Skip to main content

An entropy-based indicator system for measuring the potential of patents in technological innovation: rejecting moderation

Scientometrics volume 111, pages 1925–1946 (2017)Cite this article

Abstract

How to evaluate the value of a patent in technological innovation quantitatively and systematically challenges bibliometrics. Traditional indicator systems and weighting approaches mostly lead to “moderation” results; that is, patents ranked to a top list can have only good-looking values on all indicators rather than distinctive performances in certain individual indicators. Orienting patents authorized by the United States Patent and Trademark Office (USPTO), this paper constructs an entropy-based indicator system to measure their potential in technological innovation. Shannon’s entropy is introduced to quantitatively weight indicators and a collaborative filtering technique is used to iteratively remove negative patents. What remains is a small set of positive patents with potential in technological innovation as the output. A case study with 28,509 USPTO-authorized patents with Chinese assignees, covering the period from 1976 to 2014, demonstrates the feasibility and reliability of this method.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2

Notes

  1. http://thomsonreuters.com/en/products-services/intellectual-property/patent-research-and-analysis/derwent-world-patents-index.html.

  2. Note that patents in the 2-round iteration will exclude patents in the 3-round iteration, i.e., there are 751 patents in 3R and 16,928 patents in 2R.

  3. http://www.keplerenergy.co.uk/about-us.html.

  4. http://www.thomsoninnovation.com/tip-innovation/recordView.do?datasource=T3&category=PAT&&idType=pns&databaseIds=PATENT&fromExternalLink=true&recordKeys=US6942993B2&locale=en_US.

  5. http://www.tasly.com/en_web/index.aspx.

References

  • Agrawal, A. (2006). Engaging the inventor: Exploring licensing strategies for university inventions and the role of latent knowledge. Strategic Management Journal, 27(1), 63–79.

    Article  Google Scholar 

  • Alcacer, J., & Gittelman, M. (2006). Patent citations as a measure of knowledge flows: The influence of examiner citations. The Review of Economics and Statistics, 88(4), 774–779.

    Article  Google Scholar 

  • Archibugi, D., & Planta, M. (1996). Measuring technological change through patents and innovation surveys. Technovation, 16(9), 451–519.

    Article  Google Scholar 

  • Azagra-Caro, J., Fernández-de-Lucio, I., Perruchas, F., & Mattsson, P. (2009). What do patent examiner inserted citations indicate for a region with low absorptive capacity? Scientometrics, 80(2), 441–455.

    Article  Google Scholar 

  • Basberg, B. L. (1987). Patents and the measurement of technological change: A survey of the literature. Research Policy, 16(2), 131–141.

    Article  Google Scholar 

  • Bessen, J. (2008). The value of US patents by owner and patent characteristics. Research Policy, 37(5), 932–945.

    Article  Google Scholar 

  • Bozbura, F. T., Beskese, A., & Kahraman, C. (2007). Prioritization of human capital measurement indicators using fuzzy AHP. Expert Systems with Applications, 32(4), 1100–1112.

    Article  Google Scholar 

  • Braun, T., Glänzel, W., & Schubert, A. (2006). A Hirsch-type index for journals. Scientometrics, 69(1), 169–173.

    Google Scholar 

  • Chen, Y.-S., & Chang, K.-C. (2012). Using the entropy-based patent measure to explore the influences of related and unrelated technological diversification upon technological competences and firm performance. Scientometrics, 90(3), 825–841.

    Article  Google Scholar 

  • Chen, D.-Z., Chang, H.-W., Huang, M.-H., & Fu, F.-C. (2005). Core technologies and key industries in Taiwan from 1978 to 2002: A perspective from patent analysis. Scientometrics, 64(1), 31–53.

    Article  Google Scholar 

  • Choi, C., & Park, Y. (2009). Monitoring the organic structure of technology based on the patent development paths. Technological Forecasting and Social Change, 76(6), 754–768.

    Article  Google Scholar 

  • Choi, S., Yoon, J., Kim, K., Lee, J. Y., & Kim, C.-H. (2011). SAO network analysis of patents for technology trends identification: A case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells. Scientometrics, 88(3), 863–883.

    Article  Google Scholar 

  • Cronin, B., & Meho, L. (2006). Using the h-index to rank influential information scientistss. Journal of the American Society for Information Science and Technology, 57(9), 1275–1278.

