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1. Early Disclosure of Invention and Reduced Duplication: An Empirical Test

   https://doi.org/10.1287/mnsc.2019.3521  

   Lück, Sonja; Balsmeier, Benjamin; Seliger, Florian; Fleming, Lee (June 2020, Management Science)

   Much work on innovation strategy assumes or theorizes that competition in innovation elicits duplication of research and that disclosure decreases such duplication. We validate this empirically using the American Inventors Protection Act (AIPA), three complementary identification strategies, and a new measure of blocked future patent applications. We show that AIPA—intended to reduce duplication, through default disclosure of patent applications 18 months after filing—reduced duplication in the U.S. and European patent systems. The blocking measure provides a clear and micro measure of technological competition that can be aggregated to facilitate the empirical investigation of innovation, firm strategy, and the positive and negative externalities of patenting. This paper was accepted by Joshua Gans, business strategy. 

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2. Partisan patent examiners? Exploring the link between the political ideology of patent examiners and patent office outcomes

   https://doi.org/10.1016/j.respol.2023.104853  

   Raffiee, Joseph; Teodoridis, Florenta; Fehder, Daniel (November 2023, Research Policy)

   Patents are key strategic resources which enable firms to appropriate innovation returns and prevent rival imitation. Patent examiners – individuals who may be subject to various sources of bias – play a central role in determining which inventions are awarded patent rights. Using a novel dataset, we explore if one increasingly prevalent source of bias – political ideology – manifests in examiner decision-making. Reassuringly, our analysis suggests that the political ideology of patent examiners is largely unrelated to patent office outcomes. However, we do find evidence suggesting politically active conservative-leaning examiners are more likely to grant patents relative to politically active liberal-leaning examiners, but only for patent applications where there is ambiguity regarding what constitutes patentable subject matter and hence examiners have greater discretion. 

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3. LDA v. LSA: A Comparison of Two Computational Text Analysis Tools for the Functional Categorization of Patents

   Cvitanic, T.; Lee, B.; Song, H. I.; Fu, K.; Rosen, D. (January 2016, International Conference on Case-Based Reasoning)

   One means to support for design-by-analogy (DbA) in practice involves giving designers efficient access to source analogies as inspiration to solve problems. The patent database has been used for many DbA support efforts, as it is a preexisting repository of catalogued technology. Latent Semantic Analysis (LSA) has been shown to be an effective computational text processing method for extracting meaningful similarities between patents for useful functional exploration during DbA. However, this has only been shown to be useful at a small-scale (100 patents). Considering the vastness of the patent database and realistic exploration at a large scale, it is important to consider how these computational analyses change with orders of magnitude more data. We present analysis of 1,000 random mechanical patents, comparing the ability of LSA to Latent Dirichlet Allocation (LDA) to categorize patents into meaningful groups. Resulting implications for large(r) scale data mining of patents for DbA support are detailed. 

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4. Automated neural patent landscaping in the small data regime using citations and CPC codes

   https://doi.org/10.1007/s10506-025-09483-5  

   Islam_Erana, Tisa; Finlayson, Mark A (October 2025, Artificial Intelligence and Law)

   Patent landscaping is the process of identifying all patents related to a particular technological area, and is important for assessing various aspects of the intellectual property context. Traditionally, constructing patent landscapes is intensely laborious and expensive, and the rapid expansion of patenting activity in recent decades has driven an increasing need for efficient and effective automated patent landscaping approaches. In particular, it is critical that we be able to construct patent landscapes using a minimal number of labeled examples, as labeling patents for a narrow technology area requires highly specialized (and hence expensive) technical knowledge. We present an automated neural patent landscaping system that demonstrates significantly improved performance on difficult examples (0.69 on ‘hard’ examples, versus 0.6 for previously reported systems), and also significant improvements with much less training data (overall 0.75 on as few as 24 examples). Furthermore, in evaluating such automated landscaping systems, acquiring good data is challenge; we demonstrate a higher-quality training data generation procedure by merging (Abood and Feltenberger Artif Intell Law 26:103–125 2018) “seed/anti-seed” approach with active learning to collect difficult labeled examples near the decision boundary. Using this procedure we created a new dataset of labeled AI patents for training and testing. As in prior work we compare our approach with a number of baseline systems, and we release our code and data for others to build upon “(Code and data may be downloaded from https://doi.org/10.34703/gzx1-9v95/QDLKVWCode and data are released under the Creative Commons NC-BY 4.0 license at https://creativecommons.org/licenses/by-nc/4.0/)”. 

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5. Reliance on science: Worldwide front‐page patent citations to scientific articles

   https://doi.org/10.1002/smj.3145  

   Marx, Matt; Fuegi, Aaron (April 2020, Strategic Management Journal)

   Abstract <italic>Research summary</italic>To what extent do firms rely on basic science in their R&D efforts? Several scholars have sought to answer this and related questions, but progress has been impeded by the difficulty of matching unstructured references in patents to published papers. We introduce an open‐access dataset of references from the front pages of patents granted worldwide to scientific papers published since 1800. Each patent‐paper linkage is assigned a confidence score, which is characterized in a random sample by false negatives versus false positives. All matches are available for download athttp://relianceonscience.org. We outline several avenues for strategy research enabled by these new data. <italic>Managerial summary</italic>To what extent do firms rely on basic science in their R&D efforts? Several scholars have sought to answer this and related questions, but progress has been impeded by the difficulty of matching unstructured references in patents to published papers. We introduce an open‐access dataset of references from the front pages of patents granted worldwide to scientific papers published since 1800. Each patent‐paper linkage is assigned a confidence score, and we check a random sample of these confidence scores by hand in order to estimate both coverage (i.e., of the matches we should have found, what percentage did we find) and accuracy (i.e., of the matches we found, what percentage are correct). We outline several avenues for strategy research enabled by these new data. 

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