Chun-yi Wu

National Tsing Hua University, Industrial Engineering and Engineering Management, Post-Doc

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Chun-Yi, Wu (SquallWu). In my professional experience, I am a big data section manager and a project manager in System Integration enterprise for developing predictive model, and I have been a founder for providing patent text mining and patent map analysis platform. Moreover, I have been an assistant professor (Taiwan). And, my research interests include Industry 4.0, Machine Learning, Natural Language Processing, Text Mining and Patent Analysis.

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Julita Vassileva

University of Saskatchewan

Cristina Bicchieri

University of Pennsylvania

Manuel David Masseno

Instituto Politécnico de Beja

Massimiliano M. Schiraldi

"Tor Vergata" University of Rome

Simon Fong

University of Macau

Maurizio Forte

Duke University

Armando Marques-Guedes

UNL - New University of Lisbon

Nader Ale Ebrahim نادر آل ابراهیم

Alzahra University

Shaharudin Ismail

Universiti Sains Islam Malaysia (USIM)

Susana Nila-Enríquez

Tecnológico de Monterrey

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Papers by Chun-yi Wu

Forecasting dental implant technologies using patent analysis

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ISO14051-based Material Flow Cost Accounting system framework for collaborative green manufacturing

Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2013

ABSTRACT Manufacturers and other businesses are under increasing pressure to improve productivity... more ABSTRACT Manufacturers and other businesses are under increasing pressure to improve productivity while reducing environmental impact. An environmental management accounting approach, called Material Flow Cost Accounting (MFCA), was developed in Germany in late 1990s and, since then, was adopted widely in Japan and other countries. MFCA is a management information system specializing in tracing all input materials flowing through production processes and measuring outputs in finished goods and wastes. To standardize MFCA practices, working group (WG) 8 of ISO technical committee ISO/TC 207 has developed and officially announced ISO 14051 framework in 2011. InnoLux Corporation (InnoLux) is one of global manufacturers which first introduced MFCA in LCD and optoelectronic industry. InnoLux adopted MFCA in four collaborative factories located in Nanhai area, Guangdong Province, P.R. China. This research has two main goals. First, we develop the framework of ISO14051-complied MFCA information system. Second, the case study is carried out to analyze and compare the performance before and after MFCA implementation in InnoLux&#39;s Nanhai factories for InnoLux&#39;s pursuit of collaborative green manufacturing.

Automatic patent document summarization for collaborative knowledge systems and services

Engineering and research teams often develop new products and technologies by referring to invent... more Engineering and research teams often develop new products and technologies by referring to inventions described in patent databases. Efficient patent analysis builds R&D knowledge, reduces new product development time, increases market success, and reduces potential patent infringement. Thus, it is beneficial to automatically and systematically extract information from patent documents in order to improve knowledge sharing and collaboration among R&D team members. In this research, patents are summarized using a combined ontology based and TF-IDF concept clustering approach. The ontology captures the general knowledge and core meaning of patents in a given domain. Then, the proposed methodology extracts, clusters, and integrates the content of a patent to derive a summary and a cluster tree diagram of key terms. Patents from the International Patent Classification (IPC) codes B25C, B25D, B25F (categories for power hand tools) and B24B, C09G and H011 (categories for chemical mechanical polishing) are used as case studies to evaluate the compression ratio, retention ratio, and classification accuracy of the summarization results. The evaluation uses statistics to represent the summary generation and its compression ratio, the ontology based keyword extraction retention ratio, and the summary classification accuracy. The results show that the ontology based approach yields about the same compression ratio as previous non-ontology based research but yields on average an 11% improvement for the retention ratio and a 14% improvement for classification accuracy.

