Technology Lifecycle Analysis Research Articles

A novel technology life cycle analysis method based on LSTM and CRF

A novel technology life cycle analysis method based on LSTM and CRF

Catching Up of Latecomer Economies in ICT for Sustainable Development: An Analysis Based on Technology Life Cycle Using Patent Data

In the digital economy era, ICT plays a vital role in supporting the sustainable and high-quality development of latecomer economies. Using technology life cycle analysis and patent data from the United States Patent and Trademark Office (1960–2014), this study analyzed the catching-up characteristics of latecomer economies (with the US as the first mover), including take-off time, growth time, growth rate, and ceiling values in nine sub-fields of information and communication technology (ICT). We applied the logistic and bi-logistic model to reveal the sequence of technological development and growth speed of different economies in different ICT sub-fields. The results show that European economies (Great Britain, France, and Germany) and the US developed first, followed by Japan, Korea, and Taiwan, with China (Mainland) coming later; Asian economies (Japan, Korea, Taiwan, and China (Mainland)) displayed synchronous development strategies, while European economies displayed non-synchronous development strategies. Asian economies are catching up with the US, whereas European economies are standing still both in imitation and indigenous processes. Korea and Taiwan prioritized catching up with a few sub-fields in the indigenous process. Finally, we analyzed the technological convergence among economies in their catching-up processes and proposed policy implications for the sustainable development of ICT latecomers.

Forecasting future bigrams and promising patents: introducing text-based link prediction

Purpose In recent years patents have become a very popular data source for forecasting technological changes. However, since a vast amount of patents are “worthless” (Moore, 2005), there is a need to identify the promising ones. For this purpose, previous approaches have mainly used bibliographic data, thus neglecting the benefits of textual data, such as instant accessibility at patent disclosure. To leverage these benefits, this study aims to develop an approach that uses textual patent data for predicting promising patents. Design/methodology/approach For the identification of promising patents, the authors propose a novel approach which combines link prediction with textual patent data. Thereby the authors are able to predict the emergence of hitherto unmentioned bigrams. By mapping these future bigrams to recent patents, the authors are able to distinguish between promising and nonpromising patents. To validate this approach, the authors apply the methodology to the case example of camera technology. Findings The authors identify stochastic gradient descent as a suitable algorithm with both a receiver operating characteristic area under curve score and a positive predictive value of 78%, which outperforms chance by a factor of two. In addition, the authors present promising camera patents for diverse application fields, such as cameras for surgical systems, cameras for rearview vision systems in vehicles or light amplification by stimulated emission of radiation detection and ranging cameras for three-dimensional imaging. Research limitations/implications This study contributes in at least three directions to scholarship. First, the authors introduce a novel approach by combining link prediction with textual patent analysis and, in this way, leverage the benefits of both worlds. Second, the authors add to all theories that regard novel technologies as a recombination of existing technologies in presenting word combinations from textual data as a suitable instrument for revealing recombination in patents. And third, the approach can be used by scholars as a complementary or even integrative tool with conventional forecasting methods like the Delphi technique or Scenario planning. Practical implications At least three practical implications arise from the study. First, incumbent firms of a technology branch can use this approach as an early-warning system to identify technological change and to identify opportunities related to their company’s technological competence and provide inspiration for new ideas. Second, companies seeking to tap into new markets may also be interested in the approach as managers could anticipate whether their company’s technological competences are in line with upcoming trends. Third, the approach may be used as a supportive tool for various purposes, such as investment decisions or technology life cycle analysis. Originality/value The approach introduces textual patent data as suitable means for forecasting activities. As the statistical validation reveals, the promising patents identified by the approach are cited significantly more often than patents with less promising prospects.

Evolution of the emergency drug innovation network and policy implications: Evidence from COVID-19 drug patents

Abstract Developing emergency drugs in time is key to responding to public health emergencies such as the coronavirus disease 2019 (COVID-19) pandemic. This research constructs patent assignee citation networks based on COVID-19 drug patents and applies technology life cycle analysis and social network analysis to examine these networks. The results reveal the evolution and structure of the emergency drug innovation network and identify innovation entities with different research and development (R&D) capabilities or a foundation for cooperation. This study attempts to provide the basis for theories and policies to accelerate R&D of emergency drugs. The key results are as follows: (1) Technologies of the emergency drug innovation network show fluctuating growth aligned closely with public health emergencies. (2) The network presents a sparse structure with many active factions, whose members have the advantage of cooperating. (3) Three types of innovation entities are identified: the main technology suppliers, potential suppliers of other supporting technologies, and peripheral suppliers of other supporting technologies.

