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Now showing 1 - 8 of 8

Patent Analysis of Key Technologies for Smart Retailing and their Projected Economic Impact
(2018-01-01)
Trappey, Amy J. C.
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Wang, W. C.
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Trappey, Charles
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Hsieh, H. I.
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管理科學系
With the growing population of digital users gaining online consumption power, retail industries are facing evolutionary changes in attracting and maintaining online and offline customers. The concepts and implementation of smart malls and retail shops are proposed by retailers or solution providers to satisfy new customer demands. The foundation of smart retailing includes information integration, customer experience, and e-transactions. This research investigates the economic impact of smart retailing and the growth of related intellectual properties (IPs), specifically, the retail domain online related patents. The technological aspect of smart retailing is organized by constructing a patent ontology map to define the domain boundary. The economic impact of technological transformations is studied by measuring the economic growth of the leading regions and countries and the representative patent assignees. Consumption preferences and labor structure changes in relation to the retail industry are analyzed to verify the economic impact of corresponding intellectual properties. The goal of the research is to develop regional development strategies for smart retailing using global patent and economic impact analyses.
A Face-Recognition Approach Using Deep Reinforcement Learning Approach for User Authentication
(2017-01-01)
Wang, Ping
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Lin, Wen-Hui
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Chao, Kuo-Ming
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Lo, Chi-Chun
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資訊管理與財務金融系註：原資管所+財金所
Numerous crime-related security concerns exist in e-commerce transactions recently. User authentication for mobile payment has numerous approaches including face recognition, iris scan, and fingerprint scan to identify user's true identity by comparing the biometric features of users with patterns in the signature database. Existing studies on the face recognition problem focus mainly on the static analysis to determine the face recognition precision by examining the facial features of images with different facial expressions for users rather than the dynamic aspects where images were are often vague affected by lighting changes with different poses. Because the lighting, facial expressions, and facial details varied in the face recognition process. Consequently, it limits the effectiveness of scheme with which to determine the true identity. Accordingly, this study focused on a face recognition process under the situation of vague facial features using deep reinforcement learning (DRL) approach with convolutional neuron networks (CNNs) thru facial feature extraction, transformation, and comparison to determine the user identity for mobile payment. Specifically, the proposed authentication scheme uses back propagation algorithm to effectively improve the accuracy of face recognition using feed-forward network architecture for CNNs. Overall, the proposed scheme provided a higher precision of face recognition (100% at gamma correction gamma located in [0.5, 1.6]) compared with the average precision for face image (approximately 99.5% at normal lighting gamma = 1) of the existing CNN schemes with ImageNet 2012 Challenge training data set.
Patent Portfolio Analysis of E-payment Services Using Technical Ontology Roadmaps
(2016-01-01)
Trappey, Amy J. C.
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Hsu, Abby P. T.
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Trappey, Charles V.
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管理科學系
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Department of Management Science
Electronic payment (e-payment) is a subset of an e-commerce (EC) and critical to enhance customer loyalty. A well-designed e-payment service creates new commercial advantage and should be protected as intellectual property. This research develops an ontology roadmap using computer assisted methods to classify e-payment patents. The results of roadmap provide patent portfolio visualization which helps EC enterprises utilize strategic information for competitive advantages. This research also employs patent value indicators to benchmark patent portfolios. In the case implementation, two leading EC enterprises, Amazon and Alibaba, are studied using their e-payment patents as input to implement the proposed methodology. The roadmaps and value assessment results help to describe their underlying competitive advantages and provide management implications for business development.
Alignment strategies of AMT with e-commerce setting to improve business strategy in the supply chain operations environment - An empirical study
(2007)
Sha, D. Y.
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Chen, P. K.
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Chen, Yung-Hsin
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工業工程與管理學系
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Department of Industrial Engineering and Management
The purpose of this study is to explore which alignment strategies of advanced manufacturing technology (AMT) with e-commerce setting can develop adequate condition to meet different business strategies requirements in practice. This study is based on 127 Asian samples sieved from the International Manufacturing Strategy Survey (IMSS) database, and applies a hypothesis-test to infer the result. From these Asian samples, we found that the manufacturers usually adopt two business strategies such as product strategy, and high-quality product/delivery strategies to maintain or improve competitive advantage. To As to what business strategies can be successfully put into practice, the results of test indicates that two different alignment strategies of AMT with e-commerce setting can develop appropriate condition to satisfy the requirements of two kind of business strategies. Our finding provides the insight very beneficial for manufacturers to reconsider their alignment strategies of AMT with e-commerce setting to meet business strategies in different context.
