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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (12): 137-147    DOI: 10.11925/infotech.2096-3467.2021.0240
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Identifying Breakthrough Patent Topics by Measuring Technological Convergence——Case Study of Solar PV Domain
Han Fang1,Zhang Shengtai2,Feng Lingzi1,Yuan Junpeng1()
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
2School of Economics and Management, Beijing University of Posts and Telecommunications,Beijing 100876, China
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Abstract  

[Objective] This paper aims to identify the breakthrough topics from the core patents. [Methods] First, we retrieved the core patents from the Innography platform. Then, we identified the breakthrough innovative topics based on the measurement of the core patents’ Rao-Stirling diversity indices as well as the LDA text mining method. Finally, we conducted an empirical study to examine the proposed method with patents from the solar PV domain. [Results] We found that the core patents were mainly related to the disciplines of optics, electricity, and architecture, etc. We also identified 12 breakthrough innovative topics related to photoelectric conversion material, photovoltaic application, and thermoelectric power system. [Limitations] More research is needed to explore the measurement of technological convergence using different patent classification methods. [Conclusions] The proposed method can effectively discover the breakthrough topics from a certain domain of patents.

Key wordsCore Patent      Technological Convergence Measurement      Breakthrough Innovation      Solar PV      LDA     
Received: 10 March 2021      Published: 20 January 2022
ZTFLH:  G350  
Fund:Young Talents Research Frontier Fund, National Science Library,Chinese Academy of Sciences(E0291303)
Corresponding Authors: Yuan Junpeng,ORCID:0000-0003-2803-5312     E-mail: yuanjp@mail.las.ac.cn

Cite this article:

Han Fang, Zhang Shengtai, Feng Lingzi, Yuan Junpeng. Identifying Breakthrough Patent Topics by Measuring Technological Convergence——Case Study of Solar PV Domain. Data Analysis and Knowledge Discovery, 2021, 5(12): 137-147.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0240     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I12/137

The Research Framework
IPC 含义 专利数量
H01L 半导体器件;其他类目中不包括的电固体器件 1 954
H02N 其他类目不包含的电机 121
E04D 屋面覆盖层;天窗;檐槽;屋面施工工具 121
F24J 不包含在其他类目中的热量产生和利用 118
H01G 电容器;电解型的电容器、整流器、检波器、开关器件、光敏器件或热敏器件 76
H01M 用于直接转变化学能为电能的方法或装置,例如电池组 68
B32B 层状产品,即由扁平的或非扁平的薄层,例如泡沫状的、蜂窝状的薄层构成的产品 62
G02B 光学元件、系统或仪器 58
H01B 电缆;导体;绝缘体;导电、绝缘或介电材料的选择 56
C23C 对金属材料的镀覆;用金属材料对材料的镀覆;表面扩散法,化学转化或置换法的金属材料表面处理;真空蒸发法、溅射法、离子注入法或化学气相沉积法的一般镀覆 44
H02J 供电或配电的电路装置或系统;电能存储系统 38
B05D 对表面涂布液体或其他流体的一般工艺 37
F25B 制冷机,制冷设备或系统;加热和制冷的联合系统;热泵系统 28
H01J 放电管或放电灯 26
E04B 一般建筑物构造;墙,例如,间壁墙;屋顶;楼板;顶棚;建筑物的隔绝或其他防护 25
Distribution of Technology Categories of Core Patents in Solar PV (Part)
Interdisciplinary Map of Core Patents in the Domain of Solar PV
专利号 标题 Rao-Stirling
US9653627 Trench process and structure for backside contact solar cells with polysilicon doped regions 0.24
