[Objective] This paper reviews research on detecting patent infringements, aiming to provide theoretical frameworks and development trends for future studies. [Coverage] We retrieved 53 representative literatures from CNKI and Bing Scholar using the keywords of “Patent Infringement” or “Patent Similarity”. [Methods] First, we summarized the methods for detecting patent infringement based on clustering, vector space model, SAO (Subject-Action-Object) structure, deep learning and patent structure. Then, we compared the advantages and disadvantages of popular methods for detecting patent infringements. Finally, we explored some possible optimization solutions for the existing methods. [Results] Patent infringement detection aims to retrieve small number of patents with higher risks of infringement from a large number of patent documents. It reduces the number of patents requiring manual judgments. Our method decides the risk of patent infringement by calculating their similarities based on statistical information of different granularities. [Limitations] Due to the lack of standard data sets, we could not quantitatively compare the methods for detecting patent infringements. [Conclusions] We could optimize patent infringement detection with pre-training models, calculating similarity of different patent components, and constructing high-quality data sets.
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