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An Intelligent System for Automated Binary Knowledge Document Classification and Content Analysis

by Chun-yi Wu and Charles Trappey

Many companies rely on patent engineers to search patent documents and offer recommendations and ... more Many companies rely on patent engineers to search patent documents and offer recommendations and advice to R&D engineers. Given the increasing number of patent documents filed each year, new means to effectively and efficiently identify and manage technology specific patent documents are required. This research applies a back-propagation artificial neural network (BPANN), a hierarchical ontology technique, and a normalized term frequency (NTF) method to develop an intelligent system for binary knowledge document classification and content analysis. The intelligent system minimizes inappropriate patent document classification and reduces the effort required to search and screen patents for analysis. Finally, this paper uses the design of light emitting diode (LED) lamps as a case study to illustrate and verify the efficiency of automated binary knowledge document classification and content analysis.

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A patent quality analysis for innovative technology and product development

by Charles Trappey and Chun-yi Wu

Advanced Engineering Informatics, 2012

Enterprises evaluate intellectual property rights and the quality of patent documents in order to... more Enterprises evaluate intellectual property rights and the quality of patent documents in order to develop innovative products and discover state-of-the-art technology trends. The product technologies covered by patent claims are protected by law, and the quality of the patent insures against infringement by competitors while increasing the worth of the invention. Thus, patent quality analysis provides a means by which companies determine whether or not to customize and manufacture innovative products. Since patents provide significant financial protection for businesses, the number of patents filed is increasing at a fast pace. Companies which cannot process patent information or fail to protect their innovations by filing patents lose market competitiveness. Current patent research is needed to estimate the quality of patent documents. The purpose of this research is to improve the analysis and ranking of patent quality. The first step of the proposed methodology is to collect technology specific patents and to extract relevant patent quality performance indicators. The second step is to identify the key impact factors using principal component analysis. These factors are then used as the input parameters for a back-propagation neural network model. Patent transactions help judge patent quality and patents which are licensed or sold with intellectual property usage rights are considered high quality patents. This research collected 283 patents sold or licensed from the news of patent transactions and 116 patents which were unsold but belong to the technology specific domains of interest. After training the patent quality model, 36 historical patents are used to verify the performance of the trained model. The match between the analytical results and the actual trading status reached an 85% level of accuracy. Thus, the proposed patent quality methodology evaluates the quality of patents automatically and effectively as a preliminary screening solution. The approach saves domain experts valuable time targeting high value patents for R&D commercialization and mass customization of products.

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Develop Non-Exhaustive Overlapping Partitioning Clustering for Patent Analysis Based on the Key Phrases Extracted Using Ontology Schema and Fuzzy Adaptive Resonance Theory

by Chun-yi Wu and Charles Trappey

Advanced Concurrent Engineering, 2009

... Therefore, the Cv and Mdv are defined as Formula 9. Finally, these objectives of non-exhausti... more

Using Patent Ontology Engineering for Intellectual Property Defense Support System

by Chun-yi Wu and Charles Trappey

Intelligent patent recommendation system for innovative design collaboration

by Chun-yi Wu, Amy Trappey, Chin-YUAN Fan, and Charles Trappey

Journal of Network and Computer Applications, 2013

Patents' search is increasingly critical for a company's technological advancement and sustainabl... more Patents' search is increasingly critical for a company's technological advancement and sustainable marketing strategy. When most innovative designs are created collaboratively by a diverse team of researchers and technologists, patent knowledge management becomes time consuming with repeated efforts creating additional task conflicts. This research develops an intelligent recommendation methodology and system to enable timely and effective patent search prior, during, and after design collaboration to prevent potential infringement of existing intellectual property rights (IPR) and to secure new IPR for market advantage. The research develops an algorithm to dynamically search related patents in global patent databases. The system clusters users with similar patent search behaviors and, subsequently, infers new patent recommendations based on inter-cluster group member behaviors and characteristics. First, the methodology evaluates the filtered information obtained from collaborative patent searches. Second, the system clusters existing users and identifies users' neighbors based on the collaborative filtering algorithm. Using the clusters of users and their behaviors, the system recommends related patents. When collaborative design teams are planning R&D policies or searching patents and prior art claims to create new IP and prevent or settles IP legal disputes, the intelligent recommendation system identifies and recommends patents with greater efficiency and accuracy than previous systems and methods described in the literature.

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