Abstract
Main path analysis (MPA) is a method for efficiently analyzing technological trends, which change rapidly in competitive environments. In general, MPA is based on citation networks, and it is used to derive the most key path in a complex network. However, the existing studies using MPA do not use important textual information of patents, except for citation data. In this paper, we suggest a new MPA based on patent documents to identify the main path of technological evolution. For this purpose, first, we used the subject-action-object structure to derive core keywords based on causal relationships in patent claims. Second, the DEcision-MAking Trial and Evaluation Laboratory (DEMATEL) technique was applied to draw link weights between patents where causal relationships of keywords were reflected. Finally, a main path in a patent network was identified using the global main path and key-route main path analysis methods. In this paper, we collected and analyzed patent data related to self-driving car technologies, and we verified the technical changes in the main path obtained based on the proposed approach. We found that the generic technologies of the self-driving operation had the strongest influence on the other self-driving car technologies in the sensing-planning-acting steps.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11192-023-04652-2/MediaObjects/11192_2023_4652_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11192-023-04652-2/MediaObjects/11192_2023_4652_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11192-023-04652-2/MediaObjects/11192_2023_4652_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11192-023-04652-2/MediaObjects/11192_2023_4652_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11192-023-04652-2/MediaObjects/11192_2023_4652_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11192-023-04652-2/MediaObjects/11192_2023_4652_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11192-023-04652-2/MediaObjects/11192_2023_4652_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11192-023-04652-2/MediaObjects/11192_2023_4652_Fig8_HTML.png)
Similar content being viewed by others
References
Altshuller, G., & Altov, H. (1996). And suddenly the inventor appeared: TRIZ, the theory of inventive problem solving. Technical Innovation Center Inc.
Chen, L., Xu, S., Zhu, L., Zhang, J., Xu, H., & Yang, G. (2022). A semantic main path analysis method to identify multiple developmental trajectories. Journal of Informetrics, 16(2), 101281.
Dabral, S., Kamath, S., Appia, V., Mody, M., Zhang, B., & Batur, U. (2014). Trends in camera based automotive driver assistance systems (adas). In 2014 IEEE 57th International Midwest Symposium on Circuits and Systems (MWSCAS), IEEE.
Dalvi-Esfahani, M., Niknafs, A., Kuss, D. J., Nilashi, M., & Afrough, S. (2019). Social media addiction: Applying the DEMATEL approach. Telematics and Informatics, 43, 101250.
Dean, J. (2011). Extremely mobile devices. Popular Science., 35, 2011–2008.
Dixon, G., Hart, P. S., Clarke, C., O’Donnell, N. H., & Hmielowski, J. (2020). What drives support for self-driving car technology in the United States? Journal of Risk Research, 23(3), 275–287.
Duraisamy, B., Schwarz, T., & Wöhler, C. (2013). Track level fusion algorithms for automotive safety applications. In 2013 International Conference on Signal Processing, Image Processing & Pattern Recognition, IEEE.
Surden, H., & Williams, M.- A. (2016). Self-driving cars predictability and law. Working Draft.
Guo, J., Wang, X., Li, Q., & Zhu, D. (2016). Subject–action–object-based morphology analysis for determining the direction of technological change. Technological Forecasting and Social Change, 105, 27–40.
Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2018). Building direct citation networks. Scientometrics, 115(2), 817–832.
Hu, Z., Fang, S., Wei, L., Wen, Y., Zhang, X., & Wang, M. (2015). An SAO-based approach to technology evolution analysis using patent information: Case study—Graphene sensors. Journal of Data and Information Science, 7(3), 62.
Hummon, N. P., & Dereian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks, 11(1), 39–63.
Hwang, S., & Shin, J. (2019). Extending technological trajectories to latest technological changes by overcoming time lags. Technological Forecasting and Social Change, 143, 142–153.
Jiang, X., & Zhuge, H. (2019). Forward search path count as an alternative indirect citation impact indicator. Journal of Informetrics, 13(4), 100977.
Jo, K., & Sunwoo, M. (2013). Generation of a precise roadway map for autonomous cars. IEEE Transactions on Intelligent Transportation Systems, 15(3), 925–937.
Joung, J., & Kim, K. (2017). Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data. Technological Forecasting and Social Change, 114, 281–292.
Jung, C. M., Hur, W.-M., & Kim, Y. (2015). A comparison study of smartphone acceptance between Korea and the USA. International Journal of Mobile Communications, 13(4), 433–453.
Kaempchen, N., K. C. Fuerstenberg, A. G. Skibicki and K. C. Dietmayer (2004). Sensor fusion for multiple automotive active safety and comfort applications. In Advanced Microsystems for Automotive Applications (pp. 137–163). Springer.
Kim, S., Bracewell, R. H., & Wallace, K. M. (2007). A framework for automatic causality extraction using semantic similarity. In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference.
