Joonhyuck Lee

Establishment of Strategy for Management of Technology Using Data Mining Technique

Abstract Technology forecasting is about understanding a status of a specific technology in the future, based on the cur-rent data of the technology. It is useful when planning technology management strategies. These days, it is com-mon for countries, companies, and researchers to establish R&D directions and strategies by utilizing experts’ opinions. However, this qualitative method of technology foreca sting is costly and time consuming since it re-quires to collect a variety of opinions and analysis from many experts. In order to deal with these limitations, quantitative method of technology forecasting is being studied to secure objective forecast result and help R&D decision making process. This paper suggests a methodology of t echnology forecasting based on quantitative analysis. The methodology consists of data collection, principa l component analysis, and technology forecasting by logistic regression, which is one of the data mining techniq ues. In this research, patent documents related toautonomous vehicle are collected. Then, the texts from patent d ocuments are extracted by text mining technique to construct an appropriate form for analysis. After principal component analysis, logistic regression is performedby using principal component score. On the basis of this result , it is possible to analyze R&D development sit-uation and technology forecasting. Key Words : Technology Forecasting, Patent, Logistic Regression, Autonoum ous vehicle, Principal component analysisReceived: Sep. 14, 2014Revised : Sep. 28, 2014Accepted: Jan. 20, 2015

A Novel Methodology for Extracting Core Technology and Patents by IP Mining

Abstract Society has been developed through analogue, digital, and smart era. Every technology is going through consistent changes and rapid developments. In this competitive society, R&D strategy establishment is sig-nificantly useful and helpful for improving technology competitiveness. A patent document includes technical and legal rights information such as title, abstract, description, claim, and patent classification code. From the paetn dtocumen,t a ol otf peopel can undersatnd and coellc ltega land techncia lniformaoitn. Thsi unqiue feature of patent can be quantitatively applied for technology analysis. This research paper proposes a meth-odology for extracting core technology and patents based on quantitative methods. Statistical analysis and so-cai nlewtork anaylssi are appeild ot IPC codes ni order to exrtac tcore technoolgies whti acvite R&D and high centralities. Then, core patents are also extracted by analyzing citation and family information.Key Words : Patent analysis, IPC Code, Social Network Analysis, Patent Citation, Patent FamilyReceived: Mar. 22, 2015Revised : Apr. 5, 2015Accepted: Jun. 1, 2015

Time Series Analysis of Patent Keywords for Forecasting Emerging Technology

Forecasting of emerging technology plays important roles in business strategy and R&D investment. There are various ways for technology forecasting including patent analysis. Qualitative analysis methods through experts’ evaluations and opinions have been mainly used for technology forecasting using patents. However qualitative methods do not assure objectivity of analysis results and requires high cost and long time. To make up for the weaknesses, we are able to analyze patent data quantitatively and statistically by using text mining technique. In this paper, we suggest a new method of technology forecasting using text mining and ARIMA analysis.

A Novel Method for Technology Forecasting Based on Patent Documents

There have been many recent studies on forecasting emerging and vacant technologies. Most of them depend on a qualitative analysis such as Delphi. However, a qualitative analysis consumes too much time and money. To resolve this problem, we propose a quantitative emerging technology forecasting model. In this model, patent data are applied because they include concrete technology information. To apply patent data for a quantitative analysis, we derive a Patent–Keyword matrix using text mining. A principal component analysis is conducted on the Patent–Keyword matrix to reduce its dimensionality and derive a Patent–Principal Component matrix. The patents are also grouped together based on their technology similarities using the K-medoids algorithm. The emerging technology is then determined by considering the patent information of each cluster. In this study, we construct the proposed emerging technology forecasting model using patent data related to IEEE 802.11g and verify its performance.

Technology Strategy based on Patent analysis

Abstract By occasion of propagation of 3D TV, technology of glassless 3D display is increasingly important. Currently, there was no lead technology In this technology. Thus it is important to develop differentiated technology for secure competitiveness. In this paper, We analyze patents for R&D strategy about glassless 3D display. Through Company-Technology matrix analysis and Patent trend analysis, We extract promising technology field and core technology. Lastly We suggest R&D strategy by using patent road map. Key Words : 3D display, Patent analysis, Company-Technology matrix, R&D, Technology management strategy본 논문은 BK21 플러스 사업(고려대학교, 제조·물류분야에서의 빅 데이터 운용 사업팀)으로 지원된 연구임.본 논문은 2015년 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임.(한국연구재단-NRF-2015R1D1A1A01059742)This is Work was supported by the BK21 Plus (Big Data in Manufacturing and Logistics Systems, Korea University), This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01059742)

Generating Firm`s Performance Indicators by Applying PCA

There have been many studies on statistical forecasting on firm`s performance and stock price by applying various financial indicators such as debt ratio and sales growth rate. Selecting predictors for constructing a prediction model among the various financial indicators is very important for precise prediction. Most of the previous studies applied variable selection algorithms for selecting predictors. However, the variable selection algorithm is considered to be at risk of eliminating certain amount of information from the indicators that were excluded from model construction. Therefore, we propose a firm`s performance prediction model which principal component analysis is applied instead of the variable selection algorithm, in order to reduce dimensionality of input variables of the prediction model. In this study, we constructed the proposed prediction model by using financial data of American IT companies to empirically analyze prediction performance of the model.