Identifying the impact of patent family on the patent trajectory: A case of thin film solar cells technological trajectories

Abstract Incessant technology development and evolution has thrusted the need to recognize promising technological opportunities for organizations. Since patents contain information about an innovation, they are considered essential data sources for the development of new technology. The greater the size of international patent families, the more valuable they are. This study analyzed the technological changes in a technology domain by eliminating the effects on evolution path caused by patent families and discussed the implications of the technological path obtained from different parameters. This study builds on the assumption that the large number of patent family citations results in their increased influence, which affects the calculation of information flow in the path, and ultimately impacts the patent trajectory. An iterative process was employed in this study to reduce the effect of patent families, by establishing information flow, and map the main path network until the interference path from the main path was bifurcated. Technological innovation and the path phenomenon were identified through the key-route search attained using different paths. This study chose the case of thin-film solar technology and retrieved its patent data from the United States Patent and Trademark Office (USPTO) database. The Main Path Analysis (MPA) and the technological trajectories’ analysis were utilized. The weight values calculated by the self-citation of the patent family create an interference path which are then adjusted in the global search, and a new main path is generated. Through this adjustment, the accuracy rate of identifying technological evolution and breakthrough innovation is enhanced.

[1]  Yu-Hsin Chang,et al.  Mapping Technological Trajectories for Energy Storage Device through Patent Citation Network , 2018, 2018 9th International Conference on Awareness Science and Technology (iCAST).

[2]  Kuei-Kuei Lai,et al.  Computing Redundancy in Complementary and Supplementary Technologies Using TLC Indicators: A Theoretical Framework , 2019, 2019 Portland International Conference on Management of Engineering and Technology (PICMET).

[3]  Donald Walter.  Patent-to-patent versus patent family-to-patent family citations and the impact of an invention. , 2014, Pharmaceutical Patent Analyst.

[4]  M. Gittelman,et al.  Patent Citations as a Measure of Knowledge Flows: The Influence of Examiner Citations , 2006, The Review of Economics and Statistics.

[5]  S. Guha,et al.  Recent progress in amorphous silicon alloy leading to 13% stable cell efficiency , 1997, Conference Record of the Twenty Sixth IEEE Photovoltaic Specialists Conference - 1997.

[6]  John S. Liu,et al.  A few notes on main path analysis , 2019, Scientometrics.

[7]  Vladimir Batagelj,et al.  Efficient Algorithms for Citation Network Analysis , 2003, ArXiv.

[8]  Juin-Ming Tsai,et al.  Technology acquisition models for fast followers in high-technological markets: an empirical analysis of the LED industry , 2018, Technol. Anal. Strateg. Manag..

[9]  Rna Rudi Bekkers,et al.  Knowledge positions in high-tech markets: Trajectories, standards, strategies and true innovators , 2012 .

[10]  Debin Du,et al.  Measuring universities’ R&D performance in China’s provinces: a multistage efficiency and effectiveness perspective , 2018, Technol. Anal. Strateg. Manag..

[11]  T. D. Lee,et al.  A review of thin film solar cell technologies and challenges , 2017 .

[12]  Walter G. Park,et al.  Can patent family size and composition signal patent value? , 2019, Applied Economics.

[13]  Rainer Frietsch,et al.  Patent families as macro level patent value indicators: applying weights to account for market differences , 2012, Scientometrics.

[14]  John Metcalfe,et al.  Mapping evolutionary trajectories: Applications to the growth and transformation of medical knowledge , 2007 .

[15]  Oumarou Savadogo,et al.  Chemically and electrochemically deposited thin films for solar energy materials , 1998 .

[16]  Rongrong Li,et al.  Identifying R&D partners for dye-sensitized solar cells: a multi-level patent portfolio-based approach , 2018, Technol. Anal. Strateg. Manag..

[17]  Yu-Hsin Chang,et al.  A hybrid clustering approach to identify network positions and roles through social network and multivariate analysis , 2017, Scientometrics.

[18]  Harri Haapasalo,et al.  Analysis of Technology Management Functions in Finnish High Tech Companies , 2009 .

[19]  Lydia Helena Wong,et al.  Towards high efficiency thin film solar cells , 2017 .

[20]  Alan L. Porter,et al.  Forecasting Innovation Pathways (FIP) for new and emerging science and technologies , 2013 .

[21]  Mu-Hsuan Huang,et al.  Increasing science and technology linkage in fuel cells: A cross citation analysis of papers and patents , 2015, J. Informetrics.

