Using Patent Technology Networks to Observe Neurocomputing Technology Hotspots and Development Trends

Abstract

In recent years, development in the fields of big data and artificial intelligence has given rise to interest among scholars in neurocomputing-related applications. Neurocomputing has relatively widespread applications because it is a critical technology in numerous fields. However, most studies on neurocomputing have focused on improving related algorithms or application fields; they have failed to highlight the main technology hotspots and development trends from a comprehensive viewpoint. To fill the research gap, this study adopts a new viewpoint and employs technological fields as its main subject. Neurocomputing patents are subjected to network analysis to construct a neurocomputing technology hotspot. The results reveal that the neurocomputing technology hotspots are algorithms, methods or devices for reading or recognizing printed or written characters or patterns, and digital storage characterized by the use of particular electric or magnetic storage elements. Furthermore, the technology hotspots are discovered to not be clustered around particular fields but, rather, are multidisciplinary. The applications that combine neurocomputing with digital storage are currently undergoing the most extensive development. Finally, patentee analysis reveal that neurocomputing technology is mainly being developed by information technology corporations, thereby indicating the market development potential of neurocomputing technology. This study constructs a technology hotspot network model to elucidate the trend in development of neurocomputing technology, and the findings may serve as a reference for industries planning to promote emerging technologies.

Technology Fusion Characteristics in the Solar Photovoltaic Industry of South Korea: A Patent Network Analysis Using IPC Co-Occurrence

“…Patents are representative and reliable outputs of the research, development, and technology innovation of an industry [47], which considers both the product and process perspectives. As a result, patent analysis methodologies have been widely used to analyze technology development processes-for example, growth, fusion, and strategy [13][14][15][16]31,32,[48][49][50]. Among the various tools The government of South Korea set an aggressive goal for the share of renewable energy in the portfolio of power generation, the so-called 3020 Plan.…”

“…The patent network has been used to identify the post-production relationship and diffusion route between technologies by forming a network in the cited relationship [20,24,26,27]. Since the analysis of patents based on citation networks often only provides limited insights, such as linear flow in the order of the patents [28,29], network analysis based on a classification system has attracted more attention in the literature [30,32,51]. This approach has been adopted to analyze the connection, spread, and fusion of technology areas by examining the structured IPC code.…”

“…SNA focuses on the relations among actors, not individual actors, and their attributes [56]. This methodology has been widely used to understand the complicated interactions in technological evolution [57,58] since the network structure of patents and their IPCs can explain the complicated interdependency and trends in technological development and fusion [22,32,59].…”

“…However, as it focused on the linear flow of the patent citation perspective, it may not reflect the relationship across technology areas, thus providing a limited view of the overall technological fusion phenomena [25,26]. To overcome this limitation, network analysis including all technical areas supplied by classification codes in the patents has been undertaken [14,[27][28][29][30][31][32].…”

Exploring the Technological Changes of Green Agriculture in China: Evidence from Patent Data (1998–2021)

“…Actively developing green agriculture is of great significance to ensuring food security and promoting sustainable development in both social and economic terms [8,9]. Therefore, how to achieve green and low-carbon development of agriculture is a hot topic of concern for many scholars at present [10]. In September 2020, China set a clear target of reaching "peak carbon" by 2030 and "carbon neutrality" by 2060 [11].…”