Before revision:  Patent classification is a multi-label classification task in which the number of labels can be greater than 640 at the subclass level. The proposed framework predicts individual input patent class and subclass based on finding top k semantic similarity patents. Design/Methodology/Approach: The study uses transformer models based on Augmented SBERT and RoBERTa.
After revision:  To further increase the patent embedding quality, we use transformer models based on Augmented SBERT and RoBERTa.