arXiv subjects

Naoshi Uchihira

Publications and source records attributed to Naoshi Uchihira.

3 recordsLinked to original sources

arXiv2026

Tacit Knowledge Management with Generative AI: Proposal of the GenAI SECI Model

The emergence of generative AI is bringing about a significant transformation in knowledge management. Generative AI has the potential to address the limitations of conventional knowledge management systems, and it is increasingly being deployed in real-world settings with promising results. Related research is also expanding rapidly. However, much of this work focuses on research and practice related to the management of explicit knowledge. While fragmentary efforts have been made regarding the management of tacit knowledge using generative AI, the modeling and systematization that handle both tacit and explicit knowledge in an integrated manner remain insufficient. In this paper, we propose the "GenAI SECI" model as an updated version of the knowledge creation process (SECI) model, redesigned to leverage the capabilities of generative AI. A defining feature of the "GenAI SECI" model is the introduction of "Digital Fragmented Knowledge", a new concept that integrates explicit and tacit knowledge within cyberspace. Furthermore, a concrete system architecture for the proposed model is presented, along with a comparison with prior research models that share a similar problem awareness and objectives.

cs.AI↗

arXiv2024

Digital Business Model Analysis Using a Large Language Model

Digital transformation (DX) has recently become a pressing issue for many companies as the latest digital technologies, such as artificial intelligence and the Internet of Things, can be easily utilized. However, devising new business models is not easy for compa-nies, though they can improve their operations through digital technologies. Thus, business model design support methods are needed by people who lack digital tech-nology expertise. In contrast, large language models (LLMs) represented by ChatGPT and natural language processing utilizing LLMs have been developed revolutionarily. A business model design support system that utilizes these technologies has great potential. However, research on this area is scant. Accordingly, this study proposes an LLM-based method for comparing and analyzing similar companies from different business do-mains as a first step toward business model design support utilizing LLMs. This method can support idea generation in digital business model design.

cs.OH↗

arXiv2020

Agricultural Knowledge Management Using Smart Voice Messaging Systems: Combination of Physical and Human Sensors

The use of the Internet of Things (IoT) in agricultural knowledge management systems is one of the most promising approaches to increasing the efficiency of agriculture. However, the existing physical sensors in agriculture are limited for monitoring various changes in the characteristics of crops and may be expensive for the average farmer. We propose a combination of physical and human sensors (the five human senses). By using their own eyes, ears, noses, tongues, and fingers, farmers could check the various changes in the characteristics and conditions (colors of leaves, diseases, pests, faulty or malfunctioning equipment) of their crops and equipment, verbally describe their observations, and capture the descriptions with audio recording devices, such as smartphones. The voice recordings could be transcribed into text by web servers. The data captured by the physical and human sensors (voice messages) are analyzed by data and text mining to create and improve agricultural knowledge. An agricultural knowledge management system using physical and human sensors encourages to share and transfer knowledge among farmers for the purpose of improving the efficiency and productivity of agriculture. We applied one such agricultural knowledge management system (smart voice messaging system) to a greenhouse vegetable farm in Hokkaido. A qualitative analysis of accumulated voice messages and an interview with the farmer demonstrated the effectiveness of this system. The contributions of this study include a new and practical approach to an "agricultural Internet of Everything (IoE)" and evidence of its effectiveness as a result of our trial experiment at a real vegetable farm.

cs.CY↗