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How Can Innovation Screening Be Improved? A Machine Learning Analysis with Economic Consequences for Firm Performance
Published online by Cambridge University Press: 28 March 2025
Abstract
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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.
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.- Copyright
- © The Author(s), 2025. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington
Footnotes
I am grateful to Kai Li (the editor) and an anonymous referee for the constructive comments in the review process. I also thank Thomas Chemmanur, Francesco D’Acunto, Ran Duchin, Slava Fos, Edith Hotchkiss, Seung Jung Lee, Svetlana Petrova, Jonathan Reuter, Alberto Rossi, Yiming Qian, Ronnie Sadka, Philip Strahan, Wanli Zhao, and conference participants at the 2022 American Economic Association Annual Meeting, the 2020 Doctoral Consortium of the Financial Management Association Annual Meeting, the 2020 Southern Finance Association Annual Meeting, the 2021 Eastern Finance Association Annual Meeting, the 2021 Financial Management Association Annual Meeting, the 2022 Midwest Finance Association Annual Meeting, and seminar participants at Boston College, California State University at Fullerton, Chinese University of Hong Kong (Hong Kong & Shenzhen), City University of Hong Kong, Durham University, Fudan University, Georgetown University Global Virtual Seminar Series on Fintech, Luohan Academy, Oregon State University, Peking University, San Diego State University, Shanghai Jiaotong University, Shanghai University of Finance and Economics, University of Connecticut, and University of Florida for helpful comments. Any errors and omissions remain my own responsibility.
References
Abis, S. “Man vs. Machine: Quantitative and Discretionary Equity Management.” Working Paper, Columbia Business School (2017).Google Scholar
Arrow, K. J. “Economic Welfare and the Allocation of Resources for Invention.” In Readings in Industrial Economics, Vol 1, Rowley, C. K., ed. Berlin, Germany: Springer (1972), 219–236.Google Scholar
Arts, S.; Cassiman, B.; and Gomez, J. C.. “Text Matching to Measure Patent Similarity.” Strategic Management Journal, 39 (2018), 62–84.Google Scholar
Athey, S., and Imbens, G. W.. “The State of Applied Econometrics: Causality and Policy Evaluation.” Journal of Economic Perspectives, 31 (2017), 3–32.Google Scholar
Bessen, J., and Maskin, E.. “Sequential Innovation, Patents, and Imitation.” RAND Journal of Economics, 40 (2009), 611–635.Google Scholar
Bessen, J. E., and Meurer, M. J.. “Patent Litigation with Endogenous Disputes.” American Economic Review, 96 (2006), 77–81.Google Scholar
Bowen, D. E. III; Frésard, L.; and Hoberg, G.. “Rapidly Evolving Technologies and Startup Exits.” Management Science, 69 (2023), 940–967.Google Scholar
Chemmanur, T. J.; Gupta, M.; and Simonyan, K.. “Top Management Team Quality and Innovation in Venture-Backed Private Firms and IPO Market Rewards to Innovative Activity.” Entrepreneurship Theory and Practice, 46 (2022), 920–951.Google Scholar
Chen, T., and Guestrin, C.. “Xgboost: A Scalable Tree Boosting System.” In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM (2016) 785–794.Google Scholar
Choi, J. P. “Patent Litigation as an Information-Transmission Mechanism.” American Economic Review, 88 (1998), 1249–1263.Google Scholar
Choudhury, P.; Starr, E.; and Agarwal, R.. “Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation.” Strategic Management Journal, 41 (2020), 1381–1411.Google Scholar
Cohen, L.; Diether, K.; and Malloy, C.. “Misvaluing Innovation.” Review of Financial Studies, 26 (2013), 635–666.Google Scholar
Cornelli, F., and Schankerman, M.. “Patent Renewals and R&D Incentives.” RAND Journal of Economics, 30 (1999), 197–213.Google Scholar
deGrazia, C.; Pairolero, N. A.; and Teodorescu, M.. “Shorter Patent Pendency Without Sacrificing Quality: The Use of Examiner’s Amendments at the USPTO.” Research Policy, 50 (2021), 2019-03.Google Scholar
Dreyfuss, R. C. “Nonobviousness: A Comment on Three Learned Papers.” Lewis & Clark Law Review, 12 (2008), 431.Google Scholar
Duffy, J. F. “A Timing Approach to Patentability.” Lewis & Clark Law Review, 12 (2008), 343.Google Scholar
