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The supply chain forms the backbone of the modern consumer economy, weaving an intricate network of stakeholders across geographical and socioeconomic divides. While new technologies have enhanced supply chain management, the market dynamism and network complexities continue to challenge decision-makers. This study employs social network analysis and text mining to unravel technological patterns within the patent landscape of supply chain management. The analysis draws on a dataset of over 32,000 supply chain patents from Lens.org spanning 2000–2022. Network analysis reveals cooperation patterns and key players, while text mining and clustering identify five technology clusters: secure access control, manufacturing, logistics, data management, and RFID. Technology life cycle analysis indicates that secure access control, data management, and RFID have reached maturity, while logistics is still growing and manufacturing faces saturation. The findings highlight that despite maturity, these technologies warrant continued investment to resolve persistent challenges. The technology trends and maturity insights uncovered can help enterprises make informed strategic decisions by aligning R&D initiatives with technology lifecycles. This pioneering study bridges innovation research and technology management, offering a nuanced understanding of supply chain technologies. The framework presented can be extended to analyze other domains, opening avenues for further research. Overall, this study decodes the patent landscape to decode the future. [ABSTRACT FROM AUTHOR]

Loop-Mediated Isothermal Amplification (LAMP) technology is emerging as a rapid pathogen testing method, potentially challenging the RT-PCR "gold standard". Despite recent advancements, LAMP's widespread adoption remains limited. This study provides a comprehensive market overview and assesses future growth prospects to aid stakeholders in strategic decision-making and policy formulation. Using a dataset of 1134 LAMP patent documents, we analyzed lifecycle and geographic distribution, applicant profiles, CPC code classifications, and patent claims. Additionally, we examined clinical developments from 21 curated clinical trials, focusing on trends, geographic engagement, sponsor types, and the conditions and pathogens investigated. Our analysis highlights LAMP's potential as a promising rapid pathogen testing alternative, especially in resource-limited areas. It also reveals a gap between clinical research, which targets bacterial and parasitic diseases like malaria, leishmaniasis, and tuberculosis, and basic research and commercial efforts that prioritize viral diseases such as SARS-CoV-2 and influenza. European stakeholders emphasize the societal impact of addressing unmet needs in resource-limited areas, while American and Asian organizations focus more on research, innovation, and commercialization. [ABSTRACT FROM AUTHOR]

The sharing economy affords new opportunities to the global tourism industry, accelerating the development of tourism at a striking rate. This paper examines productivity of the bed and breakfast (B&B) industry and how to improve the performance of the B&B industry in a sharing economy. A modified global Malmquist index using slack-based measures is developed to measure productivity, and benchmark selection models using technology forecasting are proposed to improve future performance. The future technology is forecasted by averaging the frontier shifts (technology changes) in previous periods. This study challenges the implicit assumption that the technology will be stationary in traditional benchmarking, and suggests that how the information of frontier shifts derived from Malmquist index can be used to forecast future technology in a rational manner. The empirical results inform that there is a productivity growth in the B&B industry in a sharing economy, and the main driver of the growth is technology progress rather than performance improvement. In particular, a counterintuitive but interesting result is that the B&B industry experienced a slight productivity growth after the outbreak of COVID-19. The benchmarks in 2021 and 2022 are forecasted to improve the performance of the B&B industry. [ABSTRACT FROM AUTHOR]

In the industrial era, production equipment serves as an essential mother machine. In the global manufacturing industry, components such as laptop computers, mobile phones, and automotive parts all strive for aesthetic appearance. Taiwan's machine tool industry plays a significant role globally. Faced with the constantly changing market environment, the development and competitive advantage of CNC machines are crucial topics for manufacturers. Domestic manufacturers of computer numerical control machines should move towards the integration of automated equipment to accommodate various advanced parts processing procedures. Smart manufacturing will become the trend of the industry in the future. This study invited experts from academia, industry, and research institutions to conduct expert interviews. Their opinions were compiled and analyzed, supplemented by fuzzy Delphi analysis to establish the development trends of various modules. The feasibility and demand of the product's functional technology for industrial development were analyzed under three research dimensions and eight technical items. A total of 26 key sub-technical items were identified, achieving an expert consensus level of over 80. Furthermore, the importance ranking was analyzed using the fuzzy analytic hierarchy process, and the consistency tests were passed with C.I. < 0.1 and C.R. < 0.1. Finally, the obtained importance ranking of the hierarchical structure was used to predict the future development of computer numerical control machines through a technology roadmap, helping manufacturers use it as a reference model for future development trends to enhance market competitiveness. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] Accurately measuring the influence of technical topics is crucial for decision-makers to understand the developmental trends in the technology sector. It is also an important link in identifying emerging, cutting-edge, and disruptive technical topics. Traditional methods of measuring technical topic influence are significantly affected by the latency of patent data approval and citations, lack a forward-looking perspective on the potential influence of technical topics, and suffer from insufficient semantic richness in the extraction of technical topics. This paper presents a method for measuring technical topic influence based on PhraseLDA-SNA and machine learning. It aims to mitigate the impact of delays in patent data approval and citation, while improving the interpretability and accuracy of the results in assessing technical topic influence. [Method/Process] In this study the explicit and implicit determinants of technical topic influence were first analyzed, based on which an index system for measuring technical topic influence was constructed. Then, the PhraseLDA model was used to extract semantically rich technical topics from a large corpus of pre-processed patent texts and to compute the topic-patent association probabilities. PhraseLDA-SNA enhances the semantic richness of technical topic extraction and deepens the analysis of topic content. Machine learning methods leverage their robust data processing and analysis capabilities to predict the high citation potential of patents related to the topics. This research integrates PhraseLDA-SNA and machine learning methods to accurately measure the significance and advanced nature of technical topics in promoting field development, thereby achieving an accurate measurement of the influence of technical topics. Finally, an empirical study was conducted in the field of cellulose biodegradation to compare the high-impact technical topics identified by the proposed method with those identified by the traditional method. Several experts with high academic influence and extensive experience in cellulose biodegradation research were invited to evaluate the high-impact technical topics identified in this study, thus validating the effectiveness of the proposed method. [Results/Conclusions] Compared with the traditional method, the technical topic influence measurement approach based on PhraseLDA-SNA and machine learning reveals more in-depth content. Moreover, this method also analyzes the importance and leading nature of technical topics, which shows superiority in quantitative analysis. Comparing the distribution of high-impact technical topic-related patents identified by the two methods across different years, the topics identified by the proposed method had a higher association ratio in the most recent data, indicating a significant reduction in the impact of patent data approval and citation delays.

