Browsing by Author "Shih, Meng-Jung"

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Discovering competitive intelligence by mining changes in patent trends
(2010-04-01)
Shih, Meng-Jung
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Liu, Duen-Ren
;
Hsu, Ming-Li
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Obtaining sufficient competitive intelligence is a critical factor in helping business managers gain and maintain competitive advantages. Patent data is an important source of competitive intelligence that enterprises can use to gain a strategic advantage. Under existing approaches, to detect changes in patent trends, business managers must rely on patent analysts to compare two patent analysis charts of different time periods. The discovery of change of trends currently still needs laborious human efforts and no efficient computer-based approaches are available for helping this task. In this paper, we propose a patent trend change mining (PTCM) approach that can identify changes in patent trends without the need for specialist knowledge. The proposed approach consists of steps including patent collection, patent indicator calculation, and change detection. In change detection phase the approach firstly excavate rules between two different time periods, comparing them to determine the trend changes. These trend changes are then classified into four categories of change, evaluated with change degree and ranked by their change degree as the output information to be referred by decision makers. We apply the PTCM approach to Taiwan's semiconductor industry to discover changes in four types of patent trends: the R&D activities of a company, the R&D activities of the industry, company activities in the industry and industry activities generally. The proposed approach generates competitive intelligence to help managers develop appropriate business strategies. (C) 2009 Elsevier Ltd. All rights reserved.
Hybrid-Patent Classification Based on Patent-Network Analysis
(2011-02-01)
Liu, Duen-Ren
;
Shih, Meng-Jung
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Effective patent management is essential for organizations to maintain their competitive advantage. The classification of patents is a critical part of patent management and industrial analysis. This study proposes a hybrid-patent-classification approach that combines a novel patent-network-based classification method with three conventional classification methods to analyze query patents and predict their classes. The novel patent network contains various types of nodes that represent different features extracted from patent documents. The nodes are connected based on the relationship metrics derived from the patent metadata. The proposed classification method predicts a query patent's class by analyzing all reachable nodes in the patent network and calculating their relevance to the query patent. It then classifies the query patent with a modified k-nearest neighbor classifier. To further improve the approach, we combine it with content-based, citation-based, and metadata-based classification methods to develop a hybrid- classification approach. We evaluate the performance of the hybrid approach on a test dataset of patent documents obtained from the U.S. Patent and Trademark Office, and compare its performance with that of the three conventional methods. The results demonstrate that the proposed patent-network-based approach yields more accurate class predictions than the patent network-based approach.
Mining changes in patent trends for competitive intelligence
(2008)
Shih, Meng-Jung
;
Liu, Duen-Ren
;
Hsu, Ming-Li
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Obtaining sufficient competitive intelligence is a critical factor in helping business managers gain and maintain competitive advantages. Patent data is an important source of competitive intelligence that enterprises can use to gain a strategic advantage. Under existing approaches, to detect changes in patent trends, business managers must rely on comparing two patent analysis charts of different time periods, it requires human effort and time. In this paper, we propose a patent trend change mining (PTCM) technique that can identify changes in patent trends without the need for specialist knowledge. We apply the PTCM approach to Taiwan's semiconductor industry to discover changes in four types of patent trends: the R&D activities of a company, the R&D activities of the industry, company activities in the industry and industry activities generally. ne change mining approach generates competitive intelligence to help managers develop appropriate business strategies based on their findings.
Mining the change of event trends for decision support in environmental scanning
(2009-03-01)
Liu, Duen-Ren
;
Shih, Meng-Jung
;
Liau, Churn-Jung
;
Lai, Chin-Hui
;
資訊管理與財務金融系註：原資管所+財金所
As the business environment has become increasingly complex, the demand for environmental scanning to assist company managers plan strategies and responses has grown significantly. The conventional technique for supporting environmental scanning is event detection from text documents such as news stories. Event detection methods recognize events, but neglect to discover the changes brought about by the events. In this work, we propose an event change detection (ECD) approach that combines association rule mining and change mining techniques. The approach detects changes caused by events to help managers respond rapidly to changes in the external environment. Association rule mining is used to discover event trends (the subject patterns of events) from news stories. The changes can be identified by comparing event trends in different time periods. The empirical evaluation showed that the discovered event changes can support decision-makers by providing up-to-date information about the business environment, which enables them to make appropriate decisions. The proposed approach is practical for business managers to be aware of environmental changes and adjust their business strategies accordingly. (C) 2007 Elsevier Ltd. All rights reserved.