Intellectual capital forecasting for invention patent through machine learning model
| Date | 13 November 2024 |
| Pages | 129-150 |
| DOI | https://doi.org/10.1108/JIC-12-2023-0286 |
| Published date | 13 November 2024 |
| Subject Matter | Information & knowledge management,Knowledge management,HR & organizational behaviour,Organizational structure/dynamics,Accounting & finance,Accounting/accountancy,Behavioural accounting |
| Author | Mei-Hsin Wang,Hui-Chung Che |
Intellectual capital forecasting for
invention patent through machine
learning model
Mei-Hsin Wang
Graduate School of Materials Science,
National Yunlin University of Science and Technology, Douliou, Taiwan, and
Hui-Chung Che
ESG Committee, China BioMedical and Technology Application Association,
New Taipei City, Taiwan
Abstract
Purpose –This research explores support vector machine (SVM) with Gaussian radial basis function kernel
(RBF) as the model and Analysis of Variance (ANOVA) for forecasting the invalidation re-examination
decisions of China invention patents, it is beneficial to support patent monetization for corporate intellectual
capital.
Design/methodology/approach –There were 8,666 China invention patents with their existing invalidation
re-examination decisions during 2000∼2021 chosen to conduct classification model training and prediction for
the accuracy of invalidation re-examination decisions through SVM with RBF. Statistical significance was
performed by ANOVAto identify indicators for these invention patents selected in this research. These selected
8,666 China invention patents were divided into two groups based on their invalidation re-examination
decisions during 2000∼2021 in Table 1, which Group 1 included 5,974 invention patents with all valid or
partially valid claims, and Group 0 included 2,692 invention patents with all invalid claims. Thereafter, each
group was further divided into sub-groups based on 13 major regions where the applicants filed invalidation re-
examination. The training sets for Group 1, Group 0 and the sub-groups were selected based on the patent issued
in January,February, April, May, July, August, October and November; while the prediction sets were selected
from the invention patents issued in March, June, September and December.
Findings –The training and prediction accuracies were compared to the existing invalidation re-examination
decisions. Accuracies of training sets were ranged from 100% in region 7 (Beijing) and region 9 (Shanghai) to
95.95% in region 1 (US), and the average accuracy of invalidation re-examination decisions was 98.95%. While
the accuracies of prediction sets for Group 1 were ranged from 100.00% in region 7 (Beijing) to 90.78% in
region 13 (Overseas-others), and the average accuracy of classification was 95.96%, this research’s outcomes
confirmed the purpose of applying SVM with RBF to predict the patentability sustainability.
Originality/value –This research developed an empirical method through SVM with RBF to predict
patentability sustainability which is crucial for corporate intellectual capital on patents. In particular, the
investments on patents are huge, including the patent cultivation and maintenance, developments into products
or services, patent litigations and dispute managements. Therefore, this research is beneficial not only for
corporation, but also for research organisations to perform cost-effective and profitable patent strategies on
intellectual capital.
Keywords Invention patent, Patent invalidation re-examination, Analysis of Variance (ANOVA),
Support vector machine (SVM), Gaussian radial basis function kernel (RBF)
Paper type Research paper
1. Introduction
Knowledge-driven companies pay great attention to intellectual capital (Rastogi, 2000).
Intellectual capital gets generated from intangible assets like knowledge (Jardon, 2015).