US8859888 Photovoltaic module and module arrays 0.24
US20120090685 Multifunctional static or semi-static photovoltaic skylight and/or methods of making the same 0.23
US20060289055 System and method for mounting photovoltaic modules 0.22
US20070079866 Edge reflector or refractor for bifacial solar module 0.22
US20070193618 Method of making solar cell/module with porous silica antireflective coating 0.22
US20070193621 System and method for making an improved thin film solar cell interconnect 0.22
US20070199588 Photovoltaic fibers 0.22
US7622666 Photovoltaic cell cover 0.22
US6858462 Charging control system and device 0
The Diversity Indices of Core Patents in Solar PV (Part)
The Rao-Stirling Indices of Core Patents in Solar PV
The Coherence Curve
主题ID 主题概括 关键词
1 管式太阳能集热器 阵列|产生器|管状|发射器|区域|热能|燃烧|延伸|间隙|催化剂
2 平板式太阳能集热器 吸热板|结构|支持|平面|电流|面板|模块化|阵列|光学|外壳
3 碳纳米管太阳能电池 纳米|系统|光学|量子|势垒|不同|结构|碳|结构|电池
4 太阳能高压电力传输技术 电压|电路|路径|区域|电子|接收器|化学|转换|传输|技术
5 光伏屋顶构造 模块|面板|侧|单元|屋面|住房|框架|安装|组件|顶部
6 三维薄膜太阳能电池基板 组件|基|涂层|电池|薄膜|成员|三维|熔融|信号|部分
7 非晶硅薄膜沉积 转换|硅|薄|光电|晶体|非晶型|成员|沉积|相|光能
8 纳米结构太阳能电池 层|元素|纳米结构|模板|排列|玻璃|前驱体|转移|部分|电线
9 热电系统 热电|动能|水|电极|蓄热|塔式|源|输出|电流|接收器
10 柔性纳米太阳能电池 柔性胶|粘剂|互连|基板|瓦片|载体|刚性|纳米|颗粒剂|电池
11 透明导电氧化层薄膜 电极|氧化物|前端|示例|蚀刻|薄膜|透明导电氧化层|锡|支撑|确定
12 有机化合物太阳能电池 有机物|组成|活性|组|粒子|转换器|尺寸|吸收体|晶体|化合物
The Breakthrough Innovative Technological Topics in Solar PV
[1] 张金柱, 张晓林. 利用引用科学知识突变识别突破性创新[J]. 情报学报, 2014, 33(3): 259-266.
[1] (Zhang Jinzhu, Zhang Xiaolin. Identification of Radical Innovation Based on Mutation of Cited Scientific Knowledge[J]. Journal of the China Society for Scientific and Technical Information, 2014, 33(3): 259-266.)
[2] 许海云, 刘亚辉, 罗瑞. 突破性科学创新早期识别研究综述[J]. 情报理论与实践, 2021, 44(4): 198-205.
[2] (Xu Haiyun, Liu Yahui, Luo Rui. A Review on Early Identification of Science Breakthrough[J]. Information Studies: Theory & Application, 2021, 44(4): 198-205.)
[3] Dosi G, Nelson R R. Technological Paradigms and Technological Trajectories[J]. Research Policy, 1982, 11(3): 147-162.
doi: 10.1016/0048-7333(82)90016-6
[4] Kotelnikov V. Radical Innovation Versus Incremental Innovation[M]. Boston: Harvard Business Review Press, 2000: 41-85.
[5] Dahlin K B, Behrens D M. When is an Invention Really Radical? Defining and Measuring Technological Radicalness[J]. Research Policy, 2005, 34(5): 717-737.
doi: 10.1016/j.respol.2005.03.009
[6] Arts S, Veugelers R. The Technological Origins and Novelty of Breakthrough Inventions[J]. SSRN Electronic Journal, 2013, DOI: 10.2139/ssrn.2230366.
doi: 10.2139/ssrn.2230366
[7] Lee F. Recombinant Uncertainty in Technological Search[J]. Management Science, 2001, 47(1): 117-132.
doi: 10.1287/mnsc.47.1.117.10671
[8] Hargadon A. How Breakthroughs Happen: The Surprising Truth About How Companies Innovate[M]. Boston: Harvard Business Review Press, 2003.
[9] Ponomarev I V, Lawton B K, Williams D E, et al. Breakthrough Paper Indicator 2.0: Can Geographical Diversity and Interdisciplinarity Improve the Accuracy of Outstanding Papers Prediction?[J]. Scientometrics, 2014, 100(3): 755-765.
doi: 10.1007/s11192-014-1320-9
[10] 朱丽, 葛爽, 张庆红. 复杂网络结构下科技政策的创新驱动: 基于网络权力和创新扩散视角[J]. 中国科技论坛, 2019(1): 29-36.
