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[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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

With the rapid development of information technology and the steady growth of the agricultural and rural economy, agricultural information technology has attracted more and more attention, and the trend of capital and technology playing important roles in the agricultural field has gradually formed. In recent years, large Internet enterprises have begun to enter the agricultural industry, and smart agriculture is developing strongly. This paper firstly analyzed the status and technical application characteristics of large-scale Internet companies engaged in agriculture, then explained the reasons why the current technology and capital entered the agricultural field in large numbers, especially in the context of the world science and technology revolution and China's economic and social status, and analyzed the key areas and problems of the combination of technology, capital and agricultural industry; finally, analyzed the application boundary, application prospects of information technology in the agricultural field. In view of the digital development and application of new technology in agricultural and rural areas, Some policy suggestions were put forward: 1) strengthening policy guidance and support to prevent market speculation risks; 2) building a system and mechanism for the convergence and integration of Internet enterprises and agricultural industries; 3) focus on cutting-edge key technologies and strengthen efforts to promote scientific and technological innovation; 4) speeding up the dynamic follow-up of technology achievement transformation, strengthening supervision and doing a good job in leading and demonstration drive. The key priority is to focus on the world's cutting-edge technology and key application technology, strengthen the dominant position of technological innovation of enterprises, and combine with the specific practice of production, circulation and consumption of China's agricultural industry to fully promote the innovation and application of China's agricultural information technology. And the main research contents included summarizing the successful examples carefully, doing a good job in publicity and guidance, and promoting the typical leads vigorously so that they can be copied, popularized and applied; for the failure cases, learn from the insufficient lessons to prevent the recurrence of similar cases; for the advanced practical technology formed by Internet enterprises, promote technology sharing and information sharing on the premise of protecting intellectual property rights and turn it into a new driving force for the development of agricultural modernization. Only by applying the latest achievement of modern information technology to the practice of agricultural production and becoming the representative of agricultural productivity, can we truly contribute to the development of modern agriculture and rural areas in China and the wing of information.

This article presents tools used in the Theory of Inventive Problem Solving (TRIZ) which are useful when assessing the evolution direction of technical systems. The following matters are discussed: the S-shaped curve, laws (trends and lines) of the evolution of technical systems, multi-screen diagrams, as well as analysis of evolutionary potential. Inventive laws formulated by Gienrich Altshuller as well as laws previously formulated by a Polish writer and promoter of knowledge, Aleksander Głowacki, writing under the pen name Bolesław Prus, have been presented. Finally the innovation roadmaps have been shown. The use of individual tools has been supported by practical examples taken from research performed by the authors, and the usefulness of individual methods was evaluated. All methods have been compared and evaluated.

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.

Information technologies (ITs) have been playing an important role in improving our society, and the fast evolution of ITs creates a competitive environment not only for companies but also for regions. Hence, recognizing the future trend of technologies can be effective in decision-making with regard to technology selection and investment. Blockchain technology with its vast and impressive applications has received considerable attention from researchers, investors, and public agencies. The purpose of this research is to investigate blockchain technology to explore its trends according to their classification by use of the World Intellectual Property Organization (WIPO) database. Furthermore, we particularly evaluate the registered patents in the world's most well-known patent databases such as the USA patent database. We drew the current technology trends in blockchain patents by applying the text mining and clustering approach. The results represent that the registered patents in the USA patent database have been achieved in the growth phase. That means, attention to the blockchain is rising nowadays and most patents focused the cryptocurrencies and their applications in finance. However, blockchain technology is in the emergence phase and is evolving by researchers and inventors.

This paper analyzes the evolution of technology trends in education from 2010 to 2015, using as input the predictions made in the Horizon Reports on Higher Education, published yearly since 2004. Each edition attempts to forecast the most promising technologies likely to impact on education along three horizons: the short term (the year of the report), the midterm (the following two years), and the long term (the following four years). This paper applies social analysis, based on Google Trends, and bibliometric analysis, with data from Google Scholar and Web of Science, to these predictions in order to discover which technologies were successful and really impacted mainstream education, and which ones failed to have the predicted impact and why. This paper offers guidelines that may be helpful to those seeking to invest in new research areas.

