Capsule Based Neural Network Architecture to perform completeness check for Patent Eligibility Process

In the process of filing patents, attorneys need to ask many questions, to the inventors, to ascertain patent eligibility. We propose to ease up such conversation through a deep learning-based system. This system can automatically check whether all key ingredients required for checking the patent eligibility are present in technical write-up shared by the inventors. If not, the inventors can provide the missing information. We present a trainable model to identify various ingredients such as the objective, motivation, new observation, etc. from research articles. We model this as a sentence classification problem, which is a difficult task because a patent can be filed in any domain, and sentences involved can often be very long. To this end, we propose a dilated LSTM and capsule-based neural network architecture. We present experimental results of the proposed model on a real-world patent dataset covering patent applications in diverse domains in which our organization is carrying out research and innovation activities, and also three publicly available sentence classification datasets. Through empirical analysis, we show that a) Our model performs significantly better than several strong baselines on the patent dataset; b) Performing dilation operation on LSTMs allows us to capture long term dependencies; c) our model is comparable to existing state-of-art approaches on the publicly available datasets; d) Error analysis through LIME shows that the proposed approach can help patent attorneys to interpret the decisions taken by the classifier.