Predicting Patent Quality Based on Machine Learning Approach - Erdogan, Zulfiye; Altuntas, Serkan; Dereli, Turkay (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2024)
The investment budget allocated by companies in R&D activities has increased due to increased competition in the market. Applications for industrial property rights by countries, investors, companies, and universities to protect inventions obtained as an outcome of investments have also increased. The selection of the patent to be invested becomes more difficult with the increasing number of applications. Therefore, predicting patent quality is quite significant for companies to be successful in the future. The level to which a patent meets the expectations of decision makers is referred to as patent quality. Patent indices represent decision makers' expectations. In this study, an approach is proposed to predict patent quality in practice. The proposed approach uses supervised learning algorithms and analytic hierarchy process (AHP) method. The proposed approach is applied to patents related to personal digital assistant technologies. The performances of individual and ensemble machine learning methods have been also analyzed to establish the prediction model. In addition, 75% split ratio and the five-fold cross-validation methods have been used to verify the prediction model. The multilayer perceptron algorithm has 76% accuracy value. The proposed prediction model is essential in directing R&D studies to the right technology areas and transferring the incentives to patent applications with a high quality rate.
Keywords: Patents; Codes; Clustering algorithms; Prediction algorithms; Machine learning algorithms; Predictive models; Technological innovation; Analytic hierarchy process (AHP); machine learning; multilayer perceptron; patent indices; supervised learning algorithms; MULTICRITERIA DECISION-MAKING; SCIENCE-AND-TECHNOLOGY; FORECASTING TECHNOLOGY; EMERGING TECHNOLOGIES; PROMISING TECHNOLOGY; ENERGY TECHNOLOGY; NETWORK ANALYSIS; SELECTION; INDICATORS; ALGORITHM; Business & Economics; Engineering