Patent Value Analysis Using Deep Learning Models—The Case of IoT Technology Mining for the Manufacturing Industry

Amy J.C. Trappey, Charles Trappey, Usharani Hareesh Govindarajan*, John J. Sun

*此作品的通信作者

研究成果: Article同行評審

54 引文 斯高帕斯(Scopus)

摘要

The R&D output and global commercialization of intellectual properties (IPs), especially patents filed in many countries, have increased dramatically over the past decade. The overwhelming growth in research and IP activities has led to a major challenge to understand and forecast technology development insights and trends. Evidence-based data analytics is essential for technology mining. The assessment of patent values is a critical aspect of technology mining, which remains a highly subjective task performed by domain experts. As businesses become globalized, subjectivity in underlying assessments of large volumes of patent documents leads to overpriced or undervalued IP sales or licensing that exposes stakeholders to legal and financial risks. Thus, the development of intelligent methods for patent valuation requires new research emphasis. This article applies a deep learning analytical method for automatic and intelligent patent value estimation. Principal component analysis (PCA) is used to identify significant patent value indicators from the given patent dataset. Then, deep neural networks (DNN) for value prediction are modeled and trained using the training set. A detailed case study of 6466 manufacturing Internet of Things (IoT) patents is analyzed to demonstrate the improved results of building PCA-preprocessed DNN models to perform patent valuations. Finally, selected higher value IoT patents owned by leading Taiwan assignees are identified and analyzed to verify the technological competitive intelligence.
原文English
文章編號08941278
頁(從 - 到)1-13
頁數13
期刊IEEE Transactions on Engineering Management
68
發行號5
DOIs
出版狀態Published - 23 11月 2019

指紋

深入研究「Patent Value Analysis Using Deep Learning Models—The Case of IoT Technology Mining for the Manufacturing Industry」主題。共同形成了獨特的指紋。

引用此