Before revision:  The best result of F1-score at the subclass level was > 69\%) Proposing the practical model with high interpretability of results .
After revision:  The best result of F1-score at the subclass level was > 63\%, which suggests that our model outperforms the current state-of-the-art in text-based multi-label and multi-class patent classification by a margin of > 18\% F1 score. We furthermore discuss the applicability of the presented framework for semantic IP search, patent landscaping, and technology intelligence. We finally point towards a future research agenda for leveraging multi-source patent embeddings, their appropriateness across applications, as well as to improve and validate patent embeddings by creating domain-expert curated Semantic Textual Similarity (STS) benchmark datasets .