    Article  Google Scholar 

  • de Solla Price, D. (1983). Sealing wax and string: A philosophy of the experimenter’s craft and its role in the genesis of high technology. Paper presented at the Sarton Lecture, AAAS Meeting (May 1983).

  • Egghe, L. (2006). Theory and practice of the g-Index. Scientometrics, 69(1), 131–152.

    Article  Google Scholar 

  • Fleming, L. (2001). Recombinant uncertainty in technological search. Management Science, 47(1), 117–132.

    Article  Google Scholar 

  • Funk, R. J., & Owen-Smith, J. (2016). A dynamic network measure of technological change. Management Science. doi:10.1287/mnsc.2015.2366.

    Google Scholar 

  • Gittelman, M., & Kogut, B. (2003). Does good science lead to valuable knowledge? Biotechnology firms and the evolutionary logic of citation patterns. Management Science, 49(4), 366–382.

    Article  Google Scholar 

  • Glänzel, W., & Thijs, B. (2012). Using “core documents” for detecting and labelling new emerging topics. Scientometrics, 91(2), 399–416. doi:10.1007/s11192-011-0591-7.

    Article  Google Scholar 

  • Grimaldi, M., Cricelli, L., Di Giovanni, M., & Rogo, F. (2015). The patent portfolio value analysis: A new framework to leverage patent information for strategic technology planning. Technological Forecasting and Social Change, 94, 286–302. doi:10.1016/j.techfore.2014.10.013.

    Article  Google Scholar 

  • Grupp, H. (1990). The concept of entropy in scientometrics and innovation research: An indicator for institutional involvement in scientific and technological developments. Scientometrics, 18(3–4), 219–239.

    Article  Google Scholar 

  • Guan, J., & He, Y. (2007). Patent-bibliometric analysis on the Chinese science—Technology linkages. Scientometrics, 72(3), 403–425.

    Article  Google Scholar 

  • Guellec, D., & de la Potterie, Bv P. (2000). Applications, grants and the value of patent. Economics Letters, 69(1), 109–114.

    Article  MATH  Google Scholar 

  • Guellec, D., & de la Potterie, Bv P. (2001). The internationalisation of technology analysed with patent data. Research Policy, 30(8), 1253–1266.

    Article  Google Scholar 

  • Hall, B. H. (2002). The NBER patent citation data file: Lessons, insights and methodological tools. In A. B. Jaffe & M. Trajtenberg (Eds.), Patents, citations and innovations (pp. 403–460). Cambridge, MA: MIT Press.

    Google Scholar 

  • Hall, B. H., Jaffe, A., & Trajtenberg, M. (2005). Market value and patent citations. Rand Journal of Economics, 36(1), 16–38.

    Google Scholar 

  • Harhoff, D., Scherer, F. M., & Vopel, K. (2003). Citations, family size, opposition and the value of patent rights. Research Policy, 32(8), 1343–1363.

    Article  Google Scholar 

  • Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National academy of Sciences of the United States of America, 16569–16572.

  • Iwami, S., Mori, J., Sakata, I., & Kajikawa, Y. (2014). Detection method of emerging leading papers using time transition. Scientometrics, 101(2), 1515–1533. doi:10.1007/s11192-014-1380-x.

    Article  Google Scholar 

  • Jiang, J., Lu, J., Zhang, G., & Long, G. (2011). Scaling-up item-based collaborative filtering recommendation algorithm based on hadoop. Paper presented at the 2011 IEEE World Congress on Services.

  • Kay, L., Newman, N., Youtie, J., Porter, A. L., & Rafols, I. (2014). Patent overlay mapping: Visualizing technological distance. Journal of the Association for Information Science and Technology, 65(12), 2432–2443.

    Article  Google Scholar 

  • Lee, Y.-G. (2009). What affects a patent’s value? An analysis of variables that affect technological, direct economic, and indirect economic value: An exploratory conceptual approach. Scientometrics, 79(3), 623–633.

    Article  Google Scholar 

  • Lee, Y.-G., Lee, J.-D., Song, Y.-I., & Lee, S.-J. (2007). An in-depth empirical analysis of patent citation counts using zero-inflated count data model: The case of KIST. Scientometrics, 70(1), 27–39.

    Article  Google Scholar 

  • Lei, X.-P., Zhao, Z.-Y., Zhang, X., Chen, D.-Z., Huang, M.-H., Zheng, J., et al. (2013). Technological collaboration patterns in solar cell industry based on patent inventors and assignees analysis. Scientometrics, 96(2), 427–441.

