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Intelligent compilation of patent summaries using machine learning and natural language processing techniques
(2020-01-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Wu, Jheng-Long
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Wang, Jack W. C.
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管理科學系
Patents are a type of intellectual property with ownership and monopolistic rights that are publicly accessible published documents, often with illustrations, registered by governments and international organizations. The registration allows people familiar with the domain to understand how to re-create the new and useful invention but restricts the manufacturing unless the owner licenses or enters into a legal agreement to sell ownership of the patent. Patents reward the costly research and development efforts of inventors while spreading new knowledge and accelerating innovation. This research uses artificial intelligence natural language processing, deep learning techniques and machine learning algorithms to extract the essential knowledge of patent documents within a given domain as a means to evaluate their worth and technical advantage. Manual patent abstraction is a time consuming, labor intensive, and subjective process which becomes cost and outcome ineffective as the size of the patent knowledge domain increases. This research develops an intelligent patent summarization methodology using artificial intelligence machine learning approaches to allow patent domains of extremely large sizes to be effectively and objectively summarized, especially for cases where the cost and time requirements of manual summarization is infeasible. The system learns to automatically summarize patent documents with natural language texts for any given technical domain. The machine learning solution identifies technical key terminologies (words, phrases, and sentences) in the context of the semantic relationships among training patents and corresponding summaries as the core of the summarization system. To ensure the high performance of the proposed methodology, ROUGE metrics are used to evaluate precision, recall, accuracy, and consistency of knowledge generated by the summarization system. The Smart machinery technologies domain, under the sub-domains of control intelligence, sensor intelligence and intelligent decision-making provide the case studies for the patent summarization system training. The cases use 1708 training pairs of patents and summaries while testing uses 30 randomly selected patents. The case implementation and verification have shown the summary reports achieve 90% and 84% average precision and recall ratios respectively.
IP portfolios and evolution of biomedical additive manufacturing applications
(2017-04-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Chung, Curry L. S.
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管理科學系
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Department of Management Science
Additive manufacturing (AM) or 3D printing includes techniques capable of manufacturing regular and irregular shapes for small batches of customized products. The ability to customize unusual shapes makes the process particularly suitable for prosthetic products used in biomedical applications. AM adoption in the field of biomedical applications (called bio-AM in this research) has seen significant growth over the last few years. This research develops an Intellectual Property (IP) analytical methodology to explore the portfolios and evolution of patents, as well as their relevance to Taiwan's Ministry of Science and Technology (MOST) research projects in bio-AM domain. Specifically, global and domestic IP portfolios for bio-AM innovations are studied using the proposed method. First, the domain documents (of US patents and MOST projects) are collected from a global patent database and MOST project database. The key term frequency counts and technical clustering analysis of the collected documents are derived. The key terms and appearance frequencies in documents form the basis for document clustering and similarity analysis. The ontology of bio-AM is constructed based on the clustering results. Finally, the patents and projects in the adjusted clusters are subject to evolution analysis using concept lattice analysis. This research provides a computer supported IP evolution analysis system, based on the developed algorithms, for the decision support of IP and R&D strategic planning.
Consumer driven product technology function deployment using social media and patent mining
(2018-04-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Fan, Chin-Yuan
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Lee, Ian J. Y.
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管理科學系
The capability of identifying real-time customer needs is critical for manufacturers that provide short life cycle consumer products such as smart phones. Companies need to form research and development (R&D) strategies to improve key functional features for short lifespan products to reflect the adoption of innovative technologies and changing customer expectations. With the pervasive use of the Internet, this research crawls and analyzes the online voice of customers (VoC), overcoming the time lag of offline surveys, to identify and prioritize product functions for deployment using extended quality function deployment (eQFD) models. In this research, the novel analytics of the manufacturer's patent portfolio is added as an additional eQFD dimension to map ranked functional improvements to a manufacturer's R&D capabilities. Thus, a computer supported eQFD system is developed to perform the unique mappings and gap analyses between the VoC, the prioritized product functions, and the manufacturer's patent portfolio. The newly developed eQFD methodology and its novel discoveries are demonstrated in detail using a case study of three smart phones launched during the same time frame. The products include the Samsung Galaxy 57, the Huawei Honor 5X, and the ASUS Zenfone 3. The newly developed methodology is generally applicable to support VoC-centric product function deployment and R&D strategic planning in other domains.
A review of essential standards and patent landscapes for the Internet of Things: A key enabler for Industry 4.0
(2017-08-01)
Trappey, Amy J. C.