Kim, H., & Kim, K. (2012). Causality-based function network for identifying technological analogy. Expert Systems with Applications, 39(12), 10607–10619.
Kim, S., Park, I., & Yoon, B. (2020). SAO2Vec: Development of an algorithm for embedding the subject–action–object (SAO) structure using Doc2Vec. PLoS ONE, 15(2), e0227930.
Kim, Y. G., Suh, J. H., & Park, S. C. (2008). Visualization of patent analysis for emerging technology. Expert Systems with Applications, 34(3), 1804–1812.
Krishna, R. J., Chaudhry, Y., & Sharma, D. P. (2018). Analysis of community detection algorithms. In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), IEEE.
Kumar, V., Lai, K. K., Chang, Y. H., Bhatt, P. C., & Su, F. P. (2020). A structural analysis approach to identify technology innovation and evolution path: A case of M-payment technology ecosystem. Journal of Knowledge Management, 25(2), 477–499.
Lai, K. K., Chen, H. C., Chang, Y. H., Kumar, V., & Bhatt, P. C. (2020). A structured MPA approach to explore technological core competence, knowledge flow, and technology development through social network patentometrics. Journal of Knowledge Management, 25(2), 402–432.
Lathabai, H. H., Prabhakaran, T., & Changat, M. (2017). Contextual productivity assessment of authors and journals: A network scientometric approach. Scientometrics, 110(2), 711–737.
Levien, M. (2011). Freight shuttle system: Cross-border movement of goods. Texas A & M Research Foundation.
Liao, S. C., Chou, T. C., & Huang, C. H. (2022). Revisiting the development trajectory of the digital divide: A main path analysis approach. Technological Forecasting and Social Change, 179, 121607.
Liu, X., Yu, Y., Guo, C., Sun, Y., & Gao, L. (2014). Full-text based context-rich heterogeneous network mining approach for citation recommendation. In IEEE/ACM Joint Conference on Digital Libraries, IEEE.
Mesbahi, M. R., Rahmani, A. M., & Hosseinzadeh, M. (2017). Highly reliable architecture using the 80/20 rule in cloud computing datacenters. Future Generation Computer Systems, 77, 77–86.
Namjoo, M. R., & Keramati, A. (2018). Analysing Causal dependencies of composite service resilience in cloud manufacturing using resource-based theory and DEMATEL method. International Journal of Computer Integrated Manufacturing, 31(10), 942–960.
Narla, S. R. (2013). The evolution of connected vehicle technology: From smart drivers to smart cars to... self-driving cars. Ite Journal, 83(7), 22–26.
Park, H., Ree, J. J., & Kim, K. (2013). Identification of promising patents for technology transfers using TRIZ evolution trends. Expert Systems with Applications, 40(2), 736–743.
Patole, S. M., Torlak, M., Wang, D., & Ali, M. (2017). Automotive radars: A review of signal processing techniques. IEEE Signal Processing Magazine, 34(2), 22–35.
Reh, F. J. (2017). Understanding Pareto’s principle-the 80-20 rule, The Balance.
Roop, S. S., Ragab, A. H., Olson, L. E., Protopapa, A. A., Yager, M. A., Morgan, C. A., Warner, J. E., Mander, J., Parkar, A., & Roy, S. L. (2010). The freight shuttle system: advancing commercial readiness. Texas Transportation Institute.
Sahin, T., Klugel, M., Zhou, C., & Kellerer, W. (2018). Virtual cells for 5G V2X communications. IEEE Communications Standards Magazine, 2(1), 22–28.
Shieh, J.-I., Wu, H.-H., & Huang, K.-K. (2010). A DEMATEL method in identifying key success factors of hospital service quality. Knowledge-Based Systems, 23(3), 277–282.
Tian, Y., Pei, K., Jana, S., & Ray, B. (2018). Deeptest: Automated testing of deep-neural-network-driven autonomous cars. In Proceedings of the 40th International Conference On Software Engineering.
Tsai, S.-B., Zhou, J., Gao, Y., Wang, J., Li, G., Zheng, Y., Ren, P., & Xu, W. (2017). Combining FMEA with DEMATEL models to solve production process problems. PLoS ONE, 12(8), e0183634.
Tseng, Y.-H., Wang, Y.-M., Lin, Y.-I., Lin, C.-J., & Juang, D.-W. (2007). Patent surrogate extraction and evaluation in the context of patent map**. Journal of Information Science, 33(6), 718–736.
Volvo. (2017, Jun 27, 2017). "Volvo Cars and Autoliv team up with NVIDIA to develop advanced systems for self-driving cars." from https://www.media.volvocars.com/global/en-gb/media/pressreleases/209929/volvo-cars-and-autoliv-team-up-with-nvidia-to-develop-advanced-systems-for-self-driving-cars.
