An Empirical Study on Patent Novelty Detection: A Novel Approach Using Machine Learning and Natural Language Processing

Abstract

Patent, a form of intellectual property often be in the first place when it comes to securing an invention. The legal boundaries created then will become key stages of turning an invention into a commercial product. In recent years, the unprecedented growth of patent applications has induced a great challenge to patent examiners. Novelty detection is one major step considered before and after filing a patent application to assure claimed inventions are new and non-obvious. This itself is considered as a salient stage of prior art search by patent applicants, patent examiners, patent attorneys, patent agent professionals. Management in terms of critical analysis of such a large scale of documents has become a challenge since missing an optimal, effective, and efficient system. To this end, we come up with a novel experimental case study to foster highly recursive and interactive tasks. We developed and investigated more than 50 machine learning models on the considered dataset. The contributions of this work include: (1) outlined and anticipated the importance of novelty detection in the patent domain, (2) develop various baseline models for novelty detection, (3) utilize immense contributions of deep learning towards NLP to improve baseline models, (4) assess the performance of every model by using different word embeddings like word2vec, glove, fasttext, and domain-specific embeddings, (5) a novel application of NBSVM algorithm on our dataset, and considered as exceptionally good of our models. We articulated the fulfillment of models using training and validation curves to prove seemingly negligible overfit or no overfit, in the hope that effective automation in novelty detection helps in driving down the routine prior art search efforts.

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