Technological innovation in libraries

PurposeThe purpose of this study was to identify technological innovation in libraries and further examine the knowledge source and their effects during the technology life cycle (TLC).Design/methodology/approachThis paper discusses the technological innovation taking place in libraries. Patent citation analysis was used to capture the trend of technological innovation associated with libraries.FindingsThe findings are as follows: (1) library technologies are now in the ascent phase of their life cycles; (2) private companies from the United States, Germany, France, Japan and the United Kingdom are the top-five owners of intellectual property rights associated with library technology and (3) patent data along with knowledge and technology indicators can be interpreted in the light of library development. The knowledge source with the highest degree of scientific and technological orientation was identified as basic material chemistry. The major technological categories that have received the greatest knowledge effect from library-associated technological innovation are chemistry, electrical engineering, instruments, mechanical engineering, with other fields (civil engineering and furniture, games) being subject to less effect.Research limitations/implicationsThere are two research limitations in this study. First, the results use single informants, patents retrieved from United States Patent and Trademark Office, as the source of data. Second, this study uses patent citation measures for exploring the knowledge source and effect of technological innovation, these measures are only subjective for those new invention highly based technological advances. This study concludes that technological innovation for libraries will be characterized by an increasing role for science-intensive and interdisciplinary areas. This study also suggests that organizational learning facilitates innovation. Therefore, a library hoping to co-evolve with dynamic environment through technological innovation should improve its organizational learning processes.Practical implicationsTheories of technology-push and demand-pull were examined in relation to technological innovation taking place in libraries. The TLC analysis indicated that library technology is mainly in the ascent stage, suggesting that libraries have not achieved the strongest technological transformation. The findings suggest that the importance of demand-pull and technology-push vary over the TLC of libraries.Social implicationsTo survive in a dynamic environment library must be able to cope with increasing complexity and high-speed technological change. It is pivotal to integrate the views of users (as customers), software houses or design companies (as suppliers) as well as other libraries (as communities) into the sustainable development strategy of a library. In these contexts, libraries with the capacity to innovate will be able to respond to new demands faster and to invent and provide new services better than noninnovative libraries.Originality/valueAnalysis based on the technological innovation perspective to identify the future development of libraries is still lacking. This paper seeks to identify the technological innovation employed in libraries to accommodate the 21st century model of information-seeking behavior. This study identifies a variety of factors that have influenced the transformation of library services, and these include technology developments and new demands from library users. To illustrate the driving forces of technology-push in libraries, this paper examines holistic-patented technologies invented for libraries.

Start-ups as technology life cycle indicator for the early stage of application: An analysis of the battery value chain

Insights from battery research and development (R&D) need to be transferred into industrial application to create innovations and thus foster e.g. electro mobility. In terms of battery technology transfer, the early phase of application is particularly challenging due to the close intertwining between R&D and application. Therefore, the present study introduces start-ups as an additional indicator to capture the transition from science to industry within the technology life cycle. The findings show that despite highly dynamic R&D activities, technology transfer is only taking place on a very limited level. Surprisingly, start-ups focus on incremental improvements of existing technologies instead of introducing radical breakthrough-technologies. An analysis of the battery value chain reveals that opportunities for start-ups are rather located downstream in the value chain when integrating cells to battery systems and developing applications relying on innovative battery technologies. The findings contribute to the area of technology life cycle analysis explicitly using start-up companies as additional indicator for the critical transfer step from R&D to application. In a similar vein, technology forecasting literature, which is to date mainly focused on R&D, is expanded by a more application-centred perspective that allows identifying transfer opportunities along the technology value chain.

Patterns of technology life cycles: stochastic analysis based on patent citations

ABSTRACTPatent analysis has been considered as an effective means of estimating phases of a technology life cycle. However, previous studies have not considered the dynamic and idiosyncratic aspects of a technology’s progression since they were based on deterministic methods, mainly fitting s- or double s-shaped curves to patent application counts. Moreover, previous methods cannot be executed at the individual patent level. We propose a stochastic technology life cycle analysis to trace the phases of a technology’s progression based on patent citations and identify the patterns of technology life cycles at the individual patent level. At the heart of the proposed approach are a hidden Markov model to estimate the probability of a system being at a certain hidden state from observation and cluster analysis to group a set of objects according to their similarities. A case study of patents about laser technology in lithography is presented.

Stochastic technology life cycle analysis using multiple patent indicators

Technology life cycle analysis plays a crucial role in setting up investment-related strategies. The dominant approach to technology life cycle analysis utilizes curve fitting techniques to observe technological performance over time. However, doubts have been expressed about the accuracy and reliability of this method, due to its use of single indicators and the necessity of making assumptions about pre-determined growth curves. As a remedy, we propose a stochastic technology life cycle analysis that uses multiple patent indicators to examine a technology's progression through its life cycle. We define and extract seven time-series patent indicators from the United States Patent and Trademark Office database, and employ a hidden Markov model—which is an unsupervised machine learning technique based on a doubly stochastic process—to estimate the probability of a technology being at a certain stage of its life cycle. Based on this model, this paper also investigates patterns of technology life cycles, future prospects of a technology's progression, and characteristics of patent indicators between technology life cycle stages. The systematic process and quantitative outcomes the proposed approach offers can facilitate responsive and objective technology life cycle analysis. A case of molecular amplification diagnosis technology is presented.

Technology life cycle analysis method based on patent documents

To estimate the future development of one technology and make decisions whether to invest in it or not, one needs to know the current stage of its technology life cycle (TLC). The dominant approach to analysing TLC uses the S-curve to observe patent applications over time. But using the patent application counts alone to represent the development of technology oversimplifies the situation. In this paper, we build a model to calculate the TLC for an object technology based on multiple patent-related indicators. The model includes the following steps: first, we focus on devising and assessing patent-based TLC indicators. Then we choose some technologies (training technologies) with identified life cycle stages, and finally compare the indicator features in training technologies with the indicator values in an object technology (test technology) using a nearest neighbour classifier, which is widely used in pattern recognition to measure the technology life cycle stage of the object technology. Such study can be used in management practice to enable technology observers to determine the current life cycle stage of a particular technology of interest and make their R&D strategy accordingly.