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The R&D output and global commercialization of intellectual properties (IPs), especially patents filed in many countries, have increased dramatically over the past decade. The overwhelming growth in research and IP activities has led to a major challenge to understand and forecast technology development insights and trends. Evidence-based data analytics is essential for technology mining. The assessment of patent values is a critical aspect of technology mining, which remains a highly subjective task performed by domain experts. As businesses become globalized, subjectivity in underlying assessments of large volumes of patent documents leads to overpriced or undervalued IP sales or licensing that exposes stakeholders to legal and financial risks. Thus, the development of intelligent methods for patent valuation requires new research emphasis. This article applies a deep learning analytical method for automatic and intelligent patent value estimation. Principal component analysis (PCA) is used to identify significant patent value indicators from the given patent dataset. Then, deep neural networks (DNN) for value prediction are modeled and trained using the training set. A detailed case study of 6466 manufacturing Internet of Things (IoT) patents is analyzed to demonstrate the improved results of building PCA-preprocessed DNN models to perform patent valuations. Finally, selected higher value IoT patents owned by leading Taiwan assignees are identified and analyzed to verify the technological competitive intelligence.

Natural language processing (NLP) is a critical part of the digital transformation. NLP enables user-friendly interactions between machine and human by making computers understand human languages. Intelligent chatbot is an essential application of NLP to allow understanding of users’ utterance and responding in understandable sentences for specific applications simulating human-to-human conversations and interactions for problem solving or Q&As. This research studies emerging technologies for NLP-enabled intelligent chatbot development using a systematic patent analytic approach. Some intelligent text-mining techniques are applied, including document term frequency analysis for key terminology extractions, clustering method for identifying the subdomains, and Latent Dirichlet Allocation for finding the key topics of patent set. This research utilizes the Derwent Innovation database as the main source for global intelligent chatbot patent retrievals.

Global online sales for products, where many are substantially identical or deceptively similar, are the cause of a growing number of trademark (TM) infringement lawsuits. This research proposes an intelligent trademark legal precedent recommendation system to assist trademark owners to find relevant past cases, laws, and judgments to form legal arguments to defend against infringement. Judicial precedent and applicable laws from the USA are used to construct an ontology of trademark litigation knowledge. The ontology is used to analyze potential infringement cases with similar laws and precedents used to resolve previous legal disputes. The analysis provides a basis for proceeding with legal action necessary to protect a company's brand equity when arguing potential trademark infringement. Using the Python programming language, the precedent-based recommendation system provides a means for continuously updating trademark case data and assists TM owners to quickly identify similar cases to support infringement allegations.

The growth of global patents increased over the last decade as enterprises and inventors sought greater protection of their intellectual property (IP) rights. Global patents represent state-of-the-art knowledge for given domains. This research develops a hierarchical Latent Dirichlet Allocation (LDA)-based approach as a computational intelligent method to discover topics and form a top-down ontology, a semantic schema, representing the collective patent knowledge. To validate the knowledge extraction, 1,546 smart retailing patents collected from the Derwent Innovation platform from 2011 and 2016 are used to build the domain ontology schema. The patent set focuses on in-use, globally established, and non-disputed IP covering payment, user experience, and information integration for smart retailing. The clustering and LDA-based ontology system automatically build the knowledge map, which identifies the technology trends and the technology gaps enabling the development of competitive R&D and management strategies.

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.

Driving phobia is a widespread anxiety disorder in modern society. Driving phobia disorders often cause difficulties in people&rsquo, s professional and social activities. A growing trend for treating driving phobia is to apply virtual reality exposure therapy (VRET). We refined the system&rsquo, s performance based on the previous research publication and have conducted a VRET pre-test research study with treatment and control group subjects to demonstrate the effectiveness of VRET. Some systemic problems were discovered in the first published experiment. For example, the experimental process and the virtual reality (VR) driving scenarios had to be modified to reflect realistic scenarios causing the fear of driving. These issues were identified and improvements made and verified in this research. A total of 130 subjects completed the driving behavior survey. Thirty subjects were randomly drawn from the subjects with high driving fear questionnaire scores and were invited to participate in the treatment experiments. The latest research presents the refined VRET for driving phobia disorders, including the revised system framework, the main modules and integration, and the subjects&rsquo, biodata collection, management, and analysis. The experiment results provide strong evidence that the refined VRET design helps subjects overcome driving phobias. The subjects&rsquo, subjective distress and fear of driving are reduced significantly through the implementation of VRET.