Using Ontology-Based Patent Informatics to Describe the Intellectual Property Portfolio of an E-Commerce Order Fulfillment Process
(2015-01-01)
Hsu, Abby P. T.
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Trappey, Charles V.
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Trappey, Amy J. C.
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管理科學系
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Department of Management Science
Electronic commerce (EC) is the process of selling and buying goods or services through an online platform used for conducting the necessary business communications and transactions for sellers and buyers over the Internet. EC companies sell products online with an emphasis on running the entire supply chain process efficiently. The business processes, that enterprises use to conduct e-commerce business, are quite valuable and can be treated as intellectual properties (IPs). Business method patents provide inventors and enterprises with protection for the unique business process. The United States provides business method patent owners an exclusive IP right for 20 years. A good quality business method patent is considered a powerful and effective tool to generate revenue and bar potential competitors from duplicating the practices. Patent analysis can assist companies in evaluating their business strategies or redesign their business processes. Grouping patent documents and defining a domain ontology helps companies describe technology trends and innovations. This research uses Amazon\'s business processes as a case example to conduct business method patent analysis, particularly considering order fulfillment as a key method to manage inventory and purchase orders. An EC ontology schema is constructed based on the key EC business processes and key-phrase extraction from the patents. Understanding Amazon\'s patents and their relationships to the business process, other EC enterprises can examine their own patents\' strategic strengths and weaknesses. In addition, they can prevent their business processes not to infringe upon existing EC patents.
A Proposal for Intelligent Agents with Adjusting Self-Reputation Capability for Preventing Fraud in Multi-Agent Societies
(2014-03-01)
Wang, Sheng-Wen
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Huang, Chung-Yuan
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Sun, Chuen-Tsai
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資訊工程學系
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Department of Computer Science
Intelligent agents are increasingly being used for tasks such as e-commerce auto-bidding. Cooperation and defection challenges commonly found in multi-agent systems make the design and construction of a reliable reputation mechanism an important research and engineering goal. Here we describe our proposal for an intelligent agent model that addresses learning issues in terms of self-reputation. The agents in our proposed model are capable of evaluating their behaviors based on a mix of public and private interest considerations, and of testing various solutions aimed at mitigating self-discrepancy and meeting social standards. Our results indicate promise for integrating a self-reputation mechanism into a self-learning framework in a manner that encourages the early emergence of social cooperation among multiple agents and specific cooperation with individual agents.
Beyond e-commerce software quality: Web services effectiveness
(2001)
Chiu, HS
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Yang, C
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工業工程與管理學系
;
Department of Industrial Engineering and Management
This article describes a successful experience on a 30 man-month software project of Technology Development Program (TDP) of Ministry Of Economic Affairs (MOEA) in Taiwan. In this research, attention is strictly focused on the interrelationships among key components that link between business processes, architecture-centric, and component-based Systems to surpass the goal of software quality. The project presents a Web Services model for building new era applications. It discusses the definition of Web Services, the generic architecture of a Web Services, and how our solution relates to Microsoft Windows DNA and NET. We then conducted a Web services-enabled ERP application to valid the feasibility of Web services programming model and confirm the corresponding fulfillment of software quality.
Learning search pattern for construction procurement using keyword net
(2005)
Dzeng, RJ
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Wang, SS
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土木工程學系
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Department of Civil Engineering
As more and more procurement websites become available on the Internet, seeking information from websites has become an essential part of a contractor's procurement undertaking. Several e-markets, specifically for construction, have also been established, including bLiquid.com and ProcureZone. However, most websites provide only two primary ways of searching for information, namely by index/menu or by keyword. Instead of relying on the primitive search engines found in most procurement websites, a search guide system could help a user's keyword search by reducing the number of keywords required to find the desired information. Our research recognized that professional procurement experience helped users more effectively carry out website information searches, by using fewer keywords. We planned to capture such experience in order to guide inexperienced users in their search. The research goal was to improve search effectiveness by guiding the user's search using three approaches, namely correction, specification and extension. Based on these three approaches, this research applied the following guides: correction; specification-by-equivalence; specification-by-detail; extension-by-time; extension-by-location; extension-by-team; and extension-by-component. The paper will describe how we classified users for learning credibility, and the learning framework for recording expert users' search patterns. Twelve professionals, using 14 procurement packages, with 64 items in total, evaluated the proposed framework. It will be demonstrated that the proposed learning keyword guide facilitated a dynamic, customized menu and indexing system, and reduced the number of keywords required for the professionals to find the information they desired.

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