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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Author: search.filters.author.Trappey, Amy J. C.Subject: search.filters.subject.text mining

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Now showing 1 - 5 of 5
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    A Machine Learning Approach for Solar Power Technology Review and Patent Evolution Analysis
    (2019-04-01)
    Trappey, Amy J. C.
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    Chen, Paul P. J.
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    Trappey, Charles, V
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    Ma, Lin
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    管理科學系
    Solar power systems and their related technologies have developed into a globally utilized green energy source. Given the relatively high installation costs, low conversion rates and battery capacity issues, solar energy is still not a widely applied energy source when compared to traditional energy sources. Despite the challenges, there are many innovative studies of new materials and new methods for improving solar energy transformation efficiency to improve the competitiveness of solar energy in the marketplace. This research searches for promising solar power technologies by text mining 2280 global patents and 5610 literature papers of the past decade (January 2008 to June 2018). First, a solar power knowledge ontology schema (or a key term relationship map) is constructed from the comprehensive literature and patent review. Non-supervised machine learning techniques for clustering patents and literature combined with the Latent Dirichlet Allocation (LDA) topic modeling algorithm identify sub-technology clusters and their main topics. A word-embedding algorithm is applied to identify the patent documents of the specified technologies. Cross-validation of the results is used to model the technology progress with a patent evolution map. Initial analysis show that many patents focus on solar hydropower storage systems, transferring light generated power to waterpower gravity systems. Batteries are also used but have several limitations. The objectives of this research are to review solar technology development progress and describe the innovation path that has evolved for the solar power domain. By adopting unsupervised learning approaches for literature and patent mining, this research develops a novel technology e-discovery methodology and presents the detailed reviews and analyses of the solar power technology using the proposed e-discovery workflow. The insights of global solar technology development, based on both comprehensive literature and patent reviews and cross-analyses, helps energy companies select advanced technologies related to their key technical R&D strengths and business interests. The structured solar-related technology mining can be extended to the analysis of other forms of renewable energy development.
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    Automated patent document summarization for R&D intellectual property management
    (2006)
    Trappey, Amy J. C.
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    Trappey, Charles V.
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    Kao, Burgess H. S.
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    交大名義發表
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    National Chiao Tung University
    In an era of rapid information expansion, people encounter huge amounts of intellectual property (IP) such as patents in digital format. These documents are usually too numerous to be fully utilized in R&D for new product designs. Therefore, efficient and effective ways of acquiring, organizing and presenting IPs (e.g., patent documents) have become very important for enterprises. In this paper, we propose a patent document summarization system using an integrated approach of key-phrase recognition and significant information density. Significant information density or information mass is calculated based on the summation of key-phrases, their relevant phrases, title phrases, domain-specific phrases, indicator phrases and topic sentences, divided by the total number of phrases in a paragraph or a document. External text mining game, compression ratio and retention ratio are used in system experiment and evaluation. This research enables enterprises to organize knowledge and intellectual assets efficiently and to peruse IP documents effectively.
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    Computer supported ontology-based patent analysis considering business processes and strategic patent portfolio management
    (2015)
    Trappey, Amy J. C.
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    Tung, Jasmine T. C.
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    Trappey, Charles
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    Wang, Tong-Mei
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    Tang, Mark Y. L.
    Two approaches are frequently used to study the critical technology and market trends of industry. One analyzes the business processes from the market perspective with statistics market survey. The other perspective predicts new technologies from the patent (intellectual property) data. Business process analysis is limited to analyzing existing technologies and market applications. Since the service delivery processes are not frequently linked to the intellectual property of the underlying technologies, applying a patent perspective enables the analysis of technology trends, technology ownership, and royalty payment analysis. These data are used to illustrate the technology life cycles, predict emerging trends, and identify research opportunities with Computer Supported tools. Patent analysis enables the predictions of technology trends but poorly links the technology to actual products for market adoption cause of the large amount of the data. This research develops an innovative computer supported approach for technology analysis combining both market and patent perspectives. The business processes of a given knowledge domain, driven by the market perspective, is analyzed to build the basis of the domain\'s ontology. Afterward, the key phrases of the business processes are added to form the ontology schema. The related patents are then systematically linked to the business process schema nodes. The research approach enables the business process-oriented patent portfolios among current and potential competitors to be mapped and critically compared from both market performance and technology ownership perspectives. The methodology allows in-depth integrated views of rivals\' patenting and business development for strategic R&D management. The case of InvisalignTM innovative orthodontic services and the patent portfolio is studied to demonstrate the proposed methodology.
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    AUTOMATIC PATENT DOCUMENT SUMMARIZATION FOR COLLABORATIVE KNOWLEDGE SYSTEMS AND SERVICES
    (2009-03-01)
    Trappey, Amy J. C.
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    Trappey, Charles V.
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    Wu, Chun-Yi
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    管理科學系
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    Department of Management Science
    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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    A Fuzzy Ontological Knowledge Document Clustering Methodology
    (2009-06-01)
    Trappey, Amy J. C.
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    Trappey, Charles V.
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    Hsu, Fu-Chiang
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    Hsiao, David W.
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    管理科學系
    This correspondence presents a novel hierarchical clustering approach for knowledge document self-organization, particularly for patent analysis. Current keyword-based methodologies for document content management tend to be inconsistent and ineffective when partial meanings of the technical content are used for cluster analysis. Thus, a new methodology to automatically interpret and cluster knowledge documents using an ontology schema is presented. Moreover, a fuzzy logic control approach is used to match suitable document cluster(s) for given patents based on their derived ontological semantic webs. Finally, three case studies are used to test the approach. The first test case analyzed and clustered 100 patents for chemical and mechanical polishing retrieved from the World Intellectual Property Organization (WIPO). The second test case analyzed. and clustered 100 patent news articles retrieved from online Web sites. The third case analyzed and clustered 100 patents for radio-frequency identification retrieved from WIPO. The results show that the fuzzy ontology-based document clustering approach outperforms the K-means approach in precision, recall, F-measure, and Shannon's entropy.