Eberhart, A. C.; Maxwell, W. F.; and Siddique, A. R.. “An Examination of Long-Term Abnormal Stock Returns and Operating Performance Following R&D Increases.” Journal of Finance, 59 (2004), 623–650.Google Scholar
Eisenberg, R. S. “Pharma’s Nonobvious Problem.” Lewis & Clark Law Review, 12 (2008), 375.Google Scholar
Erel, I.; Stern, L. H.; Tan, C.; and Weisbach, M. S.. “Selecting Directors Using Machine Learning.” Review of Financial Studies, 34 (2021), 3226–3264.Google Scholar
Farre-Mensa, J.; Hegde, D.; and Ljungqvist, A.. “What Is a Patent Worth? Evidence from the US Patent “Lottery”.” Journal of Finance, 75 (2020), 639–682.Google Scholar
Feng, J., and Jaravel, X.. “Crafting Intellectual Property Rights: Implications for Patent Assertion Entities, Litigation, and Innovation.” American Economic Journal: Applied Economics, 12 (2020), 140–181.Google Scholar
Fitzgerald, T.; Balsmeier, B.; Fleming, L.; and Manso, G.. “Innovation Search Strategy and Predictable Returns.” Management Science, 67 (2021), 1109–1137.Google Scholar
Frakes, M. D., and Wasserman, M. F.. “Does the U.S. Patent and Trademark Office Grant Too Many Bad Patents? Evidence from a Quasi-Experiment.” Stanford Law Review, 67 (2015), 613.Google Scholar
Frakes, M. D., and Wasserman, M. F.. “Is the Time Allocated to Review Patent Applications Inducing Examiners to Grant Invalid Patents? Evidence from Microlevel Application Data.” Review of Economics and Statistics, 99 (2017), 550–563.Google Scholar
Friedman, J. H. “Greedy Function Approximation: A Gradient Boosting Machine.” Annals of Statistics, 29 (2001), 1189–1232.Google Scholar
Gilbert, R., and Shapiro, C.. “Optimal Patent Length and Breadth.” RAND Journal of Economics, 21 (1990), 106–112.Google Scholar
Graham, S. J.; Marco, A. C.; and Miller, R.. “The USPTO Patent Examination Research Dataset: A Window on Patent Processing.” Journal of Economics & Management Strategy, 27 (2018), 554–578.Google Scholar
Gu, S.; Kelly, B.; and Xiu, D.. “Empirical Asset Pricing via Machine Learning.” Review of Financial Studies, 33 (2020), 2223–2273.Google Scholar
Hall, B. H.; Jaffe, A.; and Trajtenberg, M.. “Market Value and Patent Citations.” RAND Journal of Economics, 36 (2005), 16–38.Google Scholar
Hall, B. H.; Jaffe, A. B.; and Trajtenberg, M.. “The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools.” NBER Working Paper No. 8498 (2001).Google Scholar
Heller, M. A., and Eisenberg, R. S.. “Can Patents Deter Innovation? The Anticommons in Biomedical Research.” Science, 280 (1998), 698–701.Google Scholar
Hirschman, A. O. National Power and the Structure of Foreign Trade, Vol. 105. Berkeley, CA: University of California Press (1980).Google Scholar
Jaffe, A. B., and Lerner, J.. Innovation and Its Discontents: How Our Broken Patent System Is Endangering Innovation and Progress, and What to Do About It. Princeton, NJ: Princeton University Press (2011).Google Scholar
Kleinberg, J.; Lakkaraju, H.; Leskovec, J.; Ludwig, J.; and Mullainathan, S.. “Human Decisions and Machine Predictions.” Quarterly Journal of Economics, 133 (2017), 237–293.Google Scholar
Kleinberg, J.; Ludwig, J.; Mullainathan, S.; and Obermeyer, Z.. “Prediction Policy Problems.” American Economic Review, 105 (2015), 491–495.Google Scholar
Kline, P.; Petkova, N.; Williams, H.; and Zidar, O.. “Who Profits from Patents? Rent-Sharing at Innovative Firms.” Quarterly Journal of Economics, 134 (2019), 1343–1404.Google Scholar
Kogan, L.; Papanikolaou, D.; Seru, A.; and Stoffman, N.. “Technological Innovation, Resource Allocation, and Growth.” Quarterly Journal of Economics, 132 (2017), 665–712.Google Scholar
Krishna, A. M.; Feldman, B.; Wolf, J.; Gabel, G.; Beliveau, S.; and Beach, T.. “User Interface for Customizing Patents Search: An Exploratory Study.” International Conference on Human-Computer Interaction. Berlin, Germany: Springer (2016), 264–269.Google Scholar
Lanjouw, J. O., and Schankerman, M.. “Characteristics of Patent Litigation: A Window on Competition.” RAND Journal of Economics, 32 (2001), 129–151.Google Scholar
Lemley, M. A., and Shapiro, C.. “Probabilistic Patents.” Journal of Economic Perspectives, 19 (2005), 75–98.Google Scholar
Lu, Q.; Myers, A.; and Beliveau, S.. “USPTO Patent Prosecution Research Data: Unlocking Office Action Traits.” USPTO Economic Working Paper No. 10 (2017).Google Scholar