[22]  Qing Ke,et al.  Comparing scientific and technological impact of biomedical research , 2018, J. Informetrics.

[23]  Kwangsoo Kim,et al.  An analysis of property-function based patent networks for strategic R&D planning in fast-moving industries: The case of silicon-based thin film solar cells , 2012, Expert Syst. Appl..

[24]  Vladimir Batagelj,et al.  Analyzing the Structure of U.S. Patents Network , 2006, Data Science and Classification.

[25]  Chao-Fu Hong,et al.  Exploring Technology Opportunities in an Industry via Clustering Method and Association Analysis , 2013, ICCCI.

[26]  Mu-Hsuan Huang,et al.  Cross-field evaluation of publications of research institutes using their contributions to the fields' MVPs determined by h-index , 2013, J. Informetrics.

[27]  Catalina Martínez,et al.  Patent families: When do different definitions really matter? , 2010, Scientometrics.

[28]  Cecilia Hasner,et al.  Technology advances in sugarcane propagation: A patent citation study , 2019, World Patent Information.

[29]  Inchae Park,et al.  Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network , 2018, Journal of Informetrics.

[30]  Junmo Kim,et al.  Mapping extended technological trajectories: integration of main path, derivative paths, and technology junctures , 2018, Scientometrics.

[31]  Mu-Hsuan Huang,et al.  Bibliographically coupled patents: Their temporal pattern and combined relevance , 2019, J. Informetrics.

[32]  Alan L. Porter,et al.  Exploring Technology evolution pathways to facilitate Technology management: A study of Dye-sensitized solar cells (DSSCs) , 2016, 2016 Portland International Conference on Management of Engineering and Technology (PICMET).

[33]  Konstantinos Chalvatzis,et al.  Innovative technology in the Pacific: Building resilience for vulnerable communities , 2018 .

[34]  So Young Sohn,et al.  A novel approach to explore patent development paths for subfield technologies , 2018, J. Assoc. Inf. Sci. Technol..

[35]  A. Pahlavan,et al.  Efficiency improvement of a silicon-based thin-film solar cell using plasmonic silver nanoparticles and an antireflective layer , 2020 .

[36]  Yann Ménière,et al.  International patent families: from application strategies to statistical indicators , 2017, Scientometrics.

[37]  Jie Chen,et al.  Organisational structure and managerial innovation: the mediating effect of cross-functional integration , 2018, Technol. Anal. Strateg. Manag..

[38]  Sungjoo Lee,et al.  Patterns of technological innovation and evolution in the energy sector: A patent-based approach , 2013 .

[39]  Hai Zhuge,et al.  Forward search path count as an alternative indirect citation impact indicator , 2019, J. Informetrics.

[40]  Jeppe Nicolaisen,et al.  Citation analysis , 2007, Annu. Rev. Inf. Sci. Technol..

[41]  Chung-Huei Kuan,et al.  Photovoltaic technology development: A perspective from patent growth analysis , 2011 .

[42]  Loet Leydesdorff,et al.  Patent classifications as indicators of intellectual organization , 2008, J. Assoc. Inf. Sci. Technol..

[43]  Horng-Jinh Chang,et al.  First movers or latecomers?: monitoring and development of core technology capability for solar thin-film technology vendors: the perspective of patent statistics , 2018 .

[44]  G. Dosi.  Technological Paradigms and Technological Trajectories: A Suggested Interpretation of the Determinants and Directions of Technical Change , 1982 .

[45]  Changyong Lee,et al.  Hawkes process-based technology impact analysis , 2017, J. Informetrics.

[46]  John S. Liu,et al.  An integrated approach for main path analysis: Development of the Hirsch index as an example , 2012, J. Assoc. Inf. Sci. Technol..

[47]  Lixin Chen,et al.  Do patent citations indicate knowledge linkage? The evidence from text similarities between patents and their citations , 2017, J. Informetrics.

[48]  Tsung-Hsien Kuo,et al.  Note on a heuristic procedure to identify the most valuable chain of patent priority network , 2011, 2012 Proceedings of PICMET '12: Technology Management for Emerging Technologies.

[49]  Tieju Ma,et al.  A methodology to position nations’ efforts in a technology domain with a patent network analysis: case of the electric vehicle domain , 2018, Technol. Anal. Strateg. Manag..

