  Purpose: This study aims to provide a hybrid approach for patent claim
classification with Sentence-BERT (SBERT) and K Nearest Neighbours (KNN) and
explicitly focuses on the patent claims. 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. We use a different approach to predict patent
classification by finding top k similar patent claims and using the KNN
algorithm to predict patent class or subclass. Besides, in this study, we just
focus on patent claims, and in the future study, we add other appropriate parts
of patent documents. Findings: The findings suggest the relevance of hybrid
models to predict multi-label classification based on text data. In this
approach, we used the Transformer model as the distance function in KNN, and
proposed a new version of KNN based on Augmented SBERT. Practical Implications:
The presented framework provides a practical model for patent classification.
In this study, we predict the class and subclass of the patent based on
semantic claims similarity. The end-user interpretability of the results is one
of the essential positive points of the model. Originality/Value: The main
contribution of the study included: 1) Using the Augmented approach for
fine-tuning SBERT by in-domain supervised patent claims data. 2) Improving
results based on a hybrid model for patent classification. The best result of
F1-score at the subclass level was > 69%) Proposing the practical model with
high interpretability of results.