[Purpose/Significance] Accurately measuring the influence of technical topics is crucial for decision-makers to understand the developmental trends in the technology sector. It is also an important link in identifying emerging, cutting-edge, and disruptive technical topics. Traditional methods of measuring technical topic influence are significantly affected by the latency of patent data approval and citations, lack a forward-looking perspective on the potential influence of technical topics, and suffer from insufficient semantic richness in the extraction of technical topics. This paper presents a method for measuring technical topic influence based on PhraseLDA-SNA and machine learning. It aims to mitigate the impact of delays in patent data approval and citation, while improving the interpretability and accuracy of the results in assessing technical topic influence. [Method/Process] In this study the explicit and implicit determinants of technical topic influence were first analyzed, based on which an index system for measuring technical topic influence was constructed. Then, the PhraseLDA model was used to extract semantically rich technical topics from a large corpus of pre-processed patent texts and to compute the topic-patent association probabilities. PhraseLDA-SNA enhances the semantic richness of technical topic extraction and deepens the analysis of topic content. Machine learning methods leverage their robust data processing and analysis capabilities to predict the high citation potential of patents related to the topics. This research integrates PhraseLDA-SNA and machine learning methods to accurately measure the significance and advanced nature of technical topics in promoting field development, thereby achieving an accurate measurement of the influence of technical topics. Finally, an empirical study was conducted in the field of cellulose biodegradation to compare the high-impact technical topics identified by the proposed method with those identified by the traditional method. Several experts with high academic influence and extensive experience in cellulose biodegradation research were invited to evaluate the high-impact technical topics identified in this study, thus validating the effectiveness of the proposed method. [Results/Conclusions] Compared with the traditional method, the technical topic influence measurement approach based on PhraseLDA-SNA and machine learning reveals more in-depth content. Moreover, this method also analyzes the importance and leading nature of technical topics, which shows superiority in quantitative analysis. Comparing the distribution of high-impact technical topic-related patents identified by the two methods across different years, the topics identified by the proposed method had a higher association ratio in the most recent data, indicating a significant reduction in the impact of patent data approval and citation delays. [ABSTRACT FROM AUTHOR]

The automotive market is currently shifting away from traditional vehicles reliant on internal combustion engines, favoring battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs). The widespread acceptance of these vehicles, especially without government subsidies, hinges on market dynamics, particularly customers opting for vehicles with the lowest overall cost of ownership. This paper aims to model the total cost of ownership for various powertrains, encompassing conventional vehicles, HEVs, PHEVs, and BEVs, focusing on both sedans and sports utility vehicles. The modeling uses vehicle dynamics to approximate the fuel and electricity consumption rates for each powertrain. Following this, the analysis estimates the purchase cost and the lifetime operational cost for each vehicle type, factoring in average daily mileage. As drivers consider vehicle replacements, their choice tends to lean towards the most economical option, especially when performance metrics (e.g., range, acceleration, and payload) are comparable across the choices. The analysis seeks to determine the percentage of drivers likely to choose each vehicle type based on their specific driving habits. Advances in battery technology will reduce the battery weight and cost; further, the cost of electricity will decrease as more renewable energy sources will be integrated into the grid. In turn, the total cost of ownership will decrease for the electrified vehicles. By following battery trends, this study is able to model the makeup of the automotive market over time as it transitions from fossil-fuel based vehicles to fully electric vehicles. The model finds until the cost of batteries and electricity is significantly reduced, the composition of the vehicle market is a mixture of all vehicle types. [ABSTRACT FROM AUTHOR]

The automotive market is currently shifting away from traditional vehicles reliant on internal combustion engines, favoring battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs). The widespread acceptance of these vehicles, especially without government subsidies, hinges on market dynamics, particularly customers opting for vehicles with the lowest overall cost of ownership. This paper aims to model the total cost of ownership for various powertrains, encompassing conventional vehicles, HEVs, PHEVs, and BEVs, focusing on both sedans and sports utility vehicles. The modeling uses vehicle dynamics to approximate the fuel and electricity consumption rates for each powertrain. Following this, the analysis estimates the purchase cost and the lifetime operational cost for each vehicle type, factoring in average daily mileage. As drivers consider vehicle replacements, their choice tends to lean towards the most economical option, especially when performance metrics (e.g., range, acceleration, and payload) are comparable across the choices. The analysis seeks to determine the percentage of drivers likely to choose each vehicle type based on their specific driving habits. Advances in battery technology will reduce the battery weight and cost; further, the cost of electricity will decrease as more renewable energy sources will be integrated into the grid. In turn, the total cost of ownership will decrease for the electrified vehicles. By following battery trends, this study is able to model the makeup of the automotive market over time as it transitions from fossil-fuel based vehicles to fully electric vehicles. The model finds until the cost of batteries and electricity is significantly reduced, the composition of the vehicle market is a mixture of all vehicle types.

