In recent years, Deep Learning methods have become very popular in Natural Language Processing (NLP), especially transformer-based architecture. NLP domain requires a high volume of annotated data to work. Unfortunately, obtaining high-quality and voluminous labeled data is expensive and time-consuming. One promising method which has singled out for its performance in the context of data deficiency is semi-supervised learning with Generative Adversarial Networks (GAN). In this paper, we propose a new approach called PaGAN which is a combination of a document classifier and a sentence-level classifier inside a GAN for patent documents understanding. The idea is to mine the patent’s motivating problem (aka contradiction in TRIZ domain) which is fundamentally important to understand the underlying invention and its originality. PaGAN is applied and evaluated on a real-world dataset. Experiments show outperforming results of PaGAN comparatively to baseline approaches.

TRIZ theory is considered as a kind of innovative theory, which mainly functions in solving contradiction and aims to improve engineers creativity. It may reason on different support such as web and scientific documents. Most of the current knowledge, especially in industrial domains lies in patents. They are, nevertheless, an underused resource because of their complexity, their length and the need of domain knowledge to make use of patent. In this paper, we introduce an innovative application of deep learning to mine knowledge from patents. We show that our summarization based approach, called SummaTRIZ1, enables the extraction of these contradictions essential for TRIZ problem solving engine. A BERT based summarization approach is introduced to retain contradiction sentences. Our approach is experimentally evaluated on a real data set showing the performance of SummaTRIZ.