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

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Computer Supported Comparative Analysis of Technology Portfolios for LTE-A Patent Pools
(2016)
Trappey, Amy J. C.
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Chen, Luna W. L.
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Trappey, Charles V.
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Wang, Andy Y. T.
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管理科學系
With the rapid development of LTE-Advanced (LTE-A) mobile communication technology, companies continue to file for new patents or obtain licenses from patent assignees to secure the right to use LTE-A related intellectual properties (IPs) for product commercialization. There are many standard essential patents (SEPs) complying with LTE-A specifications set by the third generation partnership project (3GPP). LTE-A patent pools are collaborative efforts to gather SEPs from IP owners to reduce the process time of SEP licensing for manufacturers. This research develops the computer supported analysis process for the comparative study of two major LTE-A patent pools (i.e., Sisvel and Via Licensing). The first step is to search the ETSI database for LTE-A SEPs. The second step analyzes the patents in both pools based on IPC classification and LTE-A ontology definition. The main goal of the research is to reveal the patent profiles of LTE-A patent pools for collaborative and collective SEP licensing, which enables LTE-A technology developers and adopters (such as mobile device manufacturers) to fully cooperate in new product development and commercialization efficiently and economically.
Computer-supported portfolio analysis and comparison using ontology-based patent classification mapping scheme: the case of mobile communication patent pools
(2017-06-01)
Trappey, Charles V.
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Trappey, Amy J. C.
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Chen, Luna W. L.
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管理科學系
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Department of Management Science
Mobile communication patents must conform to LTE-Advanced (called fourth generation or 4G) specifications to be categorized as LTE-A standard essential patents (SEPs). Global LTE-A patent pools, such as Sisvel and Via Licensing, collect SEPs from patent assignees, facilitate the SEP licensing processes, and provide fast-forward technical solutions to licensees. This research develops a computer-supported ontology-based patent classification (ontology-IPC) mapping scheme. Two major LTE-A patent pools (Sisvel and Via Licensing) and a national research project pool provide the case study. The research methodology defines the patent pool profiles using visualization maps for collaborative SEPs licensing and provides critical data to avoid re-inventing technologies and infringing upon existing intellectual property. The research enables mobile network and device developers to work cooperatively to develop fast, dependable, and interoperable products and services. The ontology-IPC analysis of the data demonstrates the approach's versatile application to support national project research reviews.
Using Web Mining and Perceptual Mapping to Support Customer-Oriented Product Positions and Designs
(2016)
Trappey, Amy J. C.
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Trappey, Charles V.
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Chang, Ai-Che
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Chen, Luna W. L.
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管理科學系
In recent years, many e-commerce websites provide consumer feedback functions and social networks, allowing customers to share their purchasing and usage experiences online. Companies collect and analyze information from customers\' reviews through the platform to understand the customers impressions of the products they purchased. Online customer reviews has been widely regarded as an important source of information influencing customers buying decisions. In addition, online customer reviews help companies to redesign their products with key features that better positions to target customers in promising market sectors. This research uses online customer reviews as the business intelligence (BI) corpus. After determining the source webpage of customer reviews, a web crawler is needed to collect customer review text. Afterwards, computer-assisted text mining, clustering analysis, and perceptual mapping are applied to develop a formal methodology to compare similar products in a given domain. In this research, the consumer electronic sector is studied. Mobile phone customer reviews are web crawled, collected, mined, and analyzed. The study assists mobile phone manufacturers to understand the voice of customers in both positive and negative perspectives of post-purchasing experiences. The customer-preferred product functions, hardware/software/app features, and price positions, as key business intelligence, are derived for new product designs and market launches.
Web Mining Customer Perceptions to Define Product Positions and Design Preferences
(2020-04-01)
Chang, Ai-Che
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Trappey, Charles, V
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Trappey, Amy J. C.
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Chen, Luna W. L.
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交大名義發表
E-commerce provides a global platform supporting product transactions through the consumer purchase lifecycle including communications of perceived satisfaction and dissatisfaction. The customer feedback functions and social networks of many e-commerce websites allow for the creation of extremely large databases that can be mined to model the customers' perceptions toward online purchases. This research uses online customer reviews as the business intelligence corpus to help companies redesign products that better satisfy consumer preferences and differentiate their product offerings. After identifying the specific webpages of customer reviews, a web crawler collects review text. Computer-supported text mining, cluster analysis, and perceptual mapping are combined as a systematic analytic approach to compare products in a given domain. The study assists phone manufacturers to understand the positive and negative perceptions of customers related to their post-purchase experiences. The customer-preferred product functions, features, and price positions provide valuable strategic intelligence for new product designs and market differentiation.