As a versatile energy carrier, hydrogen possesses tremendous potential to reduce greenhouse emissions and promote energy transition. Global interest in producing hydrogen from renewable energy sources and transporting, storing, and utilizing hydrogen is rising rapidly. However, the high costs of producing clean hydrogen and the uncertain application scenarios for hydrogen energy result in its relatively limited utilization worldwide. It is necessary to find new promising technological paths to drive the development of hydrogen energy. As part of technological innovation, emerging technologies have vital features such as prominent impact, novelty, relatively fast growth, etc. Identifying emerging hydrogen-energy-related technologies is important for discovering innovation opportunities during the energy transition. Existing research lacks analysis of the characteristics of emerging technologies. Thus, this paper proposes a method combining the latent Dirichlet allocation topic model and hydrogen-energy expert group decision-making. This is used to identify emerging hydrogen-related technology regarding two features of emerging technologies, novelty and prominent impact. After data processing, topic modeling, and analysis, the patent dataset was divided into twenty topics. Six emerging topics possess novelty and prominent impact among twenty topics. The results show that the current hotspots aim to promote the application of hydrogen energy by improving the performance of production catalysts, overcoming the wide power fluctuations and large-scale instability of renewable energy power generation, and developing advanced hydrogen safety technologies. This method efficiently identifies emerging technologies from patents and studies their development trends. It fills a gap in the research on emerging technologies in hydrogen-related energy. Research achievements could support the selection of technology pathways during the low-carbon energy transition. [ABSTRACT FROM AUTHOR]

Loop-Mediated Isothermal Amplification (LAMP) technology is emerging as a rapid pathogen testing method, potentially challenging the RT-PCR “gold standard”. Despite recent advancements, LAMP’s widespread adoption remains limited. This study provides a comprehensive market overview and assesses future growth prospects to aid stakeholders in strategic decision-making and policy formulation. Using a dataset of 1134 LAMP patent documents, we analyzed lifecycle and geographic distribution, applicant profiles, CPC code classifications, and patent claims. Additionally, we examined clinical developments from 21 curated clinical trials, focusing on trends, geographic engagement, sponsor types, and the conditions and pathogens investigated. Our analysis highlights LAMP’s potential as a promising rapid pathogen testing alternative, especially in resource-limited areas. It also reveals a gap between clinical research, which targets bacterial and parasitic diseases like malaria, leishmaniasis, and tuberculosis, and basic research and commercial efforts that prioritize viral diseases such as SARS-CoV-2 and influenza. European stakeholders emphasize the societal impact of addressing unmet needs in resource-limited areas, while American and Asian organizations focus more on research, innovation, and commercialization.

In the industrial era, production equipment serves as an essential mother machine. In the global manufacturing industry, components such as laptop computers, mobile phones, and automotive parts all strive for aesthetic appearance. Taiwan’s machine tool industry plays a significant role globally. Faced with the constantly changing market environment, the development and competitive advantage of CNC machines are crucial topics for manufacturers. Domestic manufacturers of computer numerical control machines should move towards the integration of automated equipment to accommodate various advanced parts processing procedures. Smart manufacturing will become the trend of the industry in the future. This study invited experts from academia, industry, and research institutions to conduct expert interviews. Their opinions were compiled and analyzed, supplemented by fuzzy Delphi analysis to establish the development trends of various modules. The feasibility and demand of the product’s functional technology for industrial development were analyzed under three research dimensions and eight technical items. A total of 26 key sub-technical items were identified, achieving an expert consensus level of over 80. Furthermore, the importance ranking was analyzed using the fuzzy analytic hierarchy process, and the consistency tests were passed with C.I. < 0.1 and C.R. < 0.1. Finally, the obtained importance ranking of the hierarchical structure was used to predict the future development of computer numerical control machines through a technology roadmap, helping manufacturers use it as a reference model for future development trends to enhance market competitiveness.

The primary purpose of the present study is to determine the life cycle and technology prediction based on patent bibliometric data using Markov hidden model. The study population included 50,915 patent licenses in medical equipment extracted from the US Patent and Trademark Office database from 1976 to 2015. The study findings revealed technology life cycle patterns in 21 medical equipment sub-areas. The status of medical device patent license indicators at different stages of the technology life cycle, alongside the likelihood of state transfer at different stages of the medical technology life cycle, were investigated. Ultimately, the results showed that "Drug delivery equipment, Disposable medical equipment, Oximeters, and Sharps (Medical instruments)" have more suitable areas for investment and commercialization. The current research results can also provide a good insight into technologies and can be used as a guide alongside experts and other decision-making methods.

Brewers’ spent grain (BSG) is an important waste produced by beer companies and has a high potential to be transformed into commercial by-products. The present paper reports a forecasting technology study with the aid of bibliographic review and patent analysis tools aiming to investigate the maturity of certain technologies, considering the use of BSG on a closed-loop biorefinery and circular bioeconomy concept integrated into a brewery. To evaluate the possibilities of production of high-added-value products in Brazil, we present an overview of BSG’s scientific, technological, and marketing products and applications. The comparison between the numbers of articles versus patents shows that the solutions proposed by research articles are not being transformed into maturated viable technologies. The results suggest that there is a gap between the scientific research in the institutions and their applications in the industry, which lead to the destination of BSG for more economically attractive investments when compared with research in Brazil. Ultimately, from the carried-out analysis, it is possible to propose a brewing process connected to the biorefinery system, showing its possibility in the newly arising brewery industries.

