The Use of AI Techniques to Determine the Legitimacy of Patents’ Originality

Ekefre, Victoria Asuquo (2023) The Use of AI Techniques to Determine the Legitimacy of Patents’ Originality. Masters thesis, Dublin, National College of Ireland.

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Abstract

To ensure that only cutting-edge concepts are patentable, it is crucial in intellectual property law to evaluate a patent’s originality. However, the procedure is frequently time-consuming, expensive, and prone to prejudice. In order to address the difficulties of patent analysis and establish the veracity of patent originality, this research focuses on employing artificial intelligence (AI) tools. The validity of patent claims, descriptions, and illustrations has previously been evaluated using AI-based systems that include cutting-edge machine learning, natural language processing, and image processing techniques. These systems lack the ability to process complex legal language, detect variations, and analyze intricate diagrams using sophisticated graph neural network techniques. In this study, a graph neural network model is utilized to extract significant data and separate real patents from counterfeit ones by making predictions based on learned parameters. The implemented model achieved an impressive prediction accuracy of 1.0, demonstrating its efficacy in distinguishing genuine patents from counterfeit ones.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Mijumbi, Rashid UNSPECIFIED
Uncontrolled Keywords:	patient originality; AI-based systems; graph neural networks; learned parameters; prediction accuracy
Subjects:	T Technology > T Technology (General) > T201 Patents. Trademarks Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence T Technology > T Technology (General) > Information Technology > Cloud computing
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Tamara Malone
Date Deposited:	12 Aug 2024 15:58
Last Modified:	12 Aug 2024 15:58
URI:	https://norma.ncirl.ie/id/eprint/7048

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