TY - JOUR
T1 - Patent Value Analysis Using Deep Learning Models—The Case of IoT Technology Mining for the Manufacturing Industry
AU - Trappey, Amy J.C.
AU - Trappey, Charles
AU - Govindarajan, Usharani Hareesh
AU - Sun, John J.
N1 - Publisher Copyright:
© 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 2019/11/23
Y1 - 2019/11/23
N2 - 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.
AB - 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.
KW - Business intelligence
KW - deep neural network (DNN)
KW - dynamic indicator selection
KW - Internet of Things (IoT)
KW - patent valuation
UR - http://www.scopus.com/inward/record.url?scp=85077242731&partnerID=8YFLogxK
U2 - 10.1109/TEM.2019.2957842
DO - 10.1109/TEM.2019.2957842
M3 - Article
AN - SCOPUS:85077242731
SN - 0018-9391
VL - 68
SP - 1
EP - 13
JO - IEEE Transactions on Engineering Management
JF - IEEE Transactions on Engineering Management
IS - 5
M1 - 08941278
ER -