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Combining SAO semantic analysis and morphology analysis to identify technology opportunities

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Abstract

Increasingly complex competitive environments drive corporations in almost all industries to conduct omnibearing innovation activities to enhance their technological innovation capability and international competitiveness. Against this background, we propose subject–action–object (SAO) based morphological analysis to identify technology opportunities by detecting prioritized combinations within the morphology matrix. SAO structures emphasize the key concepts with provision of diverse technology information based on semantic relationships. The combination of SAO semantic structures can support the establishment of matrix, which consists of two dimensions: compositions and properties of technology. Later, novel indicators are used to evaluate the subsequent technological feasibility of each new configuration under a customized analysis and prior combinations aided by a high score can be identified. We apply this method to the case of dye-sensitized solar cells (DSSCs) in patents documents. The approach holds promise to strengthen information support systems for commercial enterprises in technical innovation and market innovation activities. We believe the analysis can be adapted well to fit other technologies, especially in their emerging stage.

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Notes

  1. Our matrix consists of rows and columns. Later on, rows represent shapes or problems, while columns would represent components/dimensions/subsystems. Later, each column is further divided, called “level of respective columns”.

  2. Derwent Innovations Index® is capable of advance, accurate, and detailed patent databases, especially in the field of chemical, electrical, and mechanical engineering. It is enhanced by Derwent World Patents Index® and Derwent Patent Citation Index®. The abstract of patents includes USE, ADVANTAGE, NOVELTY, DESCRIPTION OF DRAWINGS, and DETAILED DESCRIPTION. http://thomsonreuters.com/en/products-services/scholarly-scientific-research/scholarly-search-and-discovery/derwent-innovations-index.html.

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Acknowledgements

We acknowledge support from the General Program of the National Natural Science Foundation of China (Grant No. 71373019) and the National High Technology Research and Development Program of China (Grant No. 2014AA015105). The findings and observations contained in this paper are those of the authors and do not necessarily reflect the views of the supporters. The authors would like to thank Omer Hanif, Chao Yang, Pengjun Qiu, Yun Fu, and other colleagues in the “Co-lab of technology innovation” of Beijing Institute of Technology, Georgia Institute of Technology, and Manchester University, for their advice and feedback.

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Authors and Affiliations

  1. School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China

    Xuefeng Wang, Pingping Ma, Ying Huang, Junfang Guo, Donghua Zhu & Zhinan Wang

  2. School of Public Policy, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Alan L. Porter

  3. Search Technology, Inc., Atlanta, GA, 30092, USA

    Alan L. Porter

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  1. Xuefeng Wang
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  2. Pingping Ma
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  3. Ying Huang
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  4. Junfang Guo
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  5. Donghua Zhu
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  6. Alan L. Porter
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  7. Zhinan Wang
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Correspondence to Xuefeng Wang.

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Wang, X., Ma, P., Huang, Y. et al. Combining SAO semantic analysis and morphology analysis to identify technology opportunities. Scientometrics 111, 3–24 (2017). https://doi.org/10.1007/s11192-017-2260-y

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  • DOI: https://doi.org/10.1007/s11192-017-2260-y

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