Search Results (54)

This study employs text mining techniques to conduct a systematic quantitative analysis of cybersecurity-related scientific publications and technological research. It aims to break through the limitations of traditional unidirectional evolutionary research, reveal the knowledge evolution rules between scientific theories and technical practices in this field, and provide valuable references and decision-making support for optimizing the collaborative innovation ecosystem. Firstly, we took academic papers and patent research on cybersecurity from 2005 to 2024 as the research objects and divided them into ten stages according to the time series. Subsequently, we identified scientific and technological topics and formed science–technology topics to assess their similarity. Then, we selected 3040 pairs of collaborative topic pairs and categorized them into three distinct groups: weak, moderate, and strong correlation. Finally, we constructed a science–technology topic association evolution atlas and analyzed the types of evolutionary pathways of topic associations and their mechanisms of action accordingly. The results demonstrate five evolutionary patterns in science–technology topic associations: division, merging, inheritance, co-occurrence, and independent development. Additionally, the science–technology topics demonstrate a high degree of collaboration, exhibiting a collaborative effect of “initial accumulation–fluctuating differentiation–deep collaboration”. Meanwhile, the correlation evolution of strongly related science–technology topics presents a symmetrical effect of “technology–science–technology” and “science–technology/technology–science”. Full article

Dealing with solid waste has always been a global concern, and construction waste is one of the most important parts. Addressing how to properly dispose of construction waste, reduce its negative environmental impact, and achieve effective resource recycling has emerged as an urgent problem to be solved. Technological innovation underpins efficient waste reduction, reuse, and recycling, but existing research often overlooks systematic and quantitative measurements of innovation initiatives. This study uncovers the development status and trends of construction waste recycling (CWR) technology, identifies key points and potential innovation directions for technological development, and also explores practical strategies to promote technological innovation and industrial growth. Through patent analysis, this study uncovers the current status of technological innovation within China’s CWR industry. A text mining approach is employed to analyze patent texts related to core technologies, explore topic contents, and identify topic intensities and evolution trends. A comparative analysis between China and the global dominant countries in CWR reveals China’s technological strengths and weaknesses. The results indicate that patent applications in China’s CWR industry are substantial, with a rapid growth rate, while its global competitiveness remains weak. The applicants are widely distributed, with traditional enterprises demonstrating strong innovation capabilities, while emerging and small-to-medium enterprises lack vitality. The industry has potential advantages in developing resource recycling devices and construction wastewater treatment technology, but the technological foundation in some other core technologies is weak. This study offers an overview of technological innovation initiatives in the CWR industry, representing a breakthrough in existing research. The findings will assist policymakers in formulating evidence-driven strategies to promote CWR. Full article

The floating liquefied natural gas (FLNG) system is an offshore facility that floats above a natural gas field, directly liquefying natural gas without the need for subsea pipelines. In recent years, there has been growing interest in exploring technological innovations for FLNG systems. As such, advancements could lead to breakthroughs in optimizing layout and operations within the limited space of these platforms. To address this, we first apply a patent mining method to cluster FLNG-related patent texts, identifying the key technological components. We then conduct a morphological analysis to pinpoint potential technological opportunities. In our case study, we identify seven such opportunities, which include a combination of plate-fin heat exchangers, horizontal LNG storage tanks, flexible flowlines, and tail loading methods. These findings offer valuable insights and directions for the future development of FLNG systems. Full article

Although safety technology has recently been shown to prevent occupational incidents, a systematic approach to identifying technological opportunities is still lacking. Incident report documents, containing large volumes of narrative text, are considered valuable resources for predetermining incident factors. Additionally, patent data, as a form of big data from technological sources, is widely utilized to explore potential technology solutions. In this context, this study aims to identify technology opportunities by integrating two types of textual big data: incident documents and patent documents. Text mining and self-organizingmaps are employed to discover applicable technologies for incident prevention, grouping them into five categories, as follows: machine tool work, high-place work, vehicle-related facilities, hydraulic machines, and miscellaneous tools. A gap analysis between incidents and patents is also conducted to assess feasibility and develop a technology strategy. The findings, derived from both types of big data, provide technology solutions that are essential for improving workplace safety and that can be used by business owners and safety managers. Full article

