Establish a patent risk prediction model for emerging technologies using deep learning and data augmentation. (April 2022)

• Record Type: Journal Article
• Authors: Chi, Yung-Chang; Wang, Hei-Chia
• Is Part Of: Advanced engineering informatics. Volume 52(2022)
• Publication Date: 2022-04
• Subjects: Deep learning -- Data augmentation -- Patent risk -- Patent infringement; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2021.101509
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: This research attempts to identify the issues involved in evaluating patent applications and infringement risks from existing patent databases. For each patent application, this research uses Convolutional Neural Networks, CNN + Long Short Term Memory Network, LSTM, prediction model, and the United States Patent and Trademark Office (USPTO) public patent application and reviews results based on keyword search. The goal of this research is to predict the probability of each patent application's approval, and improve the accuracy of the prediction. Experimental results of the model in this study predicts that the accuracy of each classification is at least 87.7%, and can be used to find the classification of the reason for a rejection of a patent application failure. Abstract: Technology patents are considered the source and bedrock of emerging technologies. Patents create value in any enterprise. However, obtaining patents is time consuming, expensive, and risky; especially if the patent application is rejected. The development of new patents requires extensive costs and resources, but sometimes they may be similar to other patents once the technology is fully developed. They might lack relevant patentable features and as a result, fail to pass the patent examination, resulting in investment losses. Patent infringement is also an especially important topic for reducing the risk of legal damages of patent holders, applicants, and manufacturers. Patent examinations have so far been performed manually. Due to manpower and time limitations, the examination time is exceedingly long and inefficient. Current patent similarity comparison research, and the classification algorithms of text mining are most commonly employed to provide analyses of the possibility of examination approval, but there is insufficient discussion about the possibility of infringement. However, if a new technology or innovation can be accurately determined in advance whether it likely to pass or fail (and why), or is at risk of patent infringement, losses can be mitigated. This research attempts to identify the issues involved in evaluating patent applications and infringement risks from existing patent databases. For each patent application, this research uses Convolutional Neural Networks, CNN + Long Short Term Memory Network, LSTM, prediction model, and the United States Patent and Trademark Office (USPTO) public utility patent application and reviews results based on keyword search. Then, data augmentation is utilized before performing model training; 10% of the approved and rejected applications are randomly selected as test cases, with the remaining 90% of the cases used to train the prediction model of this research in order to determine a model that can predict patent infringement and examination outcomes. Experimental results of the model in this study predicts that the accuracy of each classification is at least 87.7%, and can be used to find the classification of the reason for a rejection of a patent application failure.