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In this ever-changing technological landscape, the ability to quickly predict technological trends becomes crucial for any company or institute engaged in informed decision-making and strategic planning. Data for predicting technological trends can come from various sources such as patent data, which is easily accessible to the public due to the nature of patents. This research is aimed at patent analysis, focusing on combining the keyword- based method, social network analysis (SNA) method, and neural network prediction to propose a feasible keyword trend prediction method based on patent analysis by targeting upcoming keyword trends. More specifically, we utilize Long Short-Term Memory (LSTM) to predict changes in keyword frequency using keyword centralities as input. To assess the effectiveness of the proposed method, we constructed the input dataset using the USPTO patent database in the Information and Communication Technology (ICT) field. We then experimented to compare the proposed method with the benchmark method. Furthermore, to counteract the unbalanced nature of patent data, the SMOGN method is introduced. The results demonstrate its potential for application in broader contexts.
Yodprechavigit et al. (Sat,) studied this question.