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A survival analysis of the approval of US patent applications

Author

Listed:
  • Ying Xie
  • David Giles

Abstract

We model the length of time that it takes for a patent application to be granted by the US Patent and Trademark Office (USPTO), conditional on the patent actually being awarded eventually. Survival analysis is applied and both the nonparametric Kaplan-Meier and parametric accelerated failure time models are used to analyse the data. We find that the number of claims a patent makes, the number of citations a patent makes, the patent's technological category, and the type of applicant all have significant effects on the duration that a patent is under consideration. A log-normal survival model is the preferred parametric specification, and the results suggest that the hazard function is nonmonotonic over time.

Suggested Citation

  • Ying Xie & David Giles, 2011. "A survival analysis of the approval of US patent applications," Applied Economics, Taylor & Francis Journals, vol. 43(11), pages 1375-1384.
    • Handle: RePEc:taf:applec:v:43:y:2011:i:11:p:1375-1384
    DOI: 10.1080/00036840802600418
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    Citations

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    Cited by:

    1. Gupeng, Zhang & Xiangdong, Chen, 2012. "The value of invention patents in China: Country origin and technology field differences," China Economic Review, Elsevier, vol. 23(2), pages 357-370.
    2. Kenneth Zahringer & Christos Kolympiris & Nicholas Kalaitzandonakes, 2018. "Time to patent at the USPTO: the case of emerging entrepreneurial firms," The Journal of Technology Transfer, Springer, vol. 43(4), pages 923-952, August.
    3. Ting Meng & Richard Carew & Wojciech J. Florkowski, 2020. "Determinants of the grant lag and the surrender lag of horticultural crop plant breeders’ rights applications: Survival analysis with competing risks," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 68(4), pages 489-512, December.
    4. Aka, Joël, 2017. "Market approval of phytosanitary active substances in Europe: An empirical duration analysis," Food Policy, Elsevier, vol. 68(C), pages 143-153.
    5. Guijie Zhang & Guang Yu & Yuqiang Feng & Luning Liu & Zhenhua Yang, 2017. "Improving the publication delay model to characterize the patent granting process," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 621-637, May.
    6. Vinita Krishna & Sudhir K. Jain, 2025. "Patent-based indicators for predicting patent commercialization: a study of the diffusion of pharmaceutical innovations," The Journal of Technology Transfer, Springer, vol. 50(1), pages 114-137, February.
    7. Guijie Zhang & Yuqiang Feng & Guang Yu & Luning Liu & Yanqiqi Hao, 2017. "Analyzing the time delay between scientific research and technology patents based on the citation distribution model," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1287-1306, June.
    8. Andrew Eckert & Corinne Langinier, 2014. "A Survey Of The Economics Of Patent Systems And Procedures," Journal of Economic Surveys, Wiley Blackwell, vol. 28(5), pages 996-1015, December.
    9. Garg, Sam & Howard, Michael & Pahnke, Emily Cox, 2025. "Directors in new technology-based ventures: An empirical inquiry," Journal of Business Venturing, Elsevier, vol. 40(2).
    10. Youngho Kim & Sangsung Park & Junseok Lee & Dongsik Jang & Jiho Kang, 2021. "Integrated Survival Model for Predicting Patent Litigation Hazard," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    11. Zhang, Gupeng & Xiong, Libin & Duan, Hongbo & Huang, Dujuan, 2020. "Obtaining certainty vs. creating uncertainty: Does firms’ patent filing strategy work as expected?," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    12. Davit Khachatryan & Brigitte Muehlmann, 2017. "Determinants of successful patent applications to combat financial fraud," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1353-1383, June.
    13. Yang, Haodong & Zhang, Yumei & Liu, Li, 2024. "Patent priority examination under mission orientation: can speed and quality coexist?," Technology in Society, Elsevier, vol. 76(C).
    14. Li Yao & He Ni, 2023. "Prediction of patent grant and interpreting the key determinants: an application of interpretable machine learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 4933-4969, September.

    More about this item

    JEL classification:

    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C29 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Other
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General

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