Before revision:  Design/Methodology/Approach: The study uses transformer models based on Augmented SBERT and RoBERTa. We use a different approach to predict patent classification by finding top k similar patent claims and using the KNNalgorithm to predict patent class or subclass.
After revision:  Design/Methodology/Approach: The study uses transformer models based on SBERT and RoBERT, and apply the augmented approach for fine-tuning SBERT by in-domain supervised patent claims data. We leverage SBERTs efficiency in creating embedding distance measures to map p2p similarity in large sets of patent data. We deploy our framework for classification with a simple Nearest Neighbors (KNN) model that predicts Cooperative Patent Classification (CPC) of a patent based on the CPC assignment of the K patents with the highest p2p similarity. We thereby validate that p2p similarity captures their technological features in terms of CPC overlap, and at the same demonstrate the usefulness of this approach for automatic patent classification by finding top k similar patent claims and using the KNNalgorithm to predict patent class or subclass.