  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.
