Marketable value estimation of patents using ensemble learning methodology: Focusing on U.S. patents for the electricity sector.

Le Hoang Son,Sungyun Choi,Sang Ok Choi,Haneul Eom

doi:10.1371/journal.pone.0257086

Abstract

Patent valuation is required to revitalize patent transactions, but calculating a reasonable value that consumers and suppliers could satisfy is difficult. When machine learning is used, a quantitative evaluation based on a large volume of data is possible, and evaluation can be conducted quickly and inexpensively, contributing to the activation of patent transactions. However, due to patent characteristics, securing the necessary training data is challenging because most patents are traded privately to prevent technical information leaks. In this study, the derived marketable value of a patent through event study is used for patent value evaluation, matching it with the semantic information from the patent calculated using latent Dirichlet allocation (LDA)-based topic modeling. In addition, an ensemble learning methodology that combines the predicted values of multiple predictive models was used to determine the prediction stability. Base learners with high predictive power for each fold were different, but the ensemble model that was trained on the base learners’ predicted values exceeded the predictive power of the individual models. The Wilcoxon rank-sum test indicated that the superiority of the accuracy of the ensemble model was statistically significant at the 95% significance level.

Highlights

With the advancement of the industrial infrastructure, such as Industry 4.0, the importance of intangible assets continues to increase, and patents, a representative intangible asset, are considered a key resource for enhancing a corporation’s technological competitiveness [1]
Among the event study methodologies, an analysis was performed based on a market model, and the calculated abnormal return was used as the market value of each patent
The random forest (RF), multilayer perceptron (MLP), and convolutional neural network (CNN) models were used as the base learners of the ensemble model

Summary

Introduction

With the advancement of the industrial infrastructure, such as Industry 4.0, the importance of intangible assets continues to increase, and patents, a representative intangible asset, are considered a key resource for enhancing a corporation’s technological competitiveness [1]. Despite the importance of patents, patent transactions in the market are not active due to difficulties in determining a reasonable value that consumers and suppliers can satisfy [2]. Patent valuation methodologies have been largely divided into revenue, cost, and market approaches [3]. The revenue approach is a way to convert future revenue from patent rights to the present value.

Methods

Results

Conclusion