The transportation industry’s transition to carbon neutrality is essential for addressing sustainability concerns. This study details a model for calculating the carbon footprint of the transportation sector as it progresses towards carbon neutrality. The model aims to support policymakers in estimating the potential impact of various decisions regarding transportation technology and infrastructure. It accounts for energy demand, technological advancements, and infrastructure upgrades as they relate to each transportation market: passenger vehicles, commercial vehicles, aircraft, watercraft, and trains. A technology roadmap underlies this model, outlining anticipated advancements in batteries, hydrogen storage, biofuels, renewable grid electricity, and carbon capture and sequestration. By estimating the demand and the technologies that comprise each transportation market, the model estimates carbon emissions. Results indicate that based on the technology roadmap, carbon neutrality can be achieved by 2070 for the transportation sector. Furthermore, the model found that carbon neutrality can still be achieved with slippage in the technology development schedule; however, delays in infrastructure updates will delay carbon neutrality, while resulting in a substantial increase in the cumulative carbon footprint of the transportation sector.

Home automation technologies are a vital part of humanity, as they provide convenience in otherwise mundane and repetitive tasks. In recent years, given the development of the Internet of Things (IoT) and artificial intelligence (AI) sectors, these technologies have seen a tremendous rise, both in the methodologies utilized and in their industrial impact. Hence, many organizations and companies are securing commercial rights by patenting such technologies. In this study, we employ an analysis of 8482 home automation patents from the United States Patent and Trademark Office (USPTO) to extract thematic clusters and distinguish those that drive the market and those that have declined over the course of time. Moreover, we identify prevalent competitors per cluster and analyze the results under the spectrum of their market impact and objectives. The key findings indicate that home automation networks encompass a variety of technological areas and organizations with diverse interests. [ABSTRACT FROM AUTHOR]

Brewers' spent grain (BSG) is an important waste produced by beer companies and has a high potential to be transformed into commercial by-products. The present paper reports a forecasting technology study with the aid of bibliographic review and patent analysis tools aiming to investigate the maturity of certain technologies, considering the use of BSG on a closed-loop biorefinery and circular bioeconomy concept integrated into a brewery. To evaluate the possibilities of production of high-added-value products in Brazil, we present an overview of BSG's scientific, technological, and marketing products and applications. The comparison between the numbers of articles versus patents shows that the solutions proposed by research articles are not being transformed into maturated viable technologies. The results suggest that there is a gap between the scientific research in the institutions and their applications in the industry, which lead to the destination of BSG for more economically attractive investments when compared with research in Brazil. Ultimately, from the carried-out analysis, it is possible to propose a brewing process connected to the biorefinery system, showing its possibility in the newly arising brewery industries. [ABSTRACT FROM AUTHOR]

In this article, an attempt has been made to analyze the global development of technologies related to MAVs in the next decade by using the basics of technology forecasting (monitoring and experts' views) and by using the fuzzy Delphi method. The current research is applied in terms of purpose, in terms of combined strategy, and in terms of implementation in the form of descriptive-survey research. The statistical population is in two levels. The first level is made up of experts in the field of the MAVs industry, and the second level is made up of university professors in the field of technologies related to MAVs. According to the purpose of the research, the targeted sampling number was 9 people in the first level and 35 people elected in the second level. The data collection tool is a researcher-made questionnaire, whose validity was determined by content method and reliability by Cronbach's alpha method. Data analysis was also done through the fuzzy Delphi method using Spss and Excel software.The findings of this research show that the technologies related to MAVs can be counted in 29 indicators and out of these 9 indicators are in the field of artificial intelligence and electronics and telecommunication technologies are in priority and should be taken into consideration by the country's officials, decision-makers, and planners.

The primary purpose of the present study is to determine the life cycle and technology prediction based on patent bibliometric data using Markov hidden model. The study population included 50,915 patent licenses in medical equipment extracted from the US Patent and Trademark Office database from 1976 to 2015. The study findings revealed technology life cycle patterns in 21 medical equipment sub-areas. The status of medical device patent license indicators at different stages of the technology life cycle, alongside the likelihood of state transfer at different stages of the medical technology life cycle, were investigated. Ultimately, the results showed that "Drug delivery equipment, Disposable medical equipment, Oximeters, and Sharps (Medical instruments)" have more suitable areas for investment and commercialization. The current research results can also provide a good insight into technologies and can be used as a guide alongside experts and other decision-making methods. [ABSTRACT FROM AUTHOR]

The transportation industry's transition to carbon neutrality is essential for addressing sustainability concerns. This study details a model for calculating the carbon footprint of the transportation sector as it progresses towards carbon neutrality. The model aims to support policymakers in estimating the potential impact of various decisions regarding transportation technology and infrastructure. It accounts for energy demand, technological advancements, and infrastructure upgrades as they relate to each transportation market: passenger vehicles, commercial vehicles, aircraft, watercraft, and trains. A technology roadmap underlies this model, outlining anticipated advancements in batteries, hydrogen storage, biofuels, renewable grid electricity, and carbon capture and sequestration. By estimating the demand and the technologies that comprise each transportation market, the model estimates carbon emissions. Results indicate that based on the technology roadmap, carbon neutrality can be achieved by 2070 for the transportation sector. Furthermore, the model found that carbon neutrality can still be achieved with slippage in the technology development schedule; however, delays in infrastructure updates will delay carbon neutrality, while resulting in a substantial increase in the cumulative carbon footprint of the transportation sector. [ABSTRACT FROM AUTHOR]