Mixed Reality (MR) is an advanced technology designed to enhance users’ experience and perception of interactions with objects in both virtual and real worlds. MR has enormous potential for improving human-cyber physical systems (HCPS) in the manufacturing industry. The transdisciplinary engineering (TE) characteristics of this research emphasize integrating advanced patent mining techniques to trend analysis of innovative MR technologies and applications in the manufacturing. Despite the growing popularity of MR applications, trend analyses in this emerging technology have relied on qualitative literature reviews and insufficient patent macro-analytics. Therefore, mapping patents related to MR inventions provides technology experts with better insights of technology development in the manufacturing. This research consists of a thorough review of non-patent literature (NPL) for the construction of MR domain ontology and a systematic patent mining analysis, including patent map analysis, patent clustering, patent topic modeling, and trend analysis of MR applications. A total of 709 patents from the InnovationQ Plus patent database are systematically identified and analyzed for trend discovery. The key terminologies, top ten patent assignees, top five applicants’ country origins, top six Cooperative Patent Classifications (CPC), and seven technology clusters and topics are identified. These findings provide reliable MR-enabled HCPS suggestions for the manufacturing.

Natural language processing (NLP) is an indispensable part of advancing the AI era, especially in the realm of the human-computer interface/interaction (HCI) for all state-of-the-art software applications. NLP enables interfaces between machines and humans allowing machines/computers/systems to understand human languages and engaging in dialogues. An intelligent chatbot development must incorporate NLP technologies to allow the understanding of users’ utterance and responding in understandable sentences in versatile scenarios. This research investigates the emerging technological trend of intelligent chatbot development. The systematic trend analysis is described in the research. First, patents related to intelligent chatbot domain are retrieved using a well-defined search query. The queries are derived from the knowledge ontology, which is extracted using text-mining algorithms - key term frequency analysis, clustering for sub-domain identification, and Latent Dirichlet Allocation (LDA) for topic modelling. Afterwards, the management and technology maps of a patent portfolio, such as patenting trends and technology function matrix, are extracted and drawn. The technology trend analysis also investigated the distributions of the relevant patent claims for specific industries.

A logo is s graphical emblem or mark used as an identification for a company and its products and services. Logos are legally protected as intellectual properties (IPs) if registered as trademarks (TMs). LogosTM are widely distributed online nowadays in the digital economy. Due to their wide distributions online, the constant checking of TM legal usages becomes extremely challenging in the TM registration and protection system. The fact that users can easily imitate the registered TM logo designs casts serious IP legal issue, which highlights the importance of developing an automatic logo image retrieval system. Considering the complexity of TM visual semantics, this research proposes a deep embedding learning for logo image similarity analysis using triplet-network. We propose the optimization of sampling parameters to improve the TM image retrieval performance with robust model. The research aims to reduce discrepancy between human visual interpretation. This transdisciplinary engineering research incorporates deep learning (DL) modeling and TM legal analysis for image-centric TM protection. To demonstrate the model performance, more than 10,000 images for model training and 3000 images for model testing are adopted from Logo-2K+ database. Image retrieval performance shows excellent results with recall@10 exceeding 93%.

Student life causes many sources of stress due to the requirements of managing schoolwork, family, friends, health and wellbeing, and future career planning. Some students are overwhelmed and lack resilience to overcome stress, especially if they are inexperienced in managing setbacks, fail to achieve expectations, or lack skills to independently manage social skills, recreation, and study time. The long-term accumulation of stress has a negative impact on students’ physical and mental health, and may lead to a range of symptoms such as depression, anxiety, headache, insomnia, and eating disorders. Although most universities provide psychological counseling services, there is often a shortage of professional psychologists, which leads to students suffering from stress for longer than necessary without immediate assistance. The build-up of stress can lead to tragic consequences including abnormal reasoning, anti-social behavior, and suicide. There should never be a need for a student to wait more than a month to make an appointment for counseling services and every request for help should be immediately addressed and assessed. In this research, we designed a unique test platform for an immersive virtual reality group chatbot counseling system so students can receive psychological help and stress management counseling anytime and anywhere. First, the research used questionnaires to measure the stress levels and identifies how stress affects their lives. An immersive virtual reality chatbot was developed using professional psychological counseling knowledge that can provide answers during individual or group counseling sessions. Students can log in to the platform as avatars and ask the chatbot questions or interact with other students on the platform. This research provides college students with a new technology-based counseling environment designed to help relieve stress and learn new ways to improve student life quality from others. The platform provides a test base for future clinical trials to evaluate and improve the automated virtual reality chatbot counseling system.

