Human centric digital twin implementation concept utilizing kNeXaR and SVMS Available to Purchase

In recent years, there has been a growing demand in Japan for efficient knowledge and technology transfer from veteran workers to younger workers and for improved productivity against the backdrop of labor shortages and aging populations in the nursing care and manufacturing industries. Conventional digital twin systems have focused on sensor data, and have been working to improve operational efficiency in digital space based on the collected sensor data. Therefore, we propose the concept of a Human Centric Digital Twin that captures human awareness and context in cyberspace, and utilizes human awareness and thought by machine sensors and human sensors. This knowledge generated by humans themselves contains a lot of contexts, which is difficult to incorporate in conventional DX methods. In many cases, the knowledge that workers in the field have is context-dependent and dynamic, unlike the formal knowledge described in manuals. Therefore, the cost of converting “Gen-Ba Knowledge” into formal knowledge as it is high, and has prevented its utilization. We have developed “kNowledge eXplication AugmenteR (kNeXaR)” and “Smart Voice Messaging System (SVMS)” as methods and tools to support structuring human awareness and thoughts. kNeXaR is a knowledge structuring method and tool for procedurally describing dynamic knowledge such as tasks in a goal-oriented manner while maintaining human readability and machine readability. SVMS is able to collect onsite knowledge in the form of spoken words, which is difficult to collect in the past. Using a smartphone, workers in the field can voice record what they notice and think about while working. In many cases, the messages obtained during the work are recorded with the purpose of the work, the content of the work, what they thought about what they observed in light of their own experience, and unusual situations. In this paper, while introducing the respective functions of kNeXaR and SVMS, we introduce the concept of implementing the Human Centric Digital Twin, which is a structured knowledge component based on messages from SVMS that collects work and observations in the field. We propose a method to dynamically create “Structured Knowledge Snippets”, which are parts of structured knowledge, based on messages from SVMS, and to construct structured knowledge in a workshop using kNeXaR.