Traditional forecasting methods usually predict industry trends and technologies in only a single direction, either upward or downward. Therefore, the forecasting results would often show surprising deviations, particularly for those industries that are complex and may have abrupt changes. Consequently, this study aims to fill this gap and develop a new approach by making industrial driving forces a concrete tool for forecasting technological changes and industry trends. Both qualitative and quantitative data were collected to examine the future direction of OLED (organic light-emitting diode) technology, which is regarded as complex with highly uncertain opportunities and market potential. The forecasted data were then compared with the actual industrial data. The results indicated that both showed the same cyclical pattern. These preliminary findings imply that the concept of driving forces can be utilized as a fore-casting tool when integrated with quantitative data analysis. [ABSTRACT FROM AUTHOR]

In the midst of dynamic industrial changes, companies need data analysis considering the effects of integration of various technologies in order to establish innovative R & D strategies. However, the existing technology forecasting model evaluates individual technologies without considering relationship among them. To improve this problem, this study suggests a new methodology reflecting the integration of technologies. In the study, a technology forecasting indicator was developed using the technology integration index based on social network analysis. In order to verify the validity of the proposed methodology, 'drone task performance technology' based on patent data was applied to the research model. This study aimed to establish a theoretical basis to design a research model that reflects the degree of integration of technologies when conducting technology forecasting research. In addition, this study is meaningful in that it quantitatively verified the proposed methodology using actual patent data. [ABSTRACT FROM AUTHOR]

The railway main transformer is considered one of the most important electrical equipment for trains. Companies and research institutes around the world are striving to develop high-performance railway main transformers. In order to be the first mover for railway main transformer technology, companies and research institutes should predict vacant technology based on the analysis of promising detailed technology areas. Therefore, in this study, a patent analysis to predict vacant technologies based on identified promising IPC technology areas is provided. In order to identify promising detailed IPC technology areas, the technology mapping analysis, the time series analysis, and the social network analysis are conducted based on the patent-IPC matrix, extracted from the data information of 707 patents from the patent database of Korea, China, Japan, United States, Canada, and Europe. Then, through the GTM analysis based on promising detailed IPC technology areas, one vacant technology node and three analysis target nodes surrounding the vacant technology node are obtained to predict vacant technologies. From the analysis, we predict the following three groups of vacant technologies: (1) blowerless technology, (2) oil-free technology, and (3) solid-state technology. This study provides insights on the technology trend in railway main transformers, as well as the analysis framework for the development of R&D strategies based on the patent data. [ABSTRACT FROM AUTHOR]

This article sets out to investigate the relationship between the knowledge spillovers and the potential for high-value inventions to emerge. We study knowledge spillovers through the lens of knowledge networks: a web of interrelated knowledge elements with structural patterns. Knowledge spillovers influence the productivity of a technology sector and hence their pattern should impact the pathways for knowledge appropriation in the sector. It is hypothesized that a robust knowledge network is associated with knowledge appropriation leading to valuable inventions. We test the hypothesis using patent citation network of 1821 patents from 78 inventions belonging to two different technological sectors. Based upon the findings, we suggest network robustness as a new metric for determining the technical value of inventions. [ABSTRACT FROM AUTHOR]

Identifying the development trends of emerging technologies with disruptive potential as early as possible is crucial for enterprise research and development (R&D) investment planning and government R&D strategic planning. Many researchers have used academic papers and patent data to identify trends in emerging technologies, although they rarely have made use of web news data focused on emerging technologies. Web news contains a large amount of social awareness data that represents the public's sense of and response to emerging technologies, and the social awareness data is of great significance for identifying the development trends of emerging technologies. Therefore, this article presented a research framework to identify the development trends of emerging technologies using patent analysis and web news data mining. In the research framework, first, we have used patent analysis to analyze the evolution topics of these emerging technologies, and have applied the topic model to study the changing patterns of publics’ topics of concern regarding emerging technologies contained in web news. Second, we have made a comparison analysis of the results of patent analysis and the public’ sense of and response to emerging technologies contained in web news. Then, we have applied an improved sentiment analysis to study the changing patterns of publics’ expectations for emerging technologies contained in web news. Finally, by studying the changes in publics’ topics of concern and publics’ expectations for emerging technologies over time, we have identified the development trends of emerging technologies. The perovskite solar cells technology is used as a case study to analyze the effectiveness and feasibility of the framework. This article provides a new research perspective to identify technology trends, and contribute to the technology forecasting methodology. [ABSTRACT FROM AUTHOR]

With advances in science and technology and changes in industry, research on promising future technologies has emerged as important. Furthermore, with the advent of a ubiquitous and smart environment, governments and enterprises are required to predict future promising technologies on which new important core technologies will be developed. Therefore, this study aimed to establish science and technology development strategies and support business activities by predicting future promising technologies using big data and deep learning models. The names of the "TOP 10 Emerging Technologies" from 2018 to 2021 selected by the World Economic Forum were used as keywords. Next, patents collected from the United States Patent and Trademark Office and the Science Citation Index (SCI) papers collected from the Web of Science database were analyzed using a time-series forecast. For each technology, the number of patents and SCI papers in 2022, 2023 and 2024 were predicted using the long short-term memory model with the number of patents and SCI papers from 1980 to 2021 as input data. Promising technologies are determined based on the predicted number of patents and SCI papers for the next three years. Keywords characterizing future promising technologies are extracted by analyzing abstracts of patent data collected for each technology and the term frequency-inverse document frequency is measured for each patent abstract. The research results can help business managers make optimal decisions in the present situation and provide researchers with an understanding of the direction of technology development. [ABSTRACT FROM AUTHOR]