Researchers must read and understand a large volume of technical papers, including patent documents, to fully grasp the state-of-the-art technological progress in a given domain. Chemical research is particularly challenging with the fast growth of newly registered utility patents (also known as intellectual property or IP) that provide detailed descriptions of the processes used to create a new chemical or a new process to manufacture a known chemical. The researcher must be able to understand the latest patents and literature in order to develop new chemicals and processes that do not infringe on existing claims and processes. This research uses text mining, integrated machine learning, and knowledge visualization techniques to effectively and accurately support the extraction and graphical presentation of chemical processes disclosed in patent documents. The computer framework trains a machine learning model called ALBERT for automatic paragraph text classification. ALBERT separates chemical and non-chemical descriptive paragraphs from a patent for effective chemical term extraction. The ChemDataExtractor is used to classify chemical terms, such as inputs, units, and reactions from the chemical paragraphs. A computer-supported graph-based knowledge representation interface is developed to plot the extracted chemical terms and their chemical process links as a network of nodes with connecting arcs. The computer-supported chemical knowledge visualization approach helps researchers to quickly understand the innovative and unique chemical or processes of any chemical patent of interest.

An ontology-based patent roadmap and valuation analysis approach provides competitive foresights for a corporation's associated patent portfolios and the underlying business processes. To demonstrate the methodology and computer supported analysis system, the patent portfolios of two global smart e-retailers are compared. The patents in each company's portfolio are text mined and categorized based on the smart retailing ontology schema. Both cases' text mining results are plotted as patent portfolio roadmaps, linking patents to innovation categories, commercial applications, and business process models. The three-dimensional (3-D) patent valuation analysis method provides foresights of the companies' competitive advantages related to “patent scope” (PS), “patent importance” (PI), and “patent innovation” (PIN). The research implements a computer supported system to provide evidence how companies utilize patent portfolios as key strategies for protecting business related intellectual properties (IPs) while implementing sustainable and competitive business processes.

Smart Product-service systems (PSS) are the emerging type of PSS that offer market value and dynamic intelligence combining products and services as solutions to consumers based on digital technology. To design a smart PSS with an effective way, a structural design approach is required. Nevertheless, only a few existing researchers discussed this topic. Aiming to bridge this gap, an integrated way is proposed for smart PSS design. This study refines a generic approach for structural service innovation approach which integrates the advantage of PSS engineering and service engineering for designing smart product service systems. The structural design approach is based on the Theory of Inventive Problem Solving (TRIZ) incorporating with service blueprint named PRR method. Three design phases are demonstrated as verification of the PRR method by an elaborated case study of the smart beauty service. Based on PRR, three key design phases are integrated, i.e., (1) problem definition, (2) resolution generation, and (3) resolution design. Empirical results and implications are collected and discussed to obtain valuable insights for value creation.

The current virtual reality (VR) techniques develop immersive environments via inducing illusions to our sense. Nowadays, most of VR focuses on inducing visual illusion. Hence, visual is the most important input channel for experiencing and exploring the VR environments. Recently, extensive research efforts have been put on eye-tracking studies. However, the development and growing trends of the VR-based eye-tracking studies are unrevealed due to the lack of a systematic literature review on it. In this study, we reviewed related literature from 2000 to 2019 and summarized them into two main categories, including eye tracking methods and eye-tracking-enabled applications, such as tracking gaze points to manipulate the VR environment, measuring user states, and evaluating the usability of VR based on eye-tracking data. Based on the literature review, we can find that eye-tracking can assist in developing adaptive VR systems and enhance users experience. While comparing with 2D environments, immersive VR environment still requires more deep studies in eye-tracking.

