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| Early Identification of Disruptive Technology Using Weak Signals |
| Liu Junwan, Pang Bo, Xu Shuo |
| College of Economics and Management, Beijing University of Technology, Beijing 100124 |
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Abstract An efficient set of techniques and ideas for the early identification of disruptive technologies is provided by combining a patent-based weak signal detection model and a disruptive technology potential measuring system. First, the topics of patent contents are extracted using from domain patent information using the latent Dirichlet allocation (LDA) topic model. We then use the weak function to filter the topics to collect the topics containing weak signals. The terms contained in the weak-signal topics are further filtered by a prognosis function to obtain the set of weak-signal terms and their corresponding patent collections to be related to their respective domains. Second, the technology disruptive potential measurement index system is applied to measure the disruptive potential of patents containing weak-signal terms. Finally, we obtain the collection of technologies with disruptive potential, which can serve as a reference for the early identification of disruptive technologies in the target domain. We apply this procedure to patent data in the incoPat patent database in gene editing research during the period 2008-2019 to identify the technologies with disruptive potential in this domain. After comparing the results with the weak-signal results based on a keyword text mining procedure, the above identification process and the results of weak signals are verified using CRISPR/Cas9 technology, demonstrating the feasibility and effectiveness of this method.
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Received: 19 September 2022
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