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) |
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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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