Maestas, N.; Mullen, K. J.; and Strand, A.. “Does Disability Insurance Receipt Discourage Work? Using Examiner Assignment to Estimate Causal Effects of SSDI Receipt.” American Economic Review, 103 (2013), 1797–1829.Google Scholar
Mandel, G. N. “Another Missed Opportunity: The Supreme Court’s Failure to Define Nonobviousness or Combat Hindsight Bias in KSR v. Teleflex.” Lewis & Clark Law Review, 12 (2008), 323.Google Scholar
Mansfield, E. “Patents and Innovation: An Empirical Study.” Management Science, 32 (1986), 173–181.Google Scholar
Marco, A.; Carley, M.; Jackson, S.; and Myers, A.. “The USPTO Historical Patent Data Files: Two Centuries of Invention.” USPTO Economic Working Paper No. 2015-1 (2015a).Google Scholar
Marco, A. C.; Myers, A.; Graham, S. J.; D’Agostino, P.; and Apple, K.. “The USPTO Patent Assignment Dataset: Descriptions and Analysis.” USPTO Economic Working Paper No. 2015-2 (2015b).Google Scholar
Marco, A. C.; Sarnoff, J. D.; and Charles, A.. “Patent Claims and Patent Scope.” Research Policy, 48 (2019), 103,790.Google Scholar
Marco, A. C.; Toole, A. A.; Miller, R.; and Frumkin, J.. “USPTO Patent Prosecution and Examiner Performance Appraisal.” USPTO Economic Working Paper No. 2017-08 (2017).Google Scholar
Matutes, C.; Regibeau, P.; and Rockett, K.. “Optimal Patent Design and the Diffusion of Innovations.” The RAND Journal of Economics, 27 (1996), 60–83.Google Scholar
Merges, R. P. “As Many as Six Impossible Patents Before Breakfast: Property Rights for Business Concepts and Patent System Reform.” Berkeley Technology Law Journal, 14 (1999), 577.Google Scholar
Meurer, M. J. “The Settlement of Patent Litigation.” RAND Journal of Economics, 20 (1989), 77–91.Google Scholar
Mikolov, T.; Chen, K.; Corrado, G.; and Dean, J.. “Efficient Estimation of Word Representations in Vector Space.” arXiv preprint arXiv:1301.3781 (2013a).Google Scholar
Mikolov, T.; Le, Q. V.; and Sutskever, I.. “Exploiting Similarities Among Languages for Machine Translation.” arXiv preprint arXiv:1309.4168 (2013b).Google Scholar
Mikolov, T.; Yih, W.-T; and Zweig, G.. “Linguistic Regularities in Continuous Space Word Representations.” In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2013c) 746–751.Google Scholar
Mullainathan, S., and Spiess, J.. “Machine Learning: An Applied Econometric Approach.” Journal of Economic Perspectives, 31 (2017), 87–106.Google Scholar
Nordhaus, W. D. “An Economic Theory of Technological Change.” American Economic Review, 59 (1969), 18–28.Google Scholar
Righi, C., and Simcoe, T.. “Patent Examiner Specialization.” Research Policy, 48 (2019), 137–148.Google Scholar
Rokach, L., and Maimon, O. Z.. Data Mining with Decision Trees: Theory and Applications, 2nd edition. Singapore: World Scientific (2008).Google Scholar
Rossi, A. “Predicting Stock Market Returns with Machine Learning.” Working Paper, Georgetown University (2018).Google Scholar
Sampat, B., and Williams, H. L.. “How Do Patents Affect Follow-On Innovation? Evidence from the Human Genome.” American Economic Review, 109 (2019), 203–236.Google Scholar
Schankerman, M., and Schuett, F.. “Patent Screening, Innovation, and Welfare.” Review of Economic Studies, 89 (2022), 2101–2148.Google Scholar
Scherer, F. M. “Nordhaus’ Theory of Optimal Patent Life: A Geometric Reinterpretation.” American Economic Review, 62 (1972), 422–427.Google Scholar
Shapley, L. S. “A Value for n-Person Games.” Contribution to the Theory of Games, 2 (1953), 307–317.Google Scholar
Shu, T.; Tian, X.; and Zhan, X.. “Patent Quality, Firm Value, and Investor Underreaction: Evidence from Patent Examiner Busyness.” Journal of Financial Economics, 143 (2022), 1043–1069.Google Scholar
Tong, X., and Frame, J. D.. “Measuring National Technological Performance with Patent Claims Data.” Research Policy, 23 (1994), 133–141.Google Scholar
Trajtenberg, M. “A Penny for Your Quotes: Patent Citations and the Value of Innovations.” RAND Journal of Economics, 21 (1990), 172–187.Google Scholar
Trajtenberg, M.; Henderson, R.; and Jaffe, A.. “University Versus Corporate Patents: A Window on the Basicness of Invention.” Economics of Innovation and New Technology, 5 (1997), 19–50.Google Scholar
Zucker, L. G.; Darby, M. R.; and Armstrong, J. S.. “Commercializing Knowledge: University Science, Knowledge Capture, and Firm Performance in Biotechnology.” Management Science, 48 (2002), 138–153.Google Scholar
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