An Intelligent Patent Summary System Deploying Natural Language Processing and Machining Learning

With the growing awareness of the fourth industrial revolution and its implication, there are increasing applications of artificial intelligence to secure the value of intellectual property (IP), to develop competitive products, and to license IP. Natural language processing based on artificial intelligence methods have not been sufficiently developed and there remain many obstacles for current researchers to interpret the meaning of large numbers of intellectual property documents. The means to explain the related intellectual property documents (e.g. products in a given domain) and summarize sets of remain a significant challenge. In this research, we develop an intelligent patent summarization system based on artificial intelligence approaches that include Recurrent Neural Network (RNN), Word Embedding, and Attention Mechanisms. The aim of this system is to automatically summarize technical documents in a specific patent domain and identify potential opportunities or liabilities for R & D engineers, lawyers, and managers. The AI-based experiment-based solution for summarization is used to capture the key technique keywords, popular technical terms, and new technical terms. The compression ratios and the retention ratios are used to evaluate the density and consistency of critical information for the proposed summarization system and to measure the quality of the summary system output.