In order to solve the problem of insufficient design applicability in the field of lower limb rehabilitation, such as interaction, experience comfort, and modeling color, a biological excitation function system was used to guide the solution of the functional scheme of lower limb rehabilitation products, and the transformation of lower limb rehabilitation products in functional interaction, experience, and morphological color design driven by biological information-driven cross-domain mapping was improved. We used patent knowledge mining to study the product functional requirements of lower limb rehabilitation products. The results were used to screen the required biological prototypes, and the biological incentives were used to guide the design problems. According to the principle of analogy and similarity calculation, the similarity matrix was obtained, and then the strategy was analyzed. Through the analogy of functional system–product technology engineering systems, the engineering relationship between multi-biological and multi-design elements was determined. We realized the biological replacement and upgrading of product functions under biological stimulation to guide the design of lower limb rehabilitation products. The accurate quantitative biological information of multi-biological analogy fit has the significance of optimizing the training effect, improving the operation efficiency, and improving the morphology and modeling of the lower limb rehabilitation product engineering transformation and design. The acquisition rate of the functional design requirements of lower limb rehabilitation products based on text mining reached 95%, and the accuracy of the biological design prototype obtained through similarity calculation was higher than 79%, which verified the feasibility of the accurate bioinformatics design method and improved the rigor of the bioinformatics biomimetic design method. Full article

This study aims to understand the impacts of climate change and extreme climate events on residential buildings and explore how existing buildings can be adapted to resist these negative impacts. A bibliometric and scientometric analysis was conducted on resilient residential retrofits to highlight the prevalent themes, critical directions, and gaps in the literature, which can inform future research directions. The resilient residential retrofit publications from 2012 to 2023 were retrieved and analyzed using text-mining software. In all, 4011 publications and 2623 patents were identified. The analysis revealed an average annual publication growth rate of 11%, indicating increasing interest in resilient residential retrofits. Four central topics were explored specifically throughout the study, as they are known to be the most prevalent climate risks for residential buildings: Overheating, Flooding, Wind, and Wildfires. The research trends analysis reveals that emerging interests in resilient residential retrofit encompass nature-based solutions, energy efficiency, thermal comfort, microclimates, durability, post-disaster recovery, and extreme events. Nearly half of the publications reference urban context and over one-third mention costs. The building envelope is the most frequently discussed housing component. Although energy retrofit was not the primary focus of this study and was not specifically searched for, energy concerns were still prevalent in the dataset, highlighting the critical importance of energy efficiency and management in resilient residential retrofits. The analysis of R&D momentum revealed several research gaps. Despite high growth rates, there are low publication rates on key topics such as durability, holistic approaches, microclimates, nature-based solutions, and traditional homes, to name a few. These areas could benefit from further research in the context of climate-resilient residential retrofits. Additionally, the analysis indicates a lack of publications on cross-themed research specific to rural and suburban settings. There are also few studies addressing combinations of themes, such as overheating in high-rise buildings, wildfires in Nordic climates, and flooding risk in smart homes within the scope of resilient residential retrofits. The United States leads in publication output, followed by China and the UK, with China dominating the patent landscape. This scientometric analysis provides a comprehensive overview of the research landscape in resilient residential retrofit, systematically maps and analyzes the vast amount of research output, and identifies the key trends and gaps, enabling us to see a type of quantitative snapshot of the research in a field at a certain point in time and thus providing a unique point of view. This study helps stakeholders prioritize efforts and resources effectively for guiding future research, funding decisions, informing policy decisions, and ultimately enhancing the resilience of residential buildings to climate-related challenges. Full article

Prefabricated construction (PC) plays a critical role in advancing the sustainable and high-quality transformation of the construction industry. Nevertheless, the fragmented and variable nature of technological innovations in PC complicates their acquisition, comprehension, and practical application, thereby hindering the process of innovation transformation. In response to these challenges, this study applies knowledge graph techniques to aggregate, correlate, and store knowledge pertaining to PC technological innovations. Specifically, using patent data from the past five years, and grounded in knowledge management and complex network theories, this study employs text mining, topic modeling, and association rule algorithms to perform clustering, evolutionary, and association analyses. The extracted entities and relationships obtained from the analyses are then stored in a Neo4j graph database for the construction and interactive visualization of a knowledge graph for PC technological innovation. According to the knowledge graph, a question-and-answer system framework is further proposed, providing practical application guidance. This research provides a comprehensive overview of the technological landscape, key nodes, and development trends in PC. It makes a meaningful contribution to knowledge management theory and complex network theory, advancing innovative applications in PC technology. Full article