Knowledge process can support intellectual capital to conceptualize knowledge-based assets
Journal of
Intellectual
Capital
129
© Mei-Hsin Wang and Hui-Chung Che. Published by Emerald Publishing Limited. This article is
published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce,
distribute, translate and create derivative works of this article (for both commercial and non-commercial
purposes), subject to full attribution to the original publication and authors. The full terms of this license
may be seen at http://creativecommons.org/licences/by/4.0/legalcode
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1469-1930.htm
Received 24 December 2023
Revised 8 May 2024
16 August 2024
Accepted 10 September 2024
Journalof Intellectual Capital
Vol.25 No. 7, 2024
pp.129-150
EmeraldPublishing Limited
1469-1930
DOI10.1108/JIC-12-2023-0286
and create competitive advantage (Yang et al., 2015). These intangible assets are the key
drivers that ensure competitiveness of companies (Tsui et al., 2014). Patent is one type of
intangible assets that constitute or generate intellectual capital, which is important for
knowledge-driven companies. Therefore, studies exploring this bidirectional relationship
(Garcia-Perez et al., 2020) are viewed as constructive. Patent monetization can structure
revenue profiles for optimal profits and creation of intellectual capital. This study focuses on
patent sustainability by using machine learning to predict decision of invalidation
re-examination, which is one type of re-examination processes and particularly designed for
issued patent in China. The outcome of invalidation re-examination is vital for patent
monetization.
Tocultivate a patent and maintain the patent globally can cost a fortune. Therefore, the
enforcement of patent rights to increase the return on investment is an important issue
for companies. There are various ways to enforce patent rights and their monetization,
such as selling products or services protected by patents, licensing patents, securitizing
patents, leasing patents, tokenizing patents, selling patents, or litigation on patent
rights, etc.
Corporates can always generate revenue from patents with or without existing services or
products. However, there is a flood of emerging technologies. One specific technology can
involve many similar patents seeking exclusivity. Patent owners fight diligently to secure
validity,while competitors attack exclusivity from various perspectives to ruin the patent or to
limit the scope of patent claims. In real world, patent examination process cannot be perfect
under various circumstances, for example, inadequate experience of examiners, time available
for comprehensive consideration being too short, or for lack of appropriate tools to search
related prior arts, etc. Therefore, patent claims for issued patents are often vulnerable if
re-examination procedure is filed.
There have been several landmark patent litigations that ended with extremely high
damages awarded for misuse. Polaroid had to pay 909 million US dollars (Los Angeles Times,
1990) to Kodak for patent-infringement of a long-running instant photography technology in
1988. Damages awarded in IGEN v Roche was 505 million US dollars, which involved in US
patent no. 5,221,605 titled [Luminescent metal chelate labels and means for detection], US
patent no. 6,140,138 titled [Electrochemiluminescent metal chelates and means for detection],
US patent no. 6,325,973 titled [Methods and apparatus for improved luminescence assays] and
US patent no. 6,451,225 titled [Electrochemiluminescent reaction utilizing amines-derived
reductant] as reported by the Court Listener (2022).
Genetech was asked to pay USD 300 million in compensatory damages and 200 million as
punitive damages to City of Hope for breach of contract (Horvitz & Levy LLP, 2008). The
patented technology was related to the Coax genetically-altered bacteria to produce
therapeutic human proteins, such as insulin (McBride, 2009).
Hughes Tool v Smith International was settled for 230 million US dollars, which involved
two of Hughes’ US patents, US patents no. 3,397,928 titled [Seal means for drill bit bearings]
(Hanley and Applegate, 1986) and US patent no. 3,476,195 titled [Lubricant relief valve for
rock bits] (Justia, 2003) after litigation that lasted 14 years.
The damage awarded in Exxon v Mobil was 171 million US dollars, for infringement of US
patent no. 5,324,800 titled [Process and Catalyst for Polyolefin Density and Molecular Weight
Control], which involved metallocene catalysts for better fabrication control and quality of
plastics and rubber (Chemical Online, 1998).
Since patents offer handsome economic benefits, patent owners defend their Intellectual
Property Rights (IPRs) fiercely, while competitors diligently try to pick flaws to seek
revocation of patents through invalidation re-examination in patent offices or complaining of
infringement in courts. The motive of this research is to predict sustainability of China
invention patents in invalidation re-examination by using machine learning.
There are three types of patents, invention patent, utility model patent and design patent,
defined in Article 2 of Patent Law of the People’s Republic of China (CNIPA, 2022). The
JIC
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