Jong Hwan Suh

Exploring the effect of structural patent indicators in forward patent citation networks on patent price from firm market value

This paper introduces structural patent indicators in the forward patent citation networks (FPCNs). Using the estimates of their relationship with firm market value, it explores the effect of the structural patent indicators on patent price. When applied to US smartphone industry and US drug and biotechnology industry, the common finding is that a firms registered patent gets higher price if influenced faster by the previously registered patents of the same firm through self-citations. The empirical results imply that merely counting forward citations, shown related to patent price by the previous works, is insufficient; the structural properties in FPCNs are essential for the more accurate estimation of patent price. They also show that the relationships between structural patent indicators in the FPCNs and patent price are different between industries. It urges the necessity of selecting structural patent indicators, well fitted to each industry, for the better patent valuation using forward citations.

A new visualization method for patent map: application to ubiquitous computing technology

As technologies develop in faster and more complicated ways, it is getting more important to expect the direction of technological progresses. So many methods are being proposed all around world and one of them is to use patent information. Moreover, with efforts of governments in many countries, many patent analysis methods have been exploited and suggested usually on the basis of patent documents. However, current patent analysis methods have some limitations. In this paper, we suggest a new visualization method for a patent map, which represents patent analysis results with considering both structured and unstructured items of each patent document. And by the adoption of the k-means clustering algorithm and semantic networks, we suggest concrete steps to make a patent map which gives a clear and instinctive insight on the targeted technology. In application, we built up a patent map for the ubiquitous computing technology and discussed an overall view of its progresses.

Comparing writing style feature-based classification methods for estimating user reputations in social media

In recent years, the anonymous nature of the Internet has made it difficult to detect manipulated user reputations in social media, as well as to ensure the qualities of users and their posts. To deal with this, this study designs and examines an automatic approach that adopts writing style features to estimate user reputations in social media. Under varying ways of defining Good and Bad classes of user reputations based on the collected data, it evaluates the classification performance of the state-of-art methods: four writing style features, i.e. lexical, syntactic, structural, and content-specific, and eight classification techniques, i.e. four base learners—C4.5, Neural Network (NN), Support Vector Machine (SVM), and Naïve Bayes (NB)—and four Random Subspace (RS) ensemble methods based on the four base learners. When South Korea’s Web forum, Daum Agora, was selected as a test bed, the experimental results show that the configuration of the full feature set containing content-specific features and RS-SVM combining RS and SVM gives the best accuracy for classification if the test bed poster reputations are segmented strictly into Good and Bad classes by portfolio approach. Pairwise t tests on accuracy confirm two expectations coming from the literature reviews: first, the feature set adding content-specific features outperform the others; second, ensemble learning methods are more viable than base learners. Moreover, among the four ways on defining the classes of user reputations, i.e. like, dislike, sum, and portfolio, the results show that the portfolio approach gives the highest accuracy.

Well-Being store: a new channel in U-commerce for insurance industry

With new channels and devices based on ubiquitous computing, UCommerce provides an environment where buyers and sellers are literally able to commerce anytime, anywhere, and anyway they like. In U-Commerce, a lot of opportunities are expectable in various industrial areas, and especially in insurance industry. In this paper, we propose new types of insurance services in U-Commerce. And as a new channel to practice it, we propose Well-Bing Store with three concepts: ‘Corporeal Insurance Product’, ‘U-Cart’, and ‘Health Examination’. And supportive processes in Well-Being Store are depicted in detail as well. By introducing Well-Being Store, we transform a physical place into an intelligent space which cares insured customers previously before they get sick. This is actualized by managing a list of foodstuffs in a U-Cart accordingly, and by keeping a health examination regularly with help of an insurance company and a hospital.

U-DRM: A Framework of Digital Rights Management Based on Ubiquitous Computing

In the Internet, the digital format of most contents has led to its increased piracy because of the ease of copying and sharing the digital contents. Moreover, the advent of new technologies such as ubiquitous computing seems to cause problems of piracy even more than before. Therefore, we guess its going to be a more critical issue to control and manage rights of digital contents. In this paper, we propose a framework of U-DRM to control and manage rights of digital contents on the basis of the ubiquitous computing. The framework consists of two methods based on digital mediums and digital contents. And with a step ahead, we discuss ways for the application of usage data collected from the proposed framework.