We argue that the representation and formalization of complex engineering knowledge is the main aim of inquiries in the scientific field of Advanced Engineering Informatics. We introduce ontology and logic as underlying methods to formalize knowledge. We also suggest that it is important to account for the purpose of engineers and the context they work in while representing and formalizing knowledge. Based on the concepts of ontology, logic, purpose, and context, we discuss different possible research methods and approaches that scholars can use to formalize complex engineering knowledge and to validate whether a specific formalization can support engineers with their complex tasks. On the grounds of this discussion, we suggest that Advanced Engineering research efforts should be conducted in a bottom-up manner closely involving engineering practitioners. We also suggest that researchers make use of social science methods while eliciting knowledge to formalize and while validating their formalized knowledge.

Chatbot is a conversational question answering (Q&A) system capable of natural language communication between a computer system and a person. The use of chatbots for 24-hour customer service provides quick responses that solve problems online. This approach is quickly becoming a convenient way for companies to enhance their customer services without location or knowledgeable staff limitations. This research proposes a system framework and develops a prototype virtual reality (VR) enabled transformer mass-customization consultation chatbot. The chatbot technique is a retrieval-based intelligent system. First, thousands of transformer specific frequently asked questions (FAQs) are collected as a Q&A dataset for technical supports retrieval. More than 1.2 million engineering Wikipedia pages and engineering technical papers are used to train a word embedding model used for natural language processing and question-answer retrieval. The chatbot is integrated into a virtual reality (VR) immersive user interface (UI) environment enabling users to make transformer design changes while querying the system about specifications and standards while interacting with 3D models from the company’s knowledge base archive. The system provides two unique UIs for personal computer (PC) and a helmet-based immersive interface. The system supports real-time consultation of mass-customized transformers and their bills of materials (BOM) for design review, analysis and cost estimation.

BACKGROUND Because line width has been close to atom size, for semiconductor industry, except for achieving the target of line width, the cost will be more important. The mask-less laser interference lithography (LIL) technique lowers the cost makes it standing out in the market of lithographic equipment of the excessive cost in the semiconductor industry. METHOD The Keywords of patent retrieval with the theme of LIL are based on the technical features of both the conditions of producing interference lithography and different types of experimental configuration of LIL. Method of patent retrieval include Boolean logic operators are used to express the relationship between sets. Furthermore, it's necessary to find whether common patent classification codes exist in the highly correlated patents and confirm the definition of that. RESULTS The patent review in this research show the patents of LIL technique are classified according to optical method and lithographic equipment. The patents related to optical method of LIL technique take beam splitter based configuration as the main stream; in the technique of lithographic equipment, the patents of system planning technique are the most. CONCLUSION The findings of this review confirm the importance of improving the precision of LIL technique for avoiding the defocus of high-density line width. Besides, it's particularly suitable for the micro nanofluid device in the emerging bionanotechnology to observe fluid behavior at the minimum scale. It doesn't need mask and can produce periodic pattern with nanoscale make it devote to the field of periodicity.

This research develops a computer-supported ontology-based Technology Function Matrix (TFM) construction method, called eTFM, as an approach to reduce technology mining man-power and enhance the accuracy and consistency of patent analysis results. The paper addresses a rarely discussed issue of the TFM validation. The proposed validation approach compares the TFMs construction based on both on the domain ontology and the International Patent Classification (IPC) classes. The research demonstrates the methodology's practical applications using the patent analysis case of cyber physical system (CPS), an essential core technology enabling advanced manufacturing and Industry 4.0.

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 S7, 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.

Digital transformation (DT) is the process of combining digital technologies with sound business models to generate great value for enterprises. DT intertwines with customer requirements, domain knowledge, and theoretical and empirical insights for value propagations. Studies of DT are growing rapidly and heterogeneously, covering the aspects of product design, engineering, production, and life-cycle management due to the fast and market-driven industrial development under Industry 4.0. Our work addresses the challenge of understanding DT trends by presenting a machine learning (ML) approach for topic modeling to review and analyze advanced DT technology research and development. A systematic review process is developed based on the comprehensive DT in manufacturing systems and engineering literature (i.e., 99 articles). Six dominant topics are identified, namely smart factory, sustainability and product-service systems, construction digital transformation, public infrastructure-centric digital transformation, techno-centric digital transformation, and business model-centric digital transformation. The study also contributes to adopting and demonstrating the ML-based topic modeling for intelligent and systematic bibliometric analysis, particularly for unveiling advanced engineering research trends through domain literature.

