A Smart Patent Monitoring Assistant : Using Natural Language Processing

Abstract: Patent monitoring is about tracking the upcoming inventions in a particular field, predicting future trends, and specific intellectual property rights of interest. It is the process of finding relevant patents on a particular topic based on a specific query. With patent monitoring, one can keep them updated on the new technology in the market. Also, they can find potential licensing opportunities for their inventions. The outputs of patent monitoring are essential for companies, academics, and inventors looking forward to using the latest patents that can enhance further innovation. Nevertheless, there is no widely accepted best approach to patent monitoring. Usually, most patent monitoring systems are based on complex search and find, often leading to insignificant hit rates and highly human intervention. As the number of patents published each year increases massively and with patents being critical to accelerating innovation, the current approach to patent monitoring has two main drawbacks. Firstly, human-driven patent monitoring is time consuming and expensive process. In addition, there is a risk of overlooking interesting documents due to inadequate searching tools and processes, which could cost companies fortunes while at the same time hindering further innovation and creativity. This thesis presents a smart patent monitoring assistant tool that applies natural language processing. The use of several natural language processing methods is investigated to find, classify and rank relevant documents. The tool was trained on a dataset that contains the title, abstract, and claims of patent documents. Given a dataset of patent documents, the aim of this thesis is to create a tool that can classify patents into two classes relevant and not relevant. Furthermore, the tool can rank documents based on relevancy. The evaluation result of the tool gave satisfying results when it came to receiving the expected patents. In addition, there is a significant improvement in terms of performance for memory usage and the time it took to train the model and get results.