Browsing by Author "Chen, Lynn W. L."

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Computer Supported Formal Concept Analysis to Explore the Evolution of Patent Litigation
(2015)
Trappey, Amy J. C.
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Chen, Lynn W. L.
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Trappey, Charles V.
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管理科學系
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Department of Management Science
For competitive global markets, patents are useful for protecting intellectual property rights (IPRs) or defending against infringement threats from competitors. In the mobile communication industry, companies such as Qualcomm and Ericsson dominate the industry with essential patents. Some non-practicing entities (NPEs) seek opportunities to license their IPRs and often challenge infringement of their IPRs by manufacturers or channel distributors. In order to avoid NPE patent infringement litigation and understand the patterns of NPE legal actions, this research focuses on developing a computer supported intelligent method to analyze patent and IP infringement litigation and their evolution trends. We use modified formal concept analysis to explore the evolution of the patent infringement lawsuits and their disputed patents. Two modified FCA models are constructed to observe the evolution of the court cases and the disputed patents. The research provides companies with technical references based on the evolutions of litigation and patent trends for future strategic R&D directions.
Using the Collective Intelligence of Sports Fans to Improve Professional Football League Customer Service
(2014-01-01)
Trappey, Charles
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Smith, Peter
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Trappey, Shelby
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Chen, Lynn W. L.
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Tung, Jasmine T. C.
This research investigates sports fans\' emotions and feelings toward a professional football game as a unique event offering related services, crowd interactions, performances, incidences, and outcomes. Critical incident surveys (open-ended, written text dialogues) were used to identify the most salient positive and negative phrases used to express football fans\' game experiences. The key term frequencies were first analyzed using text and data mining techniques to form the ontological base (with a focus on emotions, feeling, and events); second by building a theory based ontology tree structure; and third by experts abstracting the dialogues into consistent key terms and phases related to a formal ontology structure. The collective intelligence of 37 Green Bay Packers fans\' emotions, feelings, event related incidences, and outcomes were mapped to the derived ontology schema which in turn was re-submitted to the text mining algorithms. The ontology based, collective findings depicts the Green Bay Packers fans\' deep opinions. Given the structured text data results, clusters form a theoretical base for creating initial causal models for future verification. The initial research provides a new means for effectively improving professional sports services, particularly in defining the interrelation of feelings, emotions, events, and the related object properties.