The long path from initial research on oligonucleotide therapies to approval of antisense products is not unfamiliar. This lag resembles those encountered with monoclonal antibodies, gene therapies, and many biological targets and is consistent with studies of innovation showing that technology maturation is a critical determinant of product success. We previously described an analytical model for the maturation of biomedical research, demonstrating that the efficiency of targeted and biological development is connected to metrics of technology growth. The present work applies this model to characterize the advance of oligonucleotide therapeutics. We show that recent oligonucleotide product approvals incorporate technologies and targets that are past the established point of technology growth, as do most of the oligonucleotide products currently in phase 3. Less mature oligonucleotide technologies, such as miRNAs and some novel gene targets, have not passed the established point and have not yielded products. This analysis shows that oligonucleotide product development has followed largely predictable patterns of innovation. While technology maturation alone does not ensure success, these data show that many oligonucleotide technologies are sufficiently mature to be considered part of the arsenal for therapeutic development. These results demonstrate the importance of technology assessment in strategic management of biomedical technologies.

Innovation and development paradigm has dominated the world since the 19th century. In the era of industry 4.0, innovation ecosystem has been advocated globally. There are different innovation paradigms in history. Before 1960s, the competitive and monopolized paradigm was dominative, while between the 1970s and 1990s, the cluster and innovation valley paradigm had played a key role. In the 21st century, with the advent of industry 4.0, the innovation ecosystem paradigm is advocated globally. Accordingly, there exist different innovation strategies, in reality, considering different economic and social context. However, no matter if having in mind developed or developing countries, the ecosystem paradigm has high rewards for different companies and society. There is also evidence showing that research and development by top universities and research institutes have high productivity and benefits for enterprises and society nowadays, no matter the development state of the areas considered. The author analyzes by literature review and case study the necessity, feasibility, strategy and approaches of innovation ecosystem from a knowledge supplying side. The strategy and approaches include collaboration between university, industry, agriculture and government, talents education, knowledge diffusion, patent purchasing, technology tailoring, consulting, human resource training and platform construction. Science community also takes the role of standards development and maintaining, high technology forecasting and innovation monitoring. The patent office, start-ups, spin-offs and innovation labs act as the links between science, technology, and application.

The Termolator is an open-source high-performing terminology extraction system, available on Github. The Termolator combines several different approaches to get superior coverage and precision. The in-line term component identifies potential instances of terminology using a chunking procedure, similar to noun group chunking, but favoring chunks that contain out-of-vocabulary words, nominalizations, technical adjectives, and other specialized word classes. The distributional component ranks such term chunks according to several metrics including: (a) a set of metrics that favors term chunks that are relatively more frequent in a “foreground” corpus about a single topic than they are in a “background” or multi-topic corpus; (b) a well-formedness score based on linguistic features; and (c) a relevance score which measures how often terms appear in articles and patents in a Yahoo web search. We analyse the contributions made by each of these components and show that all modules contribute to the system's performance, both in terms of the number and quality of terms identified. This paper expands upon previous publications about this research and includes descriptions of some of the improvements made since its initial release. This study also includes a comparison with another terminology extraction system available on-line, Termostat (Drouin, 2003). We found that the systems get comparable results when applied to small amounts of data: about 50% precision for a single foreground file (Einstein's Theory of Relativity). However, when running the system with 500 patent files as foreground, Termolator performed significantly better than Termostat. For 500 refrigeration patents, Termolator got 70% precision vs. Termostat's 52%. For 500 semiconductor patents, Termolator got 79% precision vs. Termostat's 51%.

Nowadays, technology forecasting has become a multidisciplinary field employing various methods for detecting patterns in data sources in order to forecast trends and future state of different technologies. Technology forecasting is widely used by decision-makers for evaluating grant and contract proposals. Although there are some production-grade systems for technology forecasting for English, Russian patent databases and citation indexes are isolated from the global ones. This makes technology forecasting in Russia more complicate. In this research, we introduce government contracts as new possible parameter for technology forecasting. We think that government contracts indicate government’s interest in certain area of research or technology and thus may influence technology trends. We analyzed Russian government contracts; however, we consider this parameter suitable for technology forecasting systems in other languages as most of fundamental research conducted in most countries is sponsored by government. We study government contracts utilizing an information retrieval system pipeline exploiting latent semantic analysis and word2vec.

This paper presents prospective methodsimplemented during the development of Surveillance Technology in Electrical Vehicles and Prospective technologies in Colombia. These methods are developed for identifying related lines of research and emerging technologies which foster innovation. Methodologies are Delphi rounds and the planning of future scenarios. Both methodologies are implemented as tools to identify research topics that revolve around EVs in Colombia. The topics selected following the development of technology forecasting, were discussed by a group of experts who contextualized the information obtained from Colombia. From this analysis, scenarios were constructed. Technologies hampering the incursion of EVs in Colombia were determined, as well as the technologies with the greatest trend to be consumed and the most influential regulatory aspects.