Suppliers are adjusting from the order-to-order manufacturing production mode toward demand forecasting. In the meantime, customers have increased demand uncertainty due to their own considerations, such as end-product demand frustration, which leads to suppliers’ inaccurate demand forecasting and inventory wastes. Our research applies ARIMA and LSTM techniques to establish rolling forecast models, which greatly improve accuracy and efficiency of demand and inventory forecasting. The forecast models, developed through historical data, are evaluated and verified by the root mean squares and average absolute error percentages in the actual case application, i.e., the orders of IC trays for semiconductor production plants. The proposed ARIMA and LSTM are superior to the manufacturer’s empirical model prediction results, with LSTM exhibiting enhanced performance in terms of short-term forecasting. The inventory continued to decline significantly after two months of model implementation and application.

Industry 4.0 enables the management of factories manufacturing products with complexity and flexibility. The corresponding logistic services must provide greater accuracy and efficiency in logistic...

The rapid evolution of new service systems raises crucial challenges for service design and requires effective methods. This study depicts a conceptual service design framework, called design-oriented systematic inventive thinking (DSIT) approach, which can be applied in different problem contexts. DSIT is presented as a new systematic and collaborative intelligence approach for creating and evaluating complex service systems using multi-criteria data analytics. DSIT synthesizes the current field of TRIZ service-design knowledge system and the emerging area of non-TRIZ service-design knowledge system. DSIT enables integrated development of service offerings at four dimensions and provides the matching integrated service design approach for each dimension. Four types of service design approaches are conceptualized as human-independent service engineering, problem-clarified service engineering, solution-converged service engineering, and designing for service. A new service computer-aided design system (service CAD) named DSIT explorer is developed consisting of customization, compatibility, and extensiveness of DSIT modules. A pervasive and smart collaborative service system (i.e., the smart MOS burger service solution) designed using DSIT explorer is illustrated. DSIT is a holistic, interdisciplinary, and collaborative service design concept, which is incorporated into a collaborative and intelligent service CAD framework to enable systematic inventive thinking throughout phases of service design lifecycle from problem definition, problem resolution, to solution evaluation.

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 Guobiao standards (GB), and global patents issued in US, Europe, China and World Intellectual Property Organization (WIPO) are systematically presented in this study.

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.

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.

Technical or knowledge documents, such as research papers, patents, and technical documents, e.g., request for quotations (RFQ), are important knowledge references for multiple purposes. For example, enterprises and R&D institutions often need to conduct literature and patent searches and analyses before, during, and after R&D and commercialization. These knowledge discovery processes help them identify prior arts related to the current R&D efforts to avoid duplicating research efforts or infringing upon existing intellectual property rights (IPRs). It is common to have many synonyms (i.e., words and phrases with near-identical meanings) appeared in documents, which may hinder search results, if queries do not consider these synonyms. For instance, conducting “freedom-to-operate” (FTO) patent search may not find all related patents if synonyms were not taking into consideration. This research develops methodologies of generating domain specific “word” and “phrase” synonym dictionaries using machine learning. The generation and validation of both domain-specific “word” and “phrase” synonym dictionaries are conducted using more than 2000 solar power related patents as testing document set. The testing result shows that, in the solar power domain, both word level and phrase level dictionaries identify synonyms effectively and, thus, significantly improve the patent search results.