In the dynamic field of robotics engineering, nanorobot technology has witnessed rapid advancements. Developing a technology roadmap is essential for quickly identifying the trends and key technological aspects of nanorobotics from an array of multi-source data. Traditional research methods, such as Delphi surveys, bibliometrics, patent analysis, and patent paper citation analyses, often fail to capture the rich semantic information available. Moreover, these approaches generally provide a unidimensional perspective, which restricts their capacity to depict the complex nature of technological evolution. To overcome these shortcomings, this paper introduces a novel framework that utilizes the ALBERT method combined with multi-source data for critical theme extraction. It integrates varied data sources, including academic papers and patents, to explore the interrelation within the nanorobot technology roadmap. The methodology begins with text feature extraction, clustering algorithms, and theme mining to identify dominant technological themes. Subsequently, it applies semantic similarity measures to connect multiple themes, employing a “multi-layer ThemeRiver map” for a visual representation of these inter-layer connections. The paper concludes with a comprehensive analysis from both the technological research and industrial application perspectives, underscoring the principal developmental themes and insights of nanorobot technology, and projecting its future directions. Full article

Patents contain detailed information of researched and developed technologies. We analyzed patent documents to understand the technology in a given domain. For the patent data analysis, we extracted the keywords from the patent documents using text mining techniques. Next, we built a patent document–keyword matrix using the patent keywords and analyzed the matrix data using statistical methods. Each element of the matrix represents the frequency of a keyword that occurs in a patent document. In general, most of the elements were zero because the keyword becomes a column of the matrix even if it occurs in only one document. Due to this zero-inflated problem, we experienced difficulty in analyzing patent keywords using existing statistical methods such as linear regression analysis. The purpose of this paper is to build a statistical model to solve the zero-inflated problem. In this paper, we propose a regression model based on quantile cumulative distribution function to solve this problem that occurs in patent keyword analysis. We perform experiments to show the performance of our proposed method using patent documents related to blockchain technology. We compare regression modeling based on a quantile cumulative distribution function with convenient models such as linear regression modeling. We expect that this paper will contribute to overcoming the zero-inflated problem in patent keyword analysis performed in various technology fields. Full article

Patent keyword analysis is used to analyze the technology keywords extracted from collected patent documents for specific technological fields. Thus, various methods related to this type of analysis have been researched in the industrial engineering fields, such as technology management and new product development. To analyze the patent document data, we have to search for patents related to the target technology and preprocess them to construct the patent–keyword matrix for statistical and machine learning algorithms. In general, a patent–keyword matrix has an extreme zero-inflated problem. This is because each keyword occupies one column even if it is included in only one document among all patent documents. General zero-inflated models have a limit at which the performance of the model deteriorates when the proportion of zeros becomes extremely large. To solve this problem, we applied a Bayesian inference to a general zero-inflated model. In this paper, we propose a patent keyword analysis using a Bayesian zero-inflated model to overcome the extreme zero-inflated problem in the patent–keyword matrix. In our experiments, we collected practical patents related to digital therapeutics technology and used the patent–keyword matrix preprocessed from them. We compared the performance of our proposed method with other comparative methods. Finally, we showed the validity and improved performance of our patent keyword analysis. We expect that our research can contribute to solving the extreme zero-inflated problem that occurs not only in patent keyword analysis, but also in various text big data analyses. Full article

Ontological knowledge graph (OKG) is a well-formed visual representation that depicts knowledge organization in formal elements (e.g., entities and attributes) and their interrelationships. OKG is crucial for innovation management analysis as it provides a clear boundary to understand complex knowledge domain in detail. In the patent analysis field, it facilitates the definition of a well-defined patent portfolio, aiming for accurate and complete patent retrievals and subsequent analyses. In recent decade, the rapid growth of the information and communication technology (ICT) sector has rendered data centers (DCs) indispensable for data processing, storage, and cloud computing, while ensuring security and privacy during DC operations. However, their energy-intensive operations pose challenges to global efforts toward achieving net-zero emissions goals. In response, this research develops a formal OKG refinement process and uses DC net-zero technology OKG as case study for in-depth OKG refinement and application in patent portfolio analysis. The net-zero DC domain covers five sub-technologies. Utilizing the proposed OKG refinement and patent portfolio analysis framework, the 1801 most recent decade’s patents related to relevant “DC net-zero technologies” are retrieved and analyzed. Particularly in this case, DC colocation and server-as-a-service perspectives are the newly discovered sub-domains for OKG refinement. Furthermore, the research also adopts the technology function matrix and technology maturity to assess current and future technology development trends, providing crucial insights supporting strategic innovation management. Full article