Technology analysis is one of the important tasks in technology and industrial management. Much information about technology is contained in the patent documents. So, patent data analysis is required for technology analysis. The existing patent analyses relied on the quantitative analysis of the collected patent documents. However, in the technology analysis, expert prior knowledge should also be considered. In this paper, we study the patent analysis method using Bayesian inference which considers prior experience of experts and likelihood function of patent data at the same time. For keyword data analysis, we use Bayesian predictive interval estimation with count data distributions such as Poisson. Using the proposed models, we forecast the future trends of technological keywords of artificial intelligence (AI) in order to know the future technology of AI. We perform a case study to provide how the proposed method can be applied to real areas. In this paper, we retrieve the patent documents related to AI technology, and analyze them to find the technological trend of AI. From the results of AI technology case study, we can find which technological keywords are more important or critical in the entire structure of AI industry. The existing methods for patent keyword analysis were depended on the collected patent documents at present. But, in technology analysis, the prior knowledge by domain experts is as important as the collected patent documents. So, we propose a method based on Bayesian inference for technology analysis using the patent documents. Our method considers the patent data analysis with the prior knowledge from domain experts.

In this paper we describe a holistic AI forecasting framework which draws on a broad body of literature from disciplines such as forecasting, technological forecasting, futures studies and scenario planning. A review of this literature leads us to propose a new class of scenario planning techniques that we call scenario mapping techniques. These techniques include scenario network mapping, cognitive maps and fuzzy cognitive maps, as well as a new method we propose that we refer to as judgmental distillation mapping. This proposed technique is based on scenario mapping and judgmental forecasting techniques, and is intended to integrate a wide variety of forecasts into a technological map with probabilistic timelines. Judgmental distillation mapping is the centerpiece of the holistic forecasting framework in which it is used to inform a strategic planning process as well as for informing future iterations of the forecasting process. Together, the framework and new technique form a holistic rethinking of how we forecast AI. We also include a discussion of the strengths and weaknesses of the framework, its implications for practice and its implications on research priorities for AI forecasting researchers.

In the era characterized by significant dynamics of the environment traditional methods of anticipating the future, assuming the immutability of the factors affecting the forecasted phenomenon, may be in the long term ineffective. The modern approach of predicting the future of technology, taking into account the multidimensionality of the environment, is, among other things, the Future-Oriented Technology Analysis (FTA). Designing the FTA research procedure is a complex process, both in organizational and methodological terms. The catalogue of methods that can be used in this process is extensive and constantly open. However, in the source literature the rules for the selection of methods appropriate for the type of research were not specified. The ways of combining methods in the research process were also missing. The main aim of this article was to present the author’s classification of methods of future-oriented technology analysis and indicate the possibilities of its application. In the text, using statistical methods and artificial neural networks, the classification of methods with the potential of exploitation in prospective technology analysis was carried out. Each of the received classes was analysed, the characteristics of particular groups of methods were selected, and authorial names characterizing the given classes were chosen. According to the author, the application of the proposed classification of methods of future-oriented technology analysis facilitates the design of the FTA research process. It will contribute to the systematization and standardization of the manner of selection of research methods. It will also allow for the selection of complementary methods.

Today technological changes have accelerated to a great deal and future planning needs a deep understanding of these changes. This understanding incorporates precise study of introduction of new and vital technologies. Planning is one of the main applications of forecasting. Technology forecasting lets you to draw the future more clearly and design a better plan for future. There are several methods for technology forecasting. One is patent analysis. This article tries to track the changes of GIS (gas insulated substation) technology and its important subsystems by the means of patent information.

Identifying the emerging requirements for technology management will help organisations to prepare for the future and remain competitive. Indeed technology management as a discipline needs to develop and respond to societal and industrial needs as well as the corresponding technology challenges. Therefore, following a review of technology forecasting methodologies, a sector-based scenario planning approach has been used to derive the emerging requirements for technology management. This structured framework provided an analytical lens to focus on the requirements for managing technology in the healthcare, energy and higher education sectors over the next 5-10 years. These requirements include the need for new business models to support the adoption of technologies; integration of new technologies with existing delivery channels; management of technology options including R&D project management; technology standards, validation and interoperability; and decision-making tools to support technology investment.