The rapid development of consumer products with short life spans, along with fast, global e-commerce and e-marketing distribution of products and services requires greater corporate diligence to protect intangible assets such as brands which can easily be coped or placed in grey markets. Trademarks are the government registered legal intellectual property rights (IPRs) used to protect companies’ brands and build brand equity. Given the rapid growth in the number of global trademark registrations, the number of trademark infringement cases is also increasing, a great challenge for the original trademark owner to detect the infringement and takes action to protect the brand image and related commercial interests. This research develops a trademark similarity assessment methodology based on the US trademark law related to the high likelihood of confusion and associated regulations. The research focuses on identifying trade mark image similarity using a deep learning approach. The convolutional neural network (CNN) and Siamese neural network (SNN) algorithms are modeled and trained using Cifar-10 and TopLogo-10 corpuses. These corpuses consist of more than 100,000 positive image pairs and more than 150,000 negative image pairs as training data. After training the model, an image input to the model extracts and recommends similar trade mark images found in the corpus. The solution assists users registering new trademarks to identifying similar marks that may lead to disputes. The solution also automatically screens images to identify marks that potentially infringe upon registered trademarks.

Computer vision (CV) attempts to mimic human eyes for image processing and identifications of detailed visual information, such as object positions, features of appearances, and even human emotions and behaviors. In this research, more than one hundred literatures, relating to applying deep learning (DL) methodologies in advanced computer visions (2010∼2018), are reviewed and analyzed. The objective is to discover the state-of-the-art DL methods, topics, and trends for CV and their practical applications. DL algorithms aim at representing multi-levels of distributed neural networks. Because of the enhancement of high speed computational power, DL modeling, based on accumulated big data analytics, has found practical applications for non-supervised intelligent decision supports, such as detection of product defects and prognosis of machine malfunctions based on real-time signal or feature data analyses. There are a vast number of literature, describing DL related researches, developments, and implementations for problem solving. For the comprehensive mining of the related literature, we integrate Latent Dirichlet Allocation (LDA), K-means (Clustering), and normalized term frequency-inverse document frequency (NTF-IDF) approaches to discover, or called technology mining, of the major trends in DL for computer visions, specifically for key applications in object detection, semantic segmentation, image retrieval, and human pose estimation.

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&amp, D strengths and business interests. The structured solar-related technology mining can be extended to the analysis of other forms of renewable energy development.

Trademarks are used by companies to help customers identify products or services using images or logos in addition to slogans, words, names, sounds, smells, color, and motions. Trademark logos are widely distributed through advertising and published through online media websites and social networks such as Facebook, Pinterest, and Flicker. The intellectual property (IP) rights of the trademark owners have strong legal protection when registered with international intellectual property platforms such as the US Patent and Trademark Office and the World Intellectual Property Office. Using a registered trademark without prior consent of the owner may result in intellectual property infringement with severe legal consequences under civil or criminal law. Companies invest large capital resources in protecting their trademark from being copied or misused in ways that confuse the customers or steal market share. This research focuses on trademark (TM) logo image retrieval systems used in the cyber marketplaces to identify similar TM logo images online automatically and intelligently. The methodology developed for TM logo similarity measurement is based on content-based image retrieval. Content retrieval reduces the gap between high-level semantic interpretation of human vision and the low-level features processed by the machine. The proposed transfer learning methodology uses embedded learning with triplet loss to fine-tune a pre-trained convolutional neural network model. The Logo-2K+ large-scale logo dataset is re-organized and divided into the top 70% as the training set and the remaining 30% as the testing set. The results show that the novel transfer learning approach is developed and demonstrated in this research for the intelligent automatic detection of similar TM logo images with high accuracy. The verification experiments (trained with 7625 logos and tested with 3221 logos) demonstrates that the Recall@10 of the test set can reach 95% using the advanced convolutional neural network model (VGG19) adjusted with the novel transfer learning methodology.

Recommendation systems are widely applied in many fields, such as online customized product searches and customer-centric advertisements. This research develops the methodology for a patent recommender to discover semantically relevant patents for further technology mining and trend analysis. The proposed recommender adopts machine learning (ML) algorithms for natural language processing (NLP) to represent patent documents in vector space and to enable semantic analyses of the patent documents. The ML approach of neural network (NN) language models, trained by domain patent documents (text) as a training set, convert patent documents into vectors and, thus, can identify semantically similar patents using document similarity measures. In particular, the proposed recommender is deployed to in-depth case studies for advanced patent recommendations. The case domain of smart machinery is used to better enable smart manufacturing by incorporating innovative technologies, such as intelligent sensors, intelligent controllers, and intelligent decision making. The research uses six sub-domains in smart machinery technologies as the case studies to verify the superior accuracy and efficacy of the recommender system and methodologies.

