365-1369, 1382.
28 Kim K, Park K, Lee S. Investigating technology opportunities: the use of SAOx analysis[J]. Scientometrics, 2019, 118(1): 45-70.
29 陈泽龙. 基于句法表征的专利文本相似性评估[D]. 北京: 北京交通大学, 2019: 14-18.
30 Arts S, Cassiman B, Gomez J C. Text matching to measure patent similarity[J]. Strategic Management Journal, 2018, 39(1): 62-84.
31 刘俊婉, 龙志昕, 王菲菲. 基于LDA主题模型与链路预测的新兴主题关联机会发现研究[J]. 数据分析与知识发现, 2019, 3(1): 104-117.
32 席笑文, 郭颖, 宋欣娜, 等. 基于word2vec与LDA主题模型的技术相似性可视化研究[J]. 情报学报, 2021, 40(9): 974-983.
33 Zhang H, Daim T, Zhang Y Q. Integrating patent analysis into technology roadmapping: a latent Dirichlet allocation based technology assessment and roadmapping in the field of Blockchain[J]. Technological Forecasting and Social Change, 2021, 167: 120729.
34 马建红, 张少光, 曹文斌, 等. 面向功能信息的相似专利动态聚类混合模型[J]. 计算机应用与软件, 2021, 38(5): 201-207.
35 Mao G Z, Han Y X, Liu X, et al. Technology status and trends of industrial wastewater treatment: a patent analysis[J]. Chemosphere, 2022, 288(Part 2): 132483.
36 Shanie T, Suprijadi J, Zulhanif. Text grouping in patent analysis using adaptive k-means clustering algorithm[J]. AIP Conference Proceedings, 2017, 1827(1): 020041.
37 田鹏伟, 张娴. 基于异构信息网络融合的专利技术主题识别研究[J]. 情报杂志, 2021, 40(8): 45-52.
38 Zhang J Z, Yu W Q. Early detection of technology opportunity based on analogy design and phrase semantic representation[J]. Scientometrics, 2020, 125(1): 551-576.
39 Anuranjana, Mittas N, Mehrotra D. Clustering the patent data using k-means approach[C]// Proceedings of the Conference on Software Engineering. Singapore: Springer, 2019: 639-645.
40 Zhou Y, Lin H, Liu Y F, et al. A novel method to identify emerging technologies using a semi-supervised topic clustering model: a case of 3D printing industry[J]. Scientometrics, 2019, 120(1): 167-185.
41 Ren H Y, Zhao Y H. Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks[J]. Technovation, 2021, 101: 102196.
42 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.
43 Wang H C, Chi Y C, Hsin P L. Constructing patent maps using text mining to sustainably detect potential technological opportunities[J]. Sustainability, 2018, 10(10): 3729.
44 Kim D H, Lee H, Kwak J. Standards as a driving force that influences emerging technological trajectories in the converging world of the Internet and things: an investigation of the M2M/IoT patent network[J]. Research Policy, 2017, 46(7): 1234-1254.
45 Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
46 吕璐成, 周健, 王学昭, 等. 基于双层主题模型的技术演化分析框架及其应用[J]. 数据分析与知识发现, 2022, 6(2): 18-32.
47 陈浩, 张梦毅, 程秀峰. 融合主题模型与决策树的跨地区专利合作关系发现与推荐——以广东省和武汉市高校专利库为例[J]. 数据分析与知识发现, 2021, 5(10): 37-50.
48 Yun J, Geum Y. Automated classification of patents: a topic modeling approach[J]. Computers & Industrial Engineering, 2020, 147: 106636.
49 Kim J, Geum Y. How to develop data-driven technology roadmaps: the integration of topic modeling and link prediction[J]. Technological Forecasting and Social Change, 2021, 171: 120972.
50 Choi D, Song B. Exploring technological trends in logistics: topic modeling-based patent analysis[J]. Sustainability, 2018, 10(8): 2810.
51 韩芳, 张生太, 冯凌子, 等. 基于专利文献技术融合测度的突破性创新主题识别——以太阳能光伏领域为例[J]. 数据分析与知识发现, 2021, 5(12): 137-147.
52 张金柱, 王玥, 胡一鸣. 基于专利科学引文内容表示学习的科学技术主题关联分析研究[J]. 数据分析与知识发现, 2019, 3(12): 52-60.
53 刘小玲, 谭宗颖. 基于专利多属性融合的技术主题划分方法研究[J]. 数据分析与知识发现, 2022, 6(2): 45-54.
54 Yoon B, Magee C L. Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction[J]. Technological Forecasting and Social Change, 2018, 132: 105-117.
55 Seo W. A patent-based approach to identifying potential technology opportunities realizable from a firm’s internal capabilities[J]. Computers & Industrial Engineering, 2022, 171: 108395.
56 Han X T, Zhu D H, Wang X F, et al. Technology opportunity analysis: combining SAO networks and link prediction[J]. IEEE Transactions on Engineering Management, 2021, 68(5): 1288-1298.
57 Cho J H, Lee J, Sohn S Y. Predicting future technological convergence patterns based on machine learning using link prediction[J]. Scientometrics, 2021, 126(7): 5413-5429.
58 Park I, Yoon B. Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network[J]. Journal of Informetrics, 2018, 12(4): 1199-1222.
59 翟东升, 刘鹤, 张杰, 等. 一种基于链路预测的技术机会挖掘方法[J]. 情报学报, 2016, 35(10): 1090-1100.
60 Aaldering L J, Leker J, Song C H. Competition or collaboration? - Analysis of technological knowledge ecosystem within the field of alternative powertrain systems: a patent-based approach[J]. Journal of Cleaner Production, 2019, 212: 362-371.
61 Qi Y, Zhang X, Hu Z Y, et al. Choosing the right collaboration partner for innovation: a framework based on topic analysis and link prediction[J]. Scientometrics, 2022, 127(9): 5519-5550.
62 Hong S, Lee C Y. Effective indexes and classification algorithms for supervised link prediction approach to anticipating technology convergence: a comparative study[J]. IEEE Transactions on Engineering Management, 2023, 70(4): 1430-1441.
63 Nakai K, Nonaka H, Hentona A, et al. Community detection and growth potential prediction using the stochastic block model and the long short-term memory from patent citation networks[C]// Proceedings of the 2018 IEEE International Conference on Industrial Engineering and Engineering Management. Piscataway: IEEE, 2018: 1884-1888.
64 Chen G F, Xu C, Wang J Y, et al. Nonnegative matrix factorization for link prediction in directed complex networks using PageRank and asymmetric link clustering information[J]. Expert Systems with Applications, 2020, 148: 113290.
65 Han F, Magee C L. Testing the science/technology relationship by analysis of patent citations of scientific papers after decomposition of both science and technology[J]. Scientometrics, 2018, 116(2): 767-796.
66 Geum Y, Kim M. How to identify promising chances for technological innovation: keygraph-based patent analysis[J]. Advanced Engineering Informatics, 2020, 46: 101155. |
|
|
|