[50]  Christophe Feder,et al.  The effects of disruptive innovations on productivity , 2018 .

[51]  So Young Sohn,et al.  Exploring the forward citation patterns of patents based on the evolution of technology fields , 2019, J. Informetrics.

[52]  Yu-Hsin Chang,et al.  Construct a Three-Stage Analysis Model of Integrated Main Path Analysis and Patent Family-Exploring the Development of Blockchain , 2019, ICSEB 2019.

[53]  Yu-Hsin Chang,et al.  A structural analysis approach to identify technology innovation and evolution path: a case of m-payment technology ecosystem , 2020, J. Knowl. Manag..

[54]  Sungjoo Lee,et al.  Identifying promising technologies using patents: A retrospective feature analysis and a prospective needs analysis on outlier patents , 2017 .

[55]  Rodrigo Kazuo Ikenami,et al.  Unpacking the innovation ecosystem construct: Evolution, gaps and trends , 2016, Technological Forecasting and Social Change.

[56]  Norman P. Hummon,et al.  Connectivity in a citation network: The development of DNA theory☆ , 1989 .

[57]  Z. Griliches,et al.  Citations, Family Size, Opposition and the Value of Patent Rights Have Profited from Comments and Suggestions , 2002 .

[58]  Lili Wang,et al.  Mapping technological trajectories and exploring knowledge sources: A case study of 3D printing technologies , 2020 .

[59]  Kuei-Kuei Lai,et al.  The role on inter-organizational knowledge flows of patent citation network: The case of Thin-film solar cells , 2019, 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC).

[60]  Yoshiyuki Takeda,et al.  Tracking emerging technologies in energy research : toward a roadmap for sustainable energy , 2008 .

[61]  Giovanni Abramo,et al.  The balance of knowledge flows , 2019, J. Informetrics.

[62]  John S. Liu,et al.  Exploring knowledge diffusion among nations: a study of core technologies in fuel cells , 2014, Scientometrics.

[63]  Holger Ernst,et al.  Patent information for strategic technology management , 2003 .

[64]  M. Trajtenberg,et al.  Patents, Citations and Innovations , 2002 .

[65]  Heejin Lee,et al.  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 , 2017 .

[66]  Dirk Meissner,et al.  Special issue on ‘corporate foresight and innovation management’ , 2018, Technol. Anal. Strateg. Manag..

[67]  Yun Liu,et al.  Mapping technological development using patent citation trees: an analysis of bogie technology , 2018, Technol. Anal. Strateg. Manag..

[68]  John A. Mathews,et al.  Knowledge flows in the solar photovoltaic industry: Insights from patenting by Taiwan, Korea, and China , 2012 .

[69]  Vladimir Batagelj,et al.  Pajek - Analysis and Visualization of Large Networks , 2001, Graph Drawing Software.

[70]  Yu-Hsin Chang,et al.  A structured MPA approach to explore technological core competence, knowledge flow, and technology development through social network patentometrics , 2020, J. Knowl. Manag..

[71]  Yuya Kajikawa,et al.  The effect of patent family information in patent citation network analysis: a comparative case study in the drivetrain domain , 2015, Scientometrics.

[72]  Donghyun You,et al.  Developmental Trajectories in Electrical Steel Technology Using Patent Information , 2018, Sustainability.

[73]  Shuo Xu,et al.  Review on emerging research topics with key-route main path analysis , 2019, Scientometrics.

[74]  Loet Leydesdorff,et al.  Patent citation spectroscopy (PCS): Online retrieval of landmark patents based on an algorithmic approach , 2018, J. Informetrics.

[75]  Wesley M. Cohen,et al.  R&D spillovers, patents and the incentives to innovate in Japan and the United States , 2002 .

[76]  Fang-Mei Tseng,et al.  Using patent data to analyze trends and the technological strategies of the amorphous silicon thin-film solar cell industry , 2011 .

[77]  Yu-Hsin Chang,et al.  Technological Evolution of Thin-film Solar Cells through Main Path Analysis , 2018 .

[78]  Yongtae Park,et al.  Monitoring the organic structure of technology based on the patent development paths , 2009 .

[79]  Jeffrey M. Kuhn,et al.  Patent Citations and Empirical Analysis , 2017 .

[80]  George A. Barnett,et al.  Global networks of genetically modified crops technology: a patent citation network analysis , 2019, Scientometrics.

[81]  A Comparative Value Chains Analysis of Solar Electricity for Energy , .