Wang, X., Ma, P., Huang, Y., Guo, J., Zhu, D., Porter, A. L., & Wang, Z. (2017a). Combining SAO semantic analysis and morphology analysis to identify technology opportunities. Scientometrics, 111(1), 3–24.
Wang, X., Wang, Z., Huang, Y., Liu, Y., Zhang, J., Heng, X., & Zhu, D. (2017b). Identifying R&D partners through Subject-Action-Object semantic analysis in a problem & solution pattern. Technology Analysis & Strategic Management, 29(10), 1167–1180.
**a, W., Li, H., & Li, B. (2016). A control strategy of autonomous vehicles based on deep reinforcement learning. In 2016 9th International Symposium on Computational Intelligence and Design (ISCID), IEEE.
Yang, C., Huang, C., & Su, J. (2018). An improved SAO network-based method for technology trend analysis: A case study of graphene. Journal of Informetrics, 12(1), 271–286.
Yang, Y.-T., & Shieh, J.-C. (2019). Is there the Pareto principle in public library circulation? A case study of one public library in Taiwan. Malaysian Journal of Library & Information Science, 24(2), 97–113.
Yoon, B., & Jeong, S. (2013). Impact analysis of biological technology: Application of network analysis and decision making trial and evaluation laboratory. Advanced Science Letters, 19(12), 3610–3614.
Yoon, B., & Park, Y. (2004). A text-mining-based patent network: Analytical tool for high-technology trend. The Journal of High Technology Management Research, 15(1), 37–50.
Yu, D., & Sheng, L. (2021). Influence difference main path analysis: Evidence from DNA and blockchain domain citation networks. Journal of Informetrics, 15(4), 101186.
Zhang, W., & Deng, Y. (2019). Combining conflicting evidence using the DEMATEL method. Soft Computing, 23(17), 8207–8216.
Zhou, Q., Huang, W., & Zhang, Y. (2011). Identifying critical success factors in emergency management using a fuzzy DEMATEL method. Safety Science, 49(2), 243–252.
Funding
This work was supported by the National Research Foundation of Korea under Grant NRF-2021R1I1A2045721.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no financial or non-financial interests to disclose.
Appendix: Searching formulas
Appendix: Searching formulas
-
(a)
recognition: “TAC = ((("RADAR" OR "radio detection and ranging" OR "radio waves" OR "millimeter wave" OR "SRR" OR "Short Range Radar" OR "LRR" OR "Long Range Radar" OR "Laser radar") OR ("ultraviolet" OR "near infrared" OR "LASER" OR "Light Amplification by the Stimulated Emission of Radiation" OR "LIDAR" OR "Lager Imaging Detection And Ranging") OR "Ultrasonic") AND ((auto* OR robotic OR driverless OR "self drivinig") W/5 (vehicle OR car)))”, “TAC = ("camera" AND ((auto* OR robotic OR driverless OR "self drivinig") W/5 (vehicle OR car)))”, and “TAC = (("telematics" OR "V2X" OR "V2V" OR "vehicle-to-vehicle" OR "vehicle-to-infrastructure" OR "Vehicular communication" OR "connected car" OR "connected vehicle" OR "wireless communication" OR "bluetooth" OR "Wi Fi" OR "short range communication" OR "internet of things" OR "in-car networking" OR "connectivity") AND ((auto* OR robotic OR driverless OR "self driving") W/5 (vehicle OR car)))”.
-
(b)
judgment: “TAC = ((((self OR vehicle OR driving) AND (plan* OR diagnos* OR monitor*)) OR "On board diagnos*" OR "OBD") AND ((auto* OR robotic OR driverless OR "self drivinig") W/5 (vehicle OR car)))”, and “TAC = (((adaptive OR super OR smart) AND (cruise OR decision OR act*)) AND ((auto* OR robotic OR driverless OR "self drivinig") W/5 (vehicle OR car)))”.
-
(c)
control: “TAC = ((((touch OR active front OR active independent front OR car) AND steering) OR steering) AND ((auto*OR robotic OR driverless OR "self drivinig") W/5 (vehicle OR car)))”, “TAC = ((speed AND (control OR adapt* OR advice OR device)) AND ((auto* OR robotic OR driverless OR "self drivinig") W/5 (vehicle OR car)))”, and “TAC = (((("automotive power" OR "vehicle stability" OR engine) AND control) OR "active safety") AND ((auto* OR robotic OR driverless OR "self drivinig") W/5 (vehicle OR car)))”.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Oh, M., Jang, H., Kim, S. et al. Main path analysis for technological development using SAO structure and DEMATEL based on keyword causality. Scientometrics 128, 2079–2104 (2023). https://doi.org/10.1007/s11192-023-04652-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-023-04652-2