[10] (Zhu Li, Ge Shuang, Zhang Qinghong. Innovation Driven of Science and Technology Policy under the Complex Network Structure in the Perspective of Network Power and Innovation Diffusion[J]. Forum on Science and Technology in China, 2019(1): 29-36.)
[11] 隗玲, 许海云, 郭婷, 等. 基于弱共现和突发监测的情报学学科研究主题及交叉性分析[J]. 图书情报工作, 2015, 59(21): 105-114.
[11] (Wei Ling, Xu Haiyun, Guo Ting, et al. Study on the Interisciplinary Topics of Information Science Based on Weak Co-Occurrence and Burst Detecting[J]. Library and Information Service, 2015, 59(21): 105-114.)
[12] 苏屹, 林周周, 欧忠辉. 基于突变理论的技术创新形成机理研究[J]. 科学学研究, 2019, 37(3): 568-574.
[12] (Su Yi, Lin Zhouzhou, Ou Zhonghui. Research on the Formation Mechanism of Technology Innovation Based on Catastrophe Theory[J]. Studies in Science of Science, 2019, 37(3): 568-574.)
[13] 韩正琪, 刘小平, 寇晶晶. 基于Rao-Stirling指数和LDA模型的领域学科交叉主题识别: 以纳米科技为例[J]. 情报科学, 2020, 38(2): 116-124.
[13] (Han Zhengqi, Liu Xiaoping, Kou Jingjing. Interdisciplinary Literature Discovery based on Rao-Stirling Diversity Indices: Case Studies in Nanoscience and Nanotechnology[J]. Information Science, 2020, 38(2): 116-124.)
[14] 李慧, 孟玮, 徐存真. 基于专利知识流网络的技术融合分析: 以石墨烯领域为例[J]. 现代情报, 2021, 41(5): 121-130.
[14] (Li Hui, Meng Wei, Xu Cunzhen. Research on Technology Convergence Based on Patent Knowledge Flow Network: A Case Study of Graphene[J]. Journal of Modern Information, 2021, 41(5): 121-130.)
[15] Karvonen M, Kässi T. Patent Citations as a Tool for Analysing the Early Stages of Convergence[J]. Technological Forecasting and Social Change, 2013, 80(6): 1094-1107.
doi: 10.1016/j.techfore.2012.05.006
[16] Geum Y, Kim C, Lee S, et al. Technological Convergence of IT and BT: Evidence from Patent Analysis[J]. ETRI Journal, 2012, 34(3): 439-449.
doi: 10.4218/etrij.12.1711.0010
[17] Kim E, Cho Y, Kim W. Dynamic Patterns of Technological Convergence in Printed Electronics Technologies: Patent Citation Network[J]. Scientometrics, 2014, 98(2): 975-998.
doi: 10.1007/s11192-013-1104-7
[18] Park H, Yoon J. Assessing Coreness and Intermediarity of Technology Sectors Using Patent Co-Classification Analysis: The Case of Korean National R&D[J]. Scientometrics, 2014, 98(2): 853-890.
doi: 10.1007/s11192-013-1109-2
[19] Cho Y, Kim M. Entropy and Gravity Concepts as New Methodological Indexes to Investigate Technological Convergence: Patent Network-Based Approach[J]. PLoS One, 2014, 9(6): e98009.
doi: 10.1371/journal.pone.0098009
[20] Ko N, Yoon J, Seo W. Analyzing Interdisciplinarity of Technology fusion Using Knowledge Flows of Patents[J]. Expert Systems with Applications, 2014, 41(4): 1955-1963.
doi: 10.1016/j.eswa.2013.08.091
[21] Stirling A. A General Framework for Analysing Diversity in Science, Technology and Society[J]. Journal of the Royal Society, Interface, 2007, 4(15): 707-719.
pmid: 17327202
[22] 张金柱, 张晓林. 基于科技资源的突破性创新指标及识别方法综述[J]. 图书情报工作, 2012, 56(22): 56-61.
