Patent Classification

Patent classification, the process of assigning standardized codes to patents based on their technological content, aims to improve patent searching, analysis, and management. Current research focuses on automating this process using deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs like Bi-LSTMs), and transformer-based architectures (like BERT variants), often incorporating explainable AI techniques to enhance transparency and trust. These advancements leverage not only patent text but also metadata like assignee information and hierarchical classification structures to improve accuracy and predict future patent application trends. The resulting improvements in efficiency and accuracy have significant implications for intellectual property management, technological forecasting, and competitive intelligence.