    Article  Google Scholar 

  • Leydesdorff, L. (2002). Indicators of structural change in the dynamics of science: Entropy statistics of the SCI Journal Citation Reports. Scientometrics, 53(1), 131–159.

    Article  Google Scholar 

  • Leydesdorff, L., Kushnir, D., & Rafols, I. (2014). Interactive overlay maps for US patent (USPTO) data based on International Patent Classification (IPC). Scientometrics, 98(3), 1583–1599.

    Article  Google Scholar 

  • Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74, 12–32.

    Article  Google Scholar 

  • Makri, M., Hitt, M. A., & Lane, P. J. (2010). Complementary technologies, knowledge relatedness, and invention outcomes in high technology mergers and acquisitions. Strategic Management Journal, 31(6), 602–628.

    Google Scholar 

  • Mao, M., Lu, J., Zhang, G., & Zhang, J. (2016). Multirelational social recommendations via multigraph ranking. IEEE Transactions on Cybernetics. doi:10.1109/TCYB.2016.2595620.

    Google Scholar 

  • Martínez, C. (2011). Patent families: When do different definitions really matter? Scientometrics, 86(1), 39–63.

    Article  Google Scholar 

  • Martino, J. P. (2003). A review of selected recent advances in technological forecasting. Technological Forecasting and Social Change, 70(8), 719–733.

    Article  Google Scholar 

  • Meyer, M., & Tang, P. (2007). Exploring the “value” of academic patents: IP management practices in UK universities and their implications for Third-Stream indicators. Scientometrics, 70(2), 415–440.

    Article  Google Scholar 

  • Mowery, D. C., Sampat, B. N., & Ziedonis, A. A. (2002). Learning to patent: Institutional experience, learning, and the characteristics of US university patents after the Bayh–Dole Act, 1981–1992. Management Science, 48(1), 73–89.

    Article  Google Scholar 

  • Narin, F., & Hamilton, K. S. (1996). Bibliometric performance measures. Scientometrics, 36(3), 293–310.

    Article  Google Scholar 

  • Pavitt, K. (1985). Patent statistics as indicators of innovative activities: Possibilities and problems. Scientometrics, 7(1–2), 77–99.

    Article  Google Scholar 

  • Qiu, X.-Q., Wang, H., Cai, B., Wang, L.-L., & Yue, S.-T. (2007). Small antibody mimetics comprising two complementarity-determining regions and a framework region for tumor targeting. Nature Biotechnology, 25(8), 921–929.

    Article  Google Scholar 

  • Qiu, X.-Q., Wang, H., Lu, X.-F., Zhang, J., Li, S.-F., Cheng, G., et al. (2003). An engineered multidomain bactericidal peptide as a model for targeted antibiotics against specific bacteria. Nature Biotechnology, 21(12), 1480–1485.

    Article  Google Scholar 

  • Reitzig, M. (2003). What determines patent value?: Insights from the semiconductor industry. Research Policy, 32(1), 13–26.

    Article  Google Scholar 

  • Reitzig, M. (2004). Improving patent valuations for management purposes validating new indicators by analyzing application rationales. Research Policy, 33(6–7), 939–957. doi:10.1016/j.respol.2004.02.004.

    Article  Google Scholar 

  • Rip, A. (1988). Mapping of science: Possibilities and limitations. In A. F. J. van Raan (Ed.), Handbook of quantitative studies of science and technology (pp. 253–273). North-Holland: Elsevier Science Publishers B.V.

    Chapter  Google Scholar 

  • Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26.

    MathSciNet  Article  MATH  Google Scholar 

  • Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing and Management, 24(5), 513–523.

    Article  Google Scholar 

  • Sapsalis, E., de la Potterie, Bv P, & Navon, R. (2006). Academic versus industry patenting: An in-depth analysis of what determines patent value. Research Policy, 35(10), 1631–1645.

    Article  Google Scholar 

  • Schmoch, U. (1993). Tracing the knowledge transfer from science to technology as reflected in patent indicators. Scientometrics, 26(1), 193–211.

    Article  Google Scholar 

  • Schumpeter, J. A. (1939). Business cycles: A theoretical, historical, and statistical analysis of the capitalist process (Vol. 1). New York: McGraw Hill.