[22] (Zhang Jinzhu, Zhang Xiaolin. Overview of Radical Innovation Indicators and Identification Methods Based on Scientific and Technical Resource[J]. Library and Information Service, 2012, 56(22): 56-61.)
[23] 周磊, 杨威, 张玉峰. 基于专利挖掘的突破性创新识别框架研究[J]. 情报理论与实践, 2016, 39(9): 73-76, 46.
[23] (Zhou Lei, Yang Wei, Zhang Yufeng. Research on the Identification Framework of Radical Innovation Based on Patent Mining[J]. Information Studies: Theory & Application, 2016, 39(9): 73-76, 46.)
[24] 黄鲁成, 蒋林杉, 吴菲菲. 萌芽期颠覆性技术识别研究[J]. 科技进步与对策, 2019, 36(1): 10-17.
[24] (Huang Lucheng, Jiang Linshan, Wu Feifei. The Identification of Disruptive Technology on Emerging Stage[J]. Science & Technology Progress and Policy, 2019, 36(1): 10-17.)
[25] Porter A L, Rafols I. Is Science Becoming More Interdisciplinary? Measuring and Mapping Six Research Fields Over Time[J]. Scientometrics, 2009, 81(3): 719-745.
doi: 10.1007/s11192-008-2197-2
[26] Chen S J, Qiu J P, Arsenault C, et al. Exploring the Interdisciplinarity Patterns of Highly Cited Papers[J]. Journal of Informetrics, 2021, 15(1): 101124.
doi: 10.1016/j.joi.2020.101124
[27] Leydesdorff L, Rafols I. Indicators of the Interdisciplinarity of Journals: Diversity, Centrality, and Citations[J]. Journal of Informetrics, 2011, 5(1): 87-100.
doi: 10.1016/j.joi.2010.09.002
[28] Cassi L, Champeimont R, Mescheba W, et al. Analysing Institutions Interdisciplinarity by Extensive Use of Rao-Stirling Diversity Index[J]. PLoS One, 2017, 12(1): e0170296.
doi: 10.1371/journal.pone.0170296
[29] Leydesdorff L, Alkemade F, Heimeriks G, et al. Patents as Instruments for Exploring Innovation Dynamics: Geographic and Technological Perspectives on “Photovoltaic Cells”[J]. Scientometrics, 2015, 102(1): 629-651.
doi: 10.1007/s11192-014-1447-8
[30] Leydesdorff L. Can Technology Life-Cycles be Indicated by Diversity in Patent Classifications? The Crucial Role of Variety[J]. Scientometrics, 2015, 105(3): 1441-1451.
pmid: 26594072
[31] Kogler D F, Heimeriks G, Leydesdorff L. Patent Portfolio Analysis of Cities: Statistics and Maps of Technological Inventiveness[J]. European Planning Studies, 2018, 26(11): 2256-2278.
doi: 10.1080/09654313.2018.1530147
[32] Leydesdorff L, Kushnir D, Rafols I. Interactive Overlay Maps for US Patent (USPTO) Data Based on International Patent Classification (IPC)[J]. Scientometrics, 2014, 98(3): 1583-1599.
doi: 10.1007/s11192-012-0923-2
[33] 孙涛涛, 唐小利, 李越. 核心专利的识别方法及其实证研究[J]. 图书情报工作, 2012, 56(4): 80-84.
[33] (Sun Taotao, Tang Xiaoli, Li Yue. Method and Application of Core Patents Identification[J]. Library and Information Service, 2012, 56(4): 80-84.)
[34] Schumpeter J A. History of Economic Analysis[M]. London: Psychology Press, 1955.
[35] Yun J, Geum Y. Analysing the Dynamics of Technological Convergence Using a Co-Classification Approach: A Case of Healthcare Services[J]. Technology Analysis & Strategic Management, 2019, 31(12): 1412-1429.
