Leveraging patent classification based on deep learning: The case study on smart cities and industrial Internet of Things

Abstract

With the trends of technology convergence and technology interdisciplinarity, technology-field (TF) resolution and classification of patents have gradually been challenged. Whether for patent applicants or for patent examiners, more precisely labeling the TF for a certain patent is important for technological searches. However, determining the TF of a patent may be difficult and may even involve the strategic behavior of patenting, which can cause noise in patent classification systems (PCSs). In addition, some specific patents could contain more TFs than claimed or be assigned questionable IPC codes; subsequently, in a regular search for technology/patents, information could be missed. Considering the advantages of deep learning compared with traditional machine learning algorithms in areas such as natural language processing (NLP), text classification and text sentiment analysis, this paper investigates several popular deep learning models and proposes a large-scale multilabel regression (MLR) model to handle specific patent analyses under situations of small sample learning. To verify the proposed MLR model for patent classification, the case study on smart cities and industrial Internet of Things (IIoT) is conducted. The MLR experiments on the TF resolution of smart cities and IIoT have yielded moderate results compared with those of the latest patent classification studies, which also rely on deep learning and the large language models (LLMs), which include RCNN, Bi-LSTM, BERT and GPT-4 etc. Therefore, the proposed MLR model with a customized loss function could be moderately effective for patent classification within a specific technology theme, could have implications for patent classification and the TF resolution of patents, and could further enrich methodologies for patent mining and informetrics based on artificial intelligence (AI).