IDEAS home Printed from https://ideas.repec.org/a/cup/jfinqa/v60y2025i8p3722-3752_4.html

How Can Innovation Screening Be Improved? A Machine Learning Analysis with Economic Consequences for Firm Performance

Author

Listed:
  • Zheng, Xiang

Abstract

This study utilizes U.S. Patent Office data to explore potential improvements in the patent examination process through machine learning. It shows that integrating machine learning with human expertise can increase patent citations by up to 26%. Using machine learning predictions as benchmarks, I find that the early expiration rate of granted patents positively correlates with examiners’ false acceptance rates. These errors negatively impact public companies’ operational performance and reduce successful IPO or M&A exits for private firms. Overall, this study highlights significant social and economic benefits of incorporating machine learning as a robo-advisor in patent screening.

Suggested Citation

  • Zheng, Xiang, 2025. "How Can Innovation Screening Be Improved? A Machine Learning Analysis with Economic Consequences for Firm Performance," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 60(8), pages 3722-3752, December.
    • Handle: RePEc:cup:jfinqa:v:60:y:2025:i:8:p:3722-3752_4
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0022109025000213/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cup:jfinqa:v:60:y:2025:i:8:p:3722-3752_4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/jfq .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.