Enhancing Intellectual Property Rights(IPR) Transparency with Blockchain and Dual Graph Neural Networks

Abstract

One way that technology is changing the legal profession is by increasing the role of neural networks in intellectual property rights (IPR). The present status of intellectual property rights might be drastically changed if neural networks were to be used to improve the efficiency, accuracy, and cost-effectiveness of copyright, patent, and trademark procedures. Neural networks have had a significant influence on several IP-related applications, such as patent analysis and search, copyright infringement detection, and trademark search. Included in the suggested method are model training, feature extraction, and pre-processing. The goal of pre-processing is to eliminate or replace irrelevant or noisy data from each tweet so that sentiment classification can proceed more effectively. Algorithms for sentiment categorization and information content analysis make up feature extraction. The training process always made use of the DGNN model. This cutting-edge approach outperforms CNN and GNN with an average accuracy of ${9 1. 4 5 \%}$.