  Patent analysis and mining are time-consuming and costly processes for
companies, but nevertheless essential if they are willing to remain
competitive. To face the overload induced by numerous patents, the idea is to
automatically filter them, bringing only few to read to experts. This paper
reports a successful application of fine-tuning and retraining on pre-trained
deep Natural Language Processing models on patent classification. The solution
that we propose combines several state-of-the-art treatments to achieve our
goal - decrease the workload while preserving recall and precision metrics.
