A study on evaluation model for patent property right using machine learning techniques: An empirical study of korean patents

被引：0

作者：

Lee, Woon-Seek ^{[1
]}

Koh, Shiegheun ^{[1
]}

Seo, Wonchul ^{[1
]}

机构：

[1] Division of Systems Management and Engineering, Pukyong National University, No. 45, Yongso-ro, Nam-gu, Busan,608-737, Korea, Republic of

来源：

ICIC Express Letters, Part B: Applications | 2015年 / 6卷 / 12期

基金：

新加坡国家研究基金会;

关键词：

Artificial intelligence - Learning algorithms - Learning systems - Quality control;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

As patents are becoming an increasingly valuable asset to protect new inventions by establishing property rights to invention owners, more and more companies are pursuing to gain robust property rights of their own technologies and products by creating strong patents. A patent can be regarded as a strong patent when it has high quality of patent right (QOPR). Therefore, how to evaluate the QOPR is increasingly growing in importance. A preliminary work has presented a quantitative evaluation method to measure the QOPR but its validation results show relatively low accuracy. Therefore, this study presents an enhanced evaluation model which shows the improvement in validation accuracy over the preliminary work. It enables companies to measure the QOPR of patents in advance so that they can create strong patents with robust property rights. Furthermore, it will be a basis for systematic support systems to R&D planning experts to establish elaborate patent portfolio strategies. © 2015, ICIC Express Letters Office. All rights reserved.

引用

页码：3341 / 3347