Accurate identification and evolutionary analysis of core technology topics within patent texts play a crucial role in enabling enterprises to discern the development trajectory of core technologies, optimize research and development (R&D) strategies, and foster technological innovation. Based on the perspective of time series dynamic analysis, this study uses the Latent Dirichlet Allocation (LDA) topic modeling and TF-IDF text vectorization methods to comprehensively mine and identify patent technology topics in the field of unmanned ships. This study deeply analyzes the dynamic evolution of unmanned ship technology topics from two aspects: the evolution of technology theme intensity and the evolution of technology theme content. We refine the development characteristics and future development directions of unmanned ship technology. The findings reveal two hot technologies, six growth technologies, and six declining technologies in unmanned ship technology. Furthermore, the analysis of technical topic evolution illustrates a pattern of fragmentation, inheritance, and integration. This study advances the methodologies used for identifying and analyzing patent technology topics and helps to grasp the development rules and evolutionary trends of core technologies. In addition, this paper has reference value for the research and practice of core technology topic identification and evolution analysis methods based on patent text mining. Full article

As the severity of climate change intensifies, understanding and predicting weather patterns have become paramount. Major firms worldwide have recognized this urgency, focusing their innovative efforts on weather prediction. In line with this trend, this research delves into the intricate patterns of patent data within the realm of weather prediction from 2010 to 2023. The study unveils a standard timeline for patent grants in this domain, particularly noting a distinctive peak in grant durations between 1500 and 2000 days. The global landscape of weather prediction innovation is highlighted, pinpointing the United States, China, and Japan as pivotal contributors. A salient finding is the ascendant influence of artificial intelligence (AI) in this sector, underscored by the prevalence of AI-centric keywords such as “machine learning” and “neural network”. This trend exemplifies the ongoing paradigm shift toward data-driven methodologies in weather forecasting. A notable correlation was identified between patent trends and academic trends on platforms such as arXiv, especially concerning keywords such as “machine learning” and “deep learning”. Moreover, our findings indicate that the transformer network, given its rising prominence in deep learning realms, is predicted to be a future keyword trend in weather prediction patents. However, despite its insights, the study also grapples with limitations in its predictive modeling component, which aims at forecasting patent grant durations. Overall, this research offers a comprehensive understanding of the patent dynamics in weather prediction, illuminating the trajectory of technological advancements and the burgeoning role of AI. It holds implications for academia, industry, and policymaking in navigating the future of weather prediction technologies. Full article

The gradual adoption of electric vehicles (EVs) globally serves as a crucial move toward addressing global decarbonization goals for sustainable development. However, the lack of cost-effective, power-efficient, and safe chargers for EV batteries hampers adoption. Understanding the research needs and identifying the gaps in EV charger innovation informs investments and research to address development challenges. This study developed a unique text mining workflow to classify themes in EV charger technology and product development by analyzing U.S. patent award summaries. The text mining workflow combined the techniques of data extraction, data cleaning, natural language processing (NLP), statistical analysis, and unsupervised machine learning (ML) to extract unique themes and to visualize their relationships. There was a 47.7% increase in the number of EV charger patents issued in 2022 relative to that in 2018. The top four themes were charging station management, power transfer efficiency, on-board charger design, and temperature management. More than half (53.8%) of the EV charger patents issued over the five-year period from 2018 to 2022 addressed problems within those four themes. Patents that addressed wireless charging, fast charging, and fleet charging accounted for less than 10% each of the EV charger patents issued. This suggests that the industry is still at the frontier of addressing those problems. This study further presents examples of the specific EV charger problems addressed within each theme. The findings can inform investment decisions and policymaking to focus on R&D resources that will advance the state of the art and spur EV adoption. Full article

The semi-automatic construction and analysis of technology roadmaps are at the forefront of applying artificial intelligence techniques. To clarify the development path of the core technologies in the field of agricultural machinery, we propose a core technology evolution path analysis method based on patent text semantic mining. First, the key sentences in the text were extracted, and the BERT model was used to represent the topic’s key sentences in semantic vectorization. Then, the technology roadmap was constructed through the unsupervised LDA topic clustering method, and the main fields of activity, blank fields, and fields of agricultural machinery were visually analyzed. Next, investment in research fields was strengthened. Finally, we mapped the technology roadmap and patent IPC codes and found that the evolution of core technologies in the field of agricultural machinery could be divided into the technology development stage, technology focus stage, and technology transformation stage; this allows us to analyze the evolution and integration of these core technologies. The internal laws of the technology evolution provide a reference for future research plans of governments, enterprises, and institutions, aiding in the patent portfolio planning by revealing the microlevel process of technology integration and the technological trends in agricultural machinery. Full article