Byoung-Youl Coh

Robust future-oriented technology portfolios: Black–Litterman approach

We propose a new way of constructing more robust technology portfolios to overcome the weaknesses of previous technology portfolios based either on the judgments of experts or on quantitative data such as patents. Instead of using historical data, the method of nonlinear forecasting enables us to forecast the future number of patent citations and accordingly, to use the forecast as a quantitative proxy for future returns and risks of technologies. Using the Black–Litterman portfolio model, we improve the accuracy of inputs by combining the future views of experts with the future returns and risks of technologies. As a consequence of this, the portfolio becomes strongly future-oriented. With our approach, corporate managers use both experts and data more effectively to build robust technology portfolios. In particular, our method is of great help for companies launching new businesses because the method avoids heavy dependency on internal experts with little knowledge about emerging technologies. A company entering the molecular amplification instrument market is exemplified herein.

Identifying product opportunities using collaborative filtering-based patent analysis

We propose a recommendation approach for product opportunity exploration.The approach identifies application products based on a firms existing product portfolio.The approach is built on patent text mining and collaborative filtering.The approach contributes to systematic product opportunity discovery across domains. One practical and low-risk approach to product planning for technology-based firms is to identify application products based on their existing product portfolios. Previous studies, however, have tended to neglect the current product development capabilities of target firms and to apply the technical data of specific fields to their methods, thereby failing to quantify a way of identifying various product opportunities. As a remedy, this paper proposes a new multi-step approach to product recommendation. The steps include (1) generating assigneeproduct portfolio vectors using text mining on a large-scale sample of patents, (2) recommending untapped products for a target firm by using latent Dirichlet allocation and collaborative filtering, (3) producing a visual map based on the promise and domain heterogeneity of the recommended products. To validate the practicability, we applied our approach to a Korean high-tech manufacturer by using all of the patents registered in the United States Patent and Trademark Office database during the period of time from 2009 to 2013. This study contributes to the systematic discovery of new product opportunities across various domains using the existing product portfolios of firms, and could become the basis for a future product opportunity analysis system.

New multi-stage similarity measure for calculation of pairwise patent similarity in a patent citation network

Being able to effectively measure similarity between patents in a complex patent citation network is a crucial task in understanding patent relatedness. In the past, techniques such as text mining and keyword analysis have been applied for patent similarity calculation. The drawback of these approaches is that they depend on word choice and writing style of authors. Most existing graph-based approaches use common neighbor-based measures, which only consider direct adjacency. In this work we propose new similarity measures for patents in a patent citation network using only the patent citation network structure. The proposed similarity measures leverage direct and indirect co-citation links between patents. A challenge is when some patents receive a large number of citations, thus are considered more similar to many other patents in the patent citation network. To overcome this challenge, we propose a normalization technique to account for the case where some pairs are ranked very similar to each other because they both are cited by many other patents. We validate our proposed similarity measures using US class codes for US patents and the well-known Jaccard similarity index. Experiments show that the proposed methods perform well when compared to the Jaccard similarity index.

Two-phase edge outlier detection method for technology opportunity discovery

This article introduces a method for identifying potential opportunities of innovation arising from the convergence of different technological areas, based on the presence of edge outliers in a patent citation network. Edge outliers are detected via the assessment of their centrality; pairs of patents connected by edge outliers are then analyzed for technological relatedness and past involvement in technological convergence. The pairs with the highest potential for future convergence are finally selected and their keywords combined to suggest new directions of innovation. We illustrate our method on a data set of US patents in the field of digital information and security.

Longitudinal Study-Based Dementia Prediction for Public Health

The issue of public health in Korea has attracted significant attention given the aging of the country’s population, which has created many types of social problems. The approach proposed in this article aims to address dementia, one of the most significant symptoms of aging and a public health care issue in Korea. The Korean National Health Insurance Service Senior Cohort Database contains personal medical data of every citizen in Korea. There are many different medical history patterns between individuals with dementia and normal controls. The approach used in this study involved examination of personal medical history features from personal disease history, sociodemographic data, and personal health examinations to develop a prediction model. The prediction model used a support-vector machine learning technique to perform a 10-fold cross-validation analysis. The experimental results demonstrated promising performance (80.9% F-measure). The proposed approach supported the significant influence of personal medical history features during an optimal observation period. It is anticipated that a biomedical “big data”-based disease prediction model may assist the diagnosis of any disease more correctly.

Technology Opportunity Discovery Based on Firms' Technologies and Products

Department of Industrial Engineering, Konkuk UniversityTechnology opportunity discovery (TOD) based on technological capability is a process which identifies new product and technology items that can be developed by utilizing or improving a firm’s existing products or technologies. By taking into consideration the investment risk of R&D and its practicality, developing technological capability-based TOD methodology is considered to be important for both business and research. To this end, we propose a technological capability-based TOD method and its system using TOD knowledge base. The method can support four types of TOD cases, which are based on a firm’s existing technologies and products, and TOD knowledge base is developed by using function information extracted from patent documents. In this paper, we introduce the overall framework of the method and provide application examples on the four TOD cases using the prototype system.

Patent Clustering and Outlier Ranking Methodologies for Attributed Patent Citation Networks for Technology Opportunity Discovery

Effectively ranking patents in outlierness in a patent citation network is a crucial task for patent analysis, including as it relates to technological opportunity discovery (TOD). Previous studies in the area of TOD focus on patent textual data. In this paper, we introduce a new approach that addresses TOD via patent outlierness, leveraging both patent attributes and citations. We propose the following characteristics for patent outliers: 1) not highly clustered with other patents; 2) low node centrality within the citation network; and 3) low similarity to other patents in the network. Existing outlier ranking approaches have the drawback of not leveraging the unique characteristics of attributed patent citation networks. We propose new outlier ranking methods developed specifically for patents in attributed patent citation networks. Attribute data independently describe a patent, while citation network data relate patents to each other, thus capturing patent outlierness from two different aspects. The contributions of this paper are, given an attributed patent citation network: 1) patent clustering algorithm, and 2) method for scoring and ranking patents in outlierness. Developed methods are validated using artificial datasets. Proposed outlier ranking methods are evaluated using U.S. patents in the area of digital information and security.