Deep Learning based Pipeline with Multichannel Inputs for Patent Classification

Abstract

Patent document classification as groundwork has been a challenging task with no satisfactory performance for decades. In this work, we introduce a deep learning pipeline for automatic patent classification with multichannel inputs based on LSTM and word vector embeddings. Sophisticated text mining methods are used to extract the most important segments from patent texts, and a domain-specific pre-trained word embeddings model for the patent domain is developed; it was trained on a very large dataset of more than five million patents. A deep neural network model is trained with multichannel inputs namely embeddings of different segments of patent texts, and sparse linear input of different metadata. A series of patent classification experiments are conducted on different patent datasets, and the experimental results indicate that using the segments of patent texts as well as the metadata as multichannel inputs for a deep neuralnetwork model, achieves better performance than one input channel.