[36] Fleming L. Recombinant Uncertainty in Technological Search[J]. Management Science, 2001, 47(1): 117-132.
doi: 10.1287/mnsc.47.1.117.10671
[37] Schoenmakers W, Duysters G. The Technological Origins of Radical Inventions[J]. Research Policy, 2010, 39(8): 1051-1059.
doi: 10.1016/j.respol.2010.05.013
[38] Zhang L, Rousseau R, Glänzel W. Diversity of References as an Indicator of the Interdisciplinarity of Journals: Taking Similarity Between Subject Fields into Account[J]. Journal of the Association for Information Science and Technology, 2016, 67(5): 1257-1265.
doi: 10.1002/asi.2016.67.issue-5
[39] 刘剑锋, 刘梦娜, 何丽娜, 等. 专利价值评价的综合指标体系研究[J]. 中国发明与专利, 2018, 15(11): 56-60.
[39] (Liu Jianfeng, Liu Mengna, He Lina, et al. Research on the Comprehensive Index System of Patent Value Evaluation[J]. China Invention & Patent, 2018, 15(11): 56-60.)
[40] 张曙, 张甫, 许惠青, 等. 基于Innography平台的核心专利挖掘、竞争预警、战略布局研究[J]. 图书情报工作, 2013, 57(19): 127-133.
[40] (Zhang Shu, Zhang Fu, Xu Huiqing, et al. Knowledge Mining, Competitive Warning and Strategic Layout of Core Patents Based on Innography[J]. Library and Information Service, 2013, 57(19): 127-133.)
[41] Benson C L, Magee C L. A Hybrid Keyword and Patent Class Methodology for Selecting Relevant Sets of Patents for a Technological Field[J]. Scientometrics, 2013, 96(1): 69-82.
doi: 10.1007/s11192-012-0930-3
[42] Stevens K, Kegelmeyer P, Andrzejewski D, et al. Exploring Topic Coherence over Many Models and Many Topics [C]//Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2012: 952-961.
[43] 侯剑华, 郭爽. 太阳能电池领域关键技术分析及潜在技术探测[J]. 现代情报, 2015, 35(9): 98-104.
[43] (Hou Jianhua, Guo Shuang. Key Technology Analysis and Potential Technology Detecting in Solar Cells Domain[J]. Journal of Modern Information, 2015, 35(9): 98-104.)
[44] 张馨元. 纳米材料在有机太阳能电池中的应用研究[D]. 长春: 吉林大学, 2019.
[44] (Zhang Xinyuan. The Study of Nanomaterials Application in Organic Solar Cells[D]. Changchun: Jilin University, 2019.)
[45] Tseng F M, Hsieh C H, Peng Y N, et al. Using Patent Data to Analyze Trends and the Technological Strategies of the Amorphous Silicon Thin-Film Solar Cell Industry[J]. Technological Forecasting and Social Change, 2011, 78(2): 332-345.
doi: 10.1016/j.techfore.2010.10.010
[46] 赵磊. 透明导电薄膜表面抗反射层/结构的制备及其光电性能研究[D]. 镇江: 江苏大学, 2020.
[46] (Zhao Lei. Research on Fabrication and Photoelectric Properties of Anti-Reflection Layers/Structures on Transparent Conductive Film Surfaces[D]. Zhenjiang: Jiangsu University, 2020.)
[47] 李海泉. 非晶硅太阳电池薄膜材料的制备及其光电特性研究[D]. 呼和浩特: 内蒙古师范大学, 2013.
[47] (Li Haiquan. Studies on the Preparations of Amorphous Silicon Solar Cell Film Materials and Properties of Optoelectronic[D]. Hohhot: Inner Mongolia Normal University, 2013.)
[48] 杨志伟. 三维衬底表面银纳米线基透明导电薄膜的制备与性能研究[D]. 兰州: 兰州大学, 2017.
[48] (Yang Zhiwei. Fabrication and Properties of AgNWs-Based Transparent Conductive Films on 3D Substrates[D]. Lanzhou: Lanzhou University, 2017.)
[49] 赵茜. 塔式太阳能热电系统镜场调度的优化[D]. 杭州: 浙江大学, 2017.
[49] (Zhao Qian. Optimization on the Scheduling of the Heliostat Field in a Solar Tower Power Plant[D]. Hangzhou: Zhejiang University, 2017.)
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