A patent infringement early-warning methodology based on intuitionistic fuzzy sets: A case study of Huawei

In the era of technology-driven economies, patent infringement has become one of the main risks faced by companies, which exists in all stages of technological innovation. However, the increasing size of patent information as well as the inherent fuzziness of patent infringement risk make the early warning of this risk a knowledge-intensive engineering activity. In this study, a novel patent infringement early warning methodology based on intuitionistic fuzzy sets (IFSs) is proposed to accurately evaluate and classify patent infringement risk for its management. First, a hierarchical indicator system of the methodology is established, including indicators of regional judicial and administrative protection. Then entropy weights for IFSs and intuitionistic fuzzy weighted geometric (IFWG) operators are utilized to objectively and automatically aggregate indicator data on early warning patents and their similar patents to evaluate IFS results, which is a multi-layer data processing structure. Finally, the normalized Euclidean distances are used to classify risk levels. In a case study, Huawei's historical patents are taken as the test data, and the methodology is verified by comparing the output results and classification with the actual litigation status. Managerial implications for design engineers and patent attorneys are discussed corresponding to various technological innovation stages.