Companies facing transformation in the automotive industry will need to adapt to new trends, technologies and functions, in order to remain competitive. The challenge is to anticipate such trends and to forecast their development over time. The aim of this paper is to develop a methodology that allows us to analyze the temporal development of technologies, taking automated driving as an example. The framework consists of a technological and a functional roadmap. The technology roadmap provides information on the temporal development of 59 technologies based on expert elicitation using a multi-stage Delphi survey and patent analyses. The functional roadmap is derived from a meta-analysis of studies including 209 predictions of the maturity of automated driving functions. The technological and functional roadmaps are merged into a consolidated roadmap, linking the temporal development of technologies and functions. Based on the publication analysis, SAE level 5 is predicted to be market-ready by 2030. Contrasted to the results from the Delphi survey in the technological roadmap, 2030 seems to be too optimistic, however, as some key technologies would not have reached market readiness by this time. As with all forecasts, the proposed framework is not able to accurately predict the future. However, the combination of different forecast approaches enables users to have a more holistic view of future developments than with single forecasting methods.

To keep pace with the latest technological changes and advancements, predicting future technological trends and topics has become a critical approach for high-tech companies and policy-making institutions. In this paper, we proposed an explorative method that integrates link prediction and Node2Vec graph embedding to predict future technology topics using co-occurrence data from patent keywords. Specifically, this method collects and preprocesses patent datasets, constructs network graphs that depict relationships among different technology topics, and builds a supervised link prediction model based on the time series of the graph to identify future technology graphs. Furthermore, node2vec graph embedding is conducted to obtain node vector representations, and then the clustering algorithms can be improved to identify the relevant topics, which could be interpreted as future technology. A case study on blockchain is conducted to validate the feasibility and practicality of the method to demonstrate the application of the method. Through the comparison of machine learning methods, we selected the Random Forest (RF) model, which presents the highest accuracy, for our experiments. The results show that the proposed method can be used to effectively visualize potential future topics related to a specific technology. Compared to traditional methods such as Latent Dirichlet Allocation (LDA), our method can identify more unique and differentiated technological topics, significantly reducing topic overlap. Additionally, the reported method can illustrate the internal relationships of topics through subgraphs, helping readers better understand the core concepts of each topic and vividly displaying the structure and composition of the topics. Furthermore, the proposed method can also depict potential relationships between different technology topics, which can facilitate the visualization of new directions of research and development. • The link-prediction method for technology forecasting is enhanced or extended by node2vec graph embedding technique. • The method combining link-prediction with node2vec graph embedding shows some advantages than the traditional techniques. • A case study on blockchain based on the proposed method for technology forecasting is conducted. [ABSTRACT FROM AUTHOR]

Home automation technologies are a vital part of humanity, as they provide convenience in otherwise mundane and repetitive tasks. In recent years, given the development of the Internet of Things (IoT) and artificial intelligence (AI) sectors, these technologies have seen a tremendous rise, both in the methodologies utilized and in their industrial impact. Hence, many organizations and companies are securing commercial rights by patenting such technologies. In this study, we employ an analysis of 8482 home automation patents from the United States Patent and Trademark Office (USPTO) to extract thematic clusters and distinguish those that drive the market and those that have declined over the course of time. Moreover, we identify prevalent competitors per cluster and analyze the results under the spectrum of their market impact and objectives. The key findings indicate that home automation networks encompass a variety of technological areas and organizations with diverse interests.

The book describes how innovations can produce unintended effects that may take years to appear. The author is a well-known scientist and policy analyst whose interdisciplinary research interests encompass energy, food, population, economic, historical, and public policy studies. The author offers a few words to distinguish between the notions of invention and innovation. "The meanings have a large overlap, but innovation is perhaps best understood as the process of introducing, adopting, and mastering new materials, products, processes, and ideas." The book presents detailed descriptions of inventions that fall into three categories: 1) ones that delivered the advertised performance, but with undesirable follow-on consequences; 2) ones that were expected to dominate their markets, but never did; and 3) ones we would like to see succeed, but have not yet achieved their objectives. A final chapter examines current optimistic predictions of what technology could achieve and how fast these advances are likely to happen. It ends with a discussion of what types of innovation are really most needed. [ABSTRACT FROM AUTHOR]

The electronic payments system is among recent service innovations that have transformed access to finance in developing and emerging economies. This paper analyses and forecasts the diffusions of four major electronic payment technologies in Nigeria, namely, ATM, PoS, Mobile and Web payments. Diffusion was measured by the volume of financial payments transacted on these technologies. First, we applied two growth models to evaluate the diffusion and to forecast the future trajectories of these payment technologies. Second, we applied the logistics substitution model to evaluate how these technologies compete in the Nigerian payments market. The findings show that like many technological innovations the diffusions of electronic payment technologies are characterised by growth, maturity, saturation, and decline. In addition, the mobile payment system was forecast to emerge as the most diffused payment technology given the volume of financial exchange that will occur on this platform. These findings have implications for innovators, regulators and service providers within the Nigerian payment ecosystems. The findings also have practical implication for achieving the cashless economy and the financial inclusion projects of the Nigerian state which rely on the successful diffusions of these technologies. [ABSTRACT FROM AUTHOR]

