IPC Multi-label Classification Applying the Characteristics of Patent Documents

Most of research on the IPC automatic classification system has focused on applying various existing machine learning methods to the patent documents rather than considering the characteristics of the data or the structure of the patent documents. This paper, therefore, proposes using two structural fields, a technical field and a background field which are selected by applying the characteristics of patent documents and the role of the structural fields. A multi-label classification model is also constructed to reflect that a patent document could have multiple IPCs and to classify patent documents at an IPC subclass level comprised of 630 categories. The effects of the structural fields of the patent documents are examined using 564,793 registered patents in Korea. An 87.2 % precision rate is obtained when using the two fields mainly. From this sequence, it is verified that the technical field and background field play an important role in improving the precision of IPC multi-label classification at the IPC subclass level.