When patents support anti-competitive conduct, innovation can be damaged not only by lack of market access but by the prohibitive costs of litigation. The creation of patent barriers is inconsistent with IP protection and enforcement agreed upon under the World Trade Organization and trade-related aspects of intellectual property rights (‘TRIPS’). The study uses the US Apple v HTC IP legal case to investigate anti-competitive market behavior and trade barriers. The research provides a formal patent analytic methodology and the case analysis result. The research method tests whether patent conflicts and technical trade barriers are significantly encouraged by existing IP laws.

Three-dimensional (3D) printing enables products to be custom-designed and produced using additive manufacturing processes. This research develops a patent analysis approach to explore biomedical 3D printing technology trends. First, the method searches for related patents. Second, frequently appearing key terms in patents are extracted as a means to identify key concepts of inventions. Third, in order to structure the domain knowledge, the ontology is created by referring to the relevant key terms and literature. The key terms create indexes used to measure similarities for clustering the patents and sub-technologies. Based on the patent context and dynamic patent concept analysis maps, the evolutionary trends of technology development are depicted. Biomedical 3D printing R&D projects are used as case examples to compare against co-occurring patent evolutions. The proposed patent concept analysis is generalisable for critical decision support of R&D planning and evaluation in any market sector.

A global enterprise must continuously improve the efficiency of logistic operations between supply chain collaborators. Integrating logistic services, resources, and necessary information flows in the supply chain to ensure efficiency and efficacy is critically important to these companies. Global logistic service companies face challenges from their clients to provide logistic services that are cost effective, accurate, and seamlessly integrate material, information and cash flows. In this research, an improved framework for one-stop logistic services is systematically designed, analyzed, and evaluated. The one-stop logistic service framework, defined in four models, is developed to provide enterprises with integrated and comprehensive services within the global supply chain context. The levels of service importance are assessed using a four categories questionnaire. This research provides a case study of the implementation of one-stop logistic services in the distribution industry and demonstrates the framework operating under different demand conditions. Finally, system dynamics causal evaluation is used to evaluate the advantages of the logistic service framework.

The uptake of solar power globally as an important alternative energy source to fossil fuels, together with a rapid fall in the cost of photovoltaic (PV) systems, has been phenomenal during the past decade. This trend is widely anticipated to continue for the years to come. The decline in PV installation costs, like many other new technologies through history, has been largely driven by the learning curve effect. However, it is suggested that other factors, such as costs of key production inputs and prices of competing technologies, also impact the costs of PV systems. In this study, we construct a hierarchical learning curve model to quantify the effects that various factors have on installation costs of PV systems based on empirical data from Taiwan. The results show that, in addition to the learning curve effect as underpinned by an increase of cumulative PV capacity, reductions to silicon price have significantly contributed to the decline of the final installation costs of PV systems in Taiwan. By quantifying the effects of critical cost factors, the learning curve effects on PV installation costs in Taiwan are defined which enable a more accurate projection of PV installation costs for governments, PV producers, operators and users.

Cyber-physical systems (CPS) are a collection of transformative technologies for managing interconnected physical and computational capabilities. Recent developments in technology are increasing the availability and affordability of sensors, data acquisition systems, and computer networks. The competitive nature of industry requires manufacturers to implement new methodologies. CPS is a broad area of engineering which supports applications across industries, such as manufacturing, healthcare, electric power grids, agriculture, and transportation. In particular, CPS is the core technology enabling the transition from Industry 3.0 to Industry 4.0 (I 4.0) and is transforming global advanced manufacturing. This paper provides a consolidated review of the latest CPS literature, a complete review of international standards, and a complete analysis of patent portfolios related to the 5C’s CPS architecture model by Lee et al. The critical evaluation of international standards and the intellectual property contained in CPS patents is unaddressed by the previous research and will benefit both academic scholars and industry practitioners. The analysis provides a basis for predicting research and development future trends and helps policy makers manage technology changes that will result from CPS in I 4.0. This paper covers the emerging I 4.0 standards from the International Organization for Standardization, the International Electrotechnical Commission, and China’s Guobiao standards followed by a patent analysis covering global patents issued in the U.S., Europe, China, and the World Intellectual Property Organization.