Identifying disruptive technologies has important value for the decision-making in technology layout and investment. The identification methods of disruptive technologies based on data mining have attracted much attention recently, but most of the existing studies use single data for the identification, that may cause bias. Therefore, this paper uses multi-source data which represent the "science-technology-industry-market" chain to identify disruptive technologies. In addition, this paper improves the two steps, generating candidate technology list and evaluating disruptive potential, in the general process of identifying disruptive technologies separately and develops two new methods. One method is to obtain the list of potential disruptive technologies from experts and then evaluate the technology disruptive potential by using a multi-dimensional index system. The case study of this method is carried out in life science field, and four types of data (papers, patents, data of start-ups and public opinion) are used to evaluate thepotential disruptive technologies. Another method is to generate the list of potential disruptive technologies by mining multi-source data and then evaluate the technology disruptive potential by experts. The case study of this method is carried out in energy technology filed and life science, and three types of data (papers, patents and projects) are used for mining to generate the candidate technologies list. The effectiveness of the two methods using multi-source data is verified by comparing the results with the list of technologies given by experts in advance. [ABSTRACT FROM AUTHOR]

In modern society, science and technology has become the core competitiveness of a company or a country. Discovering the development trajectory a technological domain is of great significance. In this article, we propose a framework to answer this question by analyzing the semantics of patent texts and the citation relationships among patents, because patents in a specific technological domain provide rich resources of technological development process. First, we collect the patent data of a specific domain from the database of United States Patent and Trademark Office. Second, we extract different topics from these patents’ texts by Latent Dirichlet Allocation. Third, an index for evaluating the correlation between two patents is defined according to the semantic similarities and citation relationship between them. Fourth, we found the global and local important patents through the global and local important index (LII). Finally, we discover some community of the network constructed by the important patents filtered by LII and statistical analysis methods to obtain some meaningful results. Furthermore, we take the patents in the technological domain of robotics as an example to examine the proposed method and reveal the technological development trends in this domain. [ABSTRACT FROM AUTHOR]

3-D printing is an additive manufacturing process, which enables products to be custom designed. Obtaining the evolution trend and identifying the potential R&D hotspots of 3-D printing technology are important strategic issues for both nations and enterprises. In this article, a combination method is proposed using the Viterbi algorithm to identify technological terms in patents, using the latent Dirichlet allocation model to capture potential technological topics in patents, and using the hidden Markov model to analyze the distribution and evolution pattern of the technological topics. Based on the results, technological topics and future R&D hotspots in 3-D printing are identified. Among the technological topics, extrusion devices and laser powder molding are gradually losing R&D; model acquisition, extrusion devices, rotary printing, and denture are the key topics of technological evolution that are difficult to overcome; laser focusing, print head, color printing, recyclable, feeding, control panel, and denture will become the focus of R&D in the near future. Finally, a comparison and experiments are given to demonstrate the plausibility of our method. [ABSTRACT FROM AUTHOR]

Companies facing transformation in the automotive industry will need to adapt to new trends, technologies and functions, in order to remain competitive. The challenge is to anticipate such trends and to forecast their development over time. The aim of this paper is to develop a methodology that allows us to analyze the temporal development of technologies, taking automated driving as an example. The framework consists of a technological and a functional roadmap. The technology roadmap provides information on the temporal development of 59 technologies based on expert elicitation using a multi-stage Delphi survey and patent analyses. The functional roadmap is derived from a meta-analysis of studies including 209 predictions of the maturity of automated driving functions. The technological and functional roadmaps are merged into a consolidated roadmap, linking the temporal development of technologies and functions. Based on the publication analysis, SAE level 5 is predicted to be market-ready by 2030. Contrasted to the results from the Delphi survey in the technological roadmap, 2030 seems to be too optimistic, however, as some key technologies would not have reached market readiness by this time. As with all forecasts, the proposed framework is not able to accurately predict the future. However, the combination of different forecast approaches enables users to have a more holistic view of future developments than with single forecasting methods. [ABSTRACT FROM AUTHOR]

While numerous studies have examined the importance of technology innovation in supporting the aging society, few have been specifically conducted on the emerging technologies that forecast for the elderly. Moreover, existing related research lacks consideration of public users' demands, and the forecasting results have not been linked with the actual technology supply in the market, which may easily lead to waste of resources and deviation of R&D focus. Given that, we present a novel method to forecast and evaluate emerging technologies based on technology supply and demand (S&D) matching. In the case study of China's gerontechnology, we first systematically excavate the elderly's demands and match them with the emerging gerontechnologies. Then, we analyse technology supply to filter out S&D oversaturated technologies and screen out S&D undersaturated results by the text semantic mining and similarity matching. Among the results, 27.68% do not have an effective supply yet, 33.04% have saturated or oversaturated. Besides, we use the TRIZ theory to filtrate key emerging areas and find the evolution path, and six significant evolutionary trends are extracted. This paper will help decision makers to accurately target the most promising emerging technologies. Highlights A framework to forecast emerging technology based on supply and demand match. Considering aging population in the technology forecasting and foresight. Monitoring emerging technology trends by using social user's demands analysis. 75 emerging technologies and 6 evolution trends of China's gerontechnology. Filter out 33.04% supply saturated and oversaturated of forecasted results. [ABSTRACT FROM AUTHOR]