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Trappey, Charles V.
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Govindarajan, Usharani Hareesh
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Chuang, Allen C.
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Sun, John J.
This paper is a formal overview of standards and patents for Internet of Things (IoT) as a key enabler for the next generation advanced manufacturing, referred as Industry 4.0 (I 4.0). IoT at the fundamental level is a means of connecting physical objects to the Internet as a ubiquitous network that enables objects to collect and exchange information. The manufacturing industry is seeking versatile manufacturing service provisions to overcome shortened product life cycles, increased labor costs, and fluctuating customer needs for competitive marketplaces. This paper depicts a systematic approach to review IoT technology standards and patents. The thorough analysis and overview include the essential standard landscape and the patent landscape based on the governing standards organizations for America, Europe and China where most global manufacturing bases are located. The literature of emerging IoT standards from the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC) and the Gtiobiao standards (GB), and global patents issued in US, Europe, China and World Intellectual Property Organization (WIPO) are systematically presented in this study. (C) 2016 Elsevier Ltd. All rights reserved.
Using patent data for technology forecasting: China RFID patent analysis
(2011-01-01)
Trappey, Charles V.
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Wu, Hsin-Ying
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Taghaboni-Dutta, Fataneh
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Trappey, Amy J. C.
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管理科學系
China is one of the world's largest manufacturers and consumers of Radio Frequency Identification (RFID) applications. Current estimates show that China will need over 3 billion RFID tags to satisfy demand in the year 2009. The applications for RFID patents have spread across a very diverse range of inventions and in the future it is likely that most products manufactured in China will contain an RFID tag. China's RFID industry has grown along with the demand and researchers are making significant technological advances. In this research, patent data from the State Intellectual Property Office of the People's Republic of China (SIPO) have been used to explore RFID technology development and its trends. Patent abstracts containing the keyword and phrase "RFID" and "Radio Frequency Identification" were collected for analysis, content extraction, and clustering. In total, 1389 patents from the SIPO database covering the years 1995-2008 were retrieved and archived for analysis. Patents provide exclusive rights and legal protection for inventors, play an important role in the development and fair diffusion of technology, and contain detailed specifications necessary to define and protect the boundaries of an invention. Through patent analysis, companies monitor the development of technology and evaluate the position of potential competitors in the market. This research introduce a methodology which combines patent content clustering and technology life cycle forecasting to find a niche space of RFID technology development in China. A patent content clustering method is used to cluster different patent documents into homogenous groups, and then technology forecasting is applied to evaluate possible market opportunities for future inventors and investors. The results suggest that the cluster called RFID wireless communication devices has entered the saturation stage and thus provides limited opportunity for development. Four other clusters; RFID concepts and applications, RFID architecture, RFID tracking implementation, and RFID transmission apparatus, have entered the mature stage. The RFID frequency and waves cluster appears to be in early growth stage with good development potential. Since the technology related to basic RFID concepts and devices has reached a mature stage in China, the research and development seems to be targeting the improvement of the RFID frequencies and waves as a means to develop more reliable RFID systems and applications. (C) 2010 Elsevier Ltd. All rights reserved.
A knowledge centric methodology for dental implant technology assessment using ontology based patent analysis and clinical meta-analysis
(2014-04-01)
Trappey, Charles V.
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Trappey, Amy J. C.
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Peng, Hsin-Yi
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Lin, Li-Deh
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Wang, Tong-Mei
The medical equipment industry has been one of the fastest growing sectors of the decade with predicted global sales reaching US$ 430 billion in 2017 [22]. During the period from 1995 to 2008, the patent applications in medical technology increased rapidly worldwide (World Intellectual Property Organization, 2012). Patent analysis, although useful in forecasting technology development trends, has posed a challenging analysis task since the volume and diversity of new patent applications has surpassed the ability of regular firms and research teams to process and identify relevant information. Further, medical related technologies rely on clinical trials to validate and gain regulatory approval for patient treatment even though patents, protecting the intellectual property rights of inventors, have been granted. This research focuses on developing a knowledge centric methodology and system to analyze and assess viable medical technology innovations and trends considering both patents and clinical reports. Specifically, the design innovations of dental implant connections are used as a case study. A novel and generic methodology combining ontology based patent analysis and clinical meta-analysis is developed to analyze and identify the most effective patented techniques in the dental implant field. The research establishes and verifies a computer supported analytical approach and system for the strategic prediction of medical technology development trends. (C) 2014 Elsevier Ltd. All rights reserved.

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