Automated Single-Label Patent Classification using Ensemble Classifiers

TL;DR

This paper introduces an innovative ensemble classifier approach for automatic patent classification, significantly improving accuracy over existing methods by combining multiple classifiers trained on different parts of patent documents.

Contribution

It proposes the first ensemble classifier method for patent classification, enhancing accuracy by integrating diverse classifiers trained on various document segments.

Findings

Ensemble classifiers outperform single classifiers in patent classification accuracy.

The method achieves promising results close to expert human performance.

Different feature representations contribute to improved classification performance.

Abstract

Many thousands of patent applications arrive at patent offices around the world every day. One important subtask when a patent application is submitted is to assign one or more classification codes from the complex and hierarchical patent classification schemes that will enable routing of the patent application to a patent examiner who is knowledgeable about the specific technical field. This task is typically undertaken by patent professionals, however due to the large number of applications and the potential complexity of an invention, they are usually overwhelmed. Therefore, there is a need for this code assignment manual task to be supported or even fully automated by classification systems that will classify patent applications, hopefully with an accuracy close to patent professionals. Like in many other text analysis problems, in the last years, this intellectually demanding task has been studied using word embeddings and deep learning techniques. In this paper we shortly review these research efforts and experiment with similar deep learning techniques using different feature representations on automatic patent classification in the level of sub-classes. On top of that, we present an innovative method of ensemble classifiers trained with different parts of the patent document. To the best of our knowledge, this is the first time that an ensemble method was proposed for the patent classification problem. Our first results are quite promising showing that an ensemble architecture of classifiers significantly outperforms current state-of-the-art techniques using the same classifiers as standalone solutions.