Technology forecasting not only helps business managers to make the right decisions but also helps researchers to grasp the direction of technology development. Technology forecasting, which facilitates the identification of the development technologies with high potential, can be an effective tool to support the management and plan for the future research activities. For this purpose, this paper firstly constructs Multi-modal input based on deep learning (MIDL) text classification model to extract relevant SCI papers from Web of Science database from 1996 to 2019 for topic classification, and then apply the Ensemble Empirical Mode Decomposition (EEMD) and Long Short-Term Memory (LSTM) neural networks to build an EEMD-LSTM technology forecasting model to predict the future development of each research field. Besides, we verify the validity of the method by taking robotics as an example in this paper. The results show that the accuracy, recall and F1 of MIDL text classification model are 0.826, 0.822 and 0.824 respectively. As compared with the optimal results of other classification models, the accuracy, recall and F1 are improved by 4.1%, 3.5%, and 3.8% respectively. The mean MAPE of the EEMD-LSTM model is 7%, which is 11% lower than ARIMA, 8% lower than LSTM, and 10% lower than 2-layer-LSTM.

Introduction--Academics, developers, and companies focused on technological development seek to know what exists and what is still missing in this field. One of the ways they use is the review of bibliographic sources (state-of-the art). In this sense, a tool was developed that allows the current state to be identified semi-automatically. Objective--This article proposes a tool that extracts information from repositories hosted on GitHub. It analyzes the data using computational techniques and presents the results through visualizations that identify the field's technological evolution studied through the most used programming languages, central repositories, and organizations. Method--A model based on Mining Software Repositories (MSR) is used, which integrates an architecture based on microservices, using different programming languages, which allowed the construction of the VigHub tool. The model focuses on four aspects--Selection of a topic, extraction of the data source, analysis of information using computational techniques, and finally, the results are communicated through visualizations. Results--The VigHub tool was available online to carry out 3 case studies. The first in the academy, where technologies, programming languages, users, and companies interested in developing VLE's (Virtual Learning Environment) were identified from 2011 to 2021. The second and third were carried out by companies (industrial environment), which stated that using the VigHub tool supports data analysis and valuable results identification. Conclusions--A tool that allows identifying a part of the current state of technology could be a helpful tool for academics, developers, and companies, saving human resources, time, and possible repeated developments- --code reuse. The VigHub tool aims to support the construction of state-of-the-art. Its results are complementary to the traditional method. [ABSTRACT FROM AUTHOR]

Technology forecasting is helpful in deriving insights on utilisation of resources effectively. The analysis helps in decision making towards preserving the resources and exploring alternative means that meet the increasing demands of the society. Generative models that comprise of probabilistic forecasting of changes in technology and discriminative models such as logistic are popular towards such estimates. Global non-fossil fuel production data in particular the solar, nuclear and wind are considered and forecasting models are developed. The present work highlights the predominant exploration of the energy generation based on wind is expected to be prominent during next few years. The approach in general can be applied to similar studies. These results reflect the importance of such studies and associated information towards forecasting based decision-making pertaining to the technology trends. [ABSTRACT FROM AUTHOR]

By reviewing the 70 years of AI, this article summarizes and discusses the paradigm transformations from the age of AI before the year 2000 to the new age of AI from the year 2000 onward. It reviews the AI thinking and features of various AI generations and paradigms during these two ages of AI and their transformations. The paper further summarizes several AI Formulas from the AI vision, system, goal, task, and process perspectives. Several important areas are highlighted in developing AI Futures: shrinking the gaps between human, natural and social AI, and developing human-like/level AI, meta AI, reflective AI, metasynthetic AI, data-driven AI, beyond 'IID AI,' actionable AI, and sustainable AI. In the new age of AI, we encourage your deep thinking of AI futures. [ABSTRACT FROM AUTHOR]

The purpose of this study is to mine CAR-T patents and therapies under development, to design a landscape of the sector and to understand key therapy segments and their current trends. The study analyzed the entire market, consisting of 1624 patent families and 509 biologics under development, to depict an overview of the CAR-T therapies and their state of the art. Our results showed cutting-edge inventions, the major players, the dynamics of cooperation among institutions, the progress of the therapies’ generation over the years and future innovation pathways. CAR-T therapies are transforming the current scenario for cancer treatment, and this study reveals the picture of what we can likely expect ahead in order to assist scientists at the academy and industry to improve their research strategies.

The main objective of this special is to offer the interested audience an overview of the most recent advancements in information and communication technologies that can enable the "New Space" vision, turning it from a conceptual design philosophy to real system implementation. [ABSTRACT FROM AUTHOR]

With advances in science and technology and changes in industry, research on promising future technologies has emerged as important. Furthermore, with the advent of a ubiquitous and smart environment, governments and enterprises are required to predict future promising technologies on which new important core technologies will be developed. Therefore, this study aimed to establish science and technology development strategies and support business activities by predicting future promising technologies using big data and deep learning models. The names of the “TOP 10 Emerging Technologies” from 2018 to 2021 selected by the World Economic Forum were used as keywords. Next, patents collected from the United States Patent and Trademark Office and the Science Citation Index (SCI) papers collected from the Web of Science database were analyzed using a time-series forecast. For each technology, the number of patents and SCI papers in 2022, 2023 and 2024 were predicted using the long short-term memory model with the number of patents and SCI papers from 1980 to 2021 as input data. Promising technologies are determined based on the predicted number of patents and SCI papers for the next three years. Keywords characterizing future promising technologies are extracted by analyzing abstracts of patent data collected for each technology and the term frequency-inverse document frequency is measured for each patent abstract. The research results can help business managers make optimal decisions in the present situation and provide researchers with an understanding of the direction of technology development.