    Google Scholar 

  • Shambour, Q., & Lu, J. (2012). A trust-semantic fusion-based recommendation approach for e-business applications. Decision Support Systems, 54(1), 768–780.

    Article  Google Scholar 

  • Shannon, C. (1948). A mathematical theory of communication. The Bell Systems Technical Journal, 27(3), 379–423.

    MathSciNet  Article  MATH  Google Scholar 

  • Tijssen, R. J. (2001). Global and domestic utilization of industrial relevant science: Patent citation analysis of science–technology interactions and knowledge flows. Research Policy, 30(1), 35–54.

    Article  Google Scholar 

  • Verhoeven, D., Bakker, J., & Veugelers, R. (2016). Measuring technological novelty with patent-based indicators. Research Policy, 45(3), 707–723. doi:10.1016/j.respol.2015.11.010.

    Article  Google Scholar 

  • Vinkler, P. (2013). Comparative rank assessment of journal articles. Journal of Informetrics, 7(3), 712–717. doi:10.1016/j.joi.2013.04.006.

    Article  Google Scholar 

  • Von Wartburg, I., Teichert, T., & Rost, K. (2005). Inventive progress measured by multi-stage patent citation analysis. Research Policy, 34(10), 1591–1607.

    Article  Google Scholar 

  • Waltman, L., Calero-Medina, C., Kosten, J., Noyons, E., Tijssen, R. J., Eck, N. J., et al. (2012). The Leiden Ranking 2011/2012: Data collection, indicators, and interpretation. Journal of the American Society for Information Science and Technology, 63(12), 2419–2432.

    Article  Google Scholar 

  • Wang, S.-J. (2007). Factors to evaluate a patent in addition to citations. Scientometrics, 71(3), 509–522.

    Article  Google Scholar 

  • Wang, B., & Hsieh, C.-H. (2015). Measuring the value of patents with fuzzy multiple criteria decision making: Insight into the practices of the Industrial Technology Research Institute. Technological Forecasting and Social Change, 92, 263–275. doi:10.1016/j.techfore.2014.09.015.

    Article  Google Scholar 

  • Xu, H., Martin, E., & Mahidadia, A. (2014). Contents and time sensitive document ranking of scientific literature. Journal of Informetrics, 8(3), 546–561. doi:10.1016/j.joi.2014.04.006.

    Article  Google Scholar 

  • Yoon, J., Park, H., & Kim, K. (2013). Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis. Scientometrics, 94(1), 313–331.

    Article  Google Scholar 

  • Zhang, Y., Porter, A. L., Hu, Z., Guo, Y., & Newman, N. C. (2014a). “Term clumping” for technical intelligence: A case study on dye-sensitized solar cells. Technological Forecasting and Social Change, 85, 26–39.

    Article  Google Scholar 

  • Zhang, Y., Shang, L., Huang, L., Porter, A. L., Lu, J., & Zhu, D. (2016). A hybrid similarity measure method for patent portfolio analysis Journal of Informetrics.. doi:10.1016/j.joi.2016.09.006.

    Google Scholar 

  • Zhang, Y., Zhou, X., Porter, A. L., Gomila, J. M. V., & Yan, A. (2014b). Triple Helix innovation in China’s dye-sensitized solar cell industry: Hybrid methods with semantic TRIZ and technology roadmapping. Scientometrics, 99(1), 55–75.

    Article  Google Scholar 

Download references

Acknowledgement

We acknowledge support from the Australian Research Council (ARC) under Discovery Project DP140101366, and the National Science Foundation of China under Grants 71503020 and 71673024. We are also grateful to the anonymous reviewers for addressing critical but valuable comments on the evaluation of patent indicators.

Author information

Authors and Affiliations

  1. Decision Systems and E-Service Intelligence Research Lab, Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia

    Yi Zhang, Guangquan Zhang & Jie Lu

  2. School of Management and Economics, Beijing Institute of Technology, Beijing, People’s Republic of China

    Yue Qian, Ying Huang & Ying Guo

Authors
  1. Yi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yue Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ying Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guangquan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jie Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ying Guo.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Qian, Y., Huang, Y. et al. An entropy-based indicator system for measuring the potential of patents in technological innovation: rejecting moderation. Scientometrics 111, 1925–1946 (2017). https://doi.org/10.1007/s11192-017-2337-7

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-017-2337-7

Keywords

  • Patent analysis
  • Indicator system
  • Bibliometrics
  • Technological innovation
  • Entropy