We present a visual traceability interface that enables users to verify AI-generated answers by navigating from natural-language outputs to original meeting audio. Even with textual evidence, RAG users still struggle to verify whether answers come from specific utterances or from transcription or summarization errors. Our system introduces a four-level interaction model connecting (1) AI-generated answers, (2) contributing meetings, (3) summarized meeting chunks, and (4) time-aligned utterance-level audio. The interface lets users traverse this evidence chain and directly play the corresponding audio segments. We demonstrate how audio-grounded traceability supports answer verification, error detection, and trust calibration in AI-assisted meeting analysis.

In pursuit of realizing Society 5.0, this study explores efficient development methods for services tailored to individual user needs. The rapid evolution of digital devices and the diversification of user demographics have led to swiftly changing service needs, necessitating increased personalization. This research focuses on developing technologies that enable service development based on a deep understanding of specific user needs. By utilizing a virtual agent (VA)-based interactive need extraction system developed in previous research, and leveraging the Sharing Up cycling Cases with Context and Evaluation for Efficient Software Development System (SUCCEED System), we aim to automate the extraction of user needs and provide insights to developers. We propose an interactive need extraction method for novel, undeveloped services and a method for obtaining development cases based on these needs, thereby contributing to the efficiency of personalized service development approaches.

In advanced smart systems, heterogeneous distributed services are integrated dynamically, based on various contexts in cyber/physical worlds. Currently, the logic of such service integration is implemented specifically in each application. Thus, as the way of integration becomes sophisticated, the complexity and development effort of the application become quite expensive. In this research, we propose a service, called Uni-messe (Unified Rule-based Message Delivery Service), which provides context-aware service integration in an application-neutral manner. Based on ECR (Event-Condition-Routing) rules, Uni-messe routes an event message from an application to a designated application based on a specified condition. We first present the architecture of Uni-messe with publish/subscribe messaging platform. We then propose the data model of the ECR rules based on 6W1H (Who/Whom/When/Where/What/Why/How) event model. Finally, we define the condition evaluation and routing behaviors. Using the implementation of Uni-messe, we demonstrate automatic curtain control and daily routine automation in a smart home. Since Uni-messe de-couples the rule-based service integration from individual applications, it allows developers and users to implement and reuse flexible integration efficiently.

The personal adaptation of services, in which a system provides services according to the preferences and needs of individual users, is a key to the realization of the emerging Society 5.0. The personal adaptation of systems has been implemented through personal settings by users. However, it is very difficult for users who are not familiar with ICT to manually define the settings that meet their needs. In this paper, we therefore propose a new notion of smart system: Smart System with Interactive Personalization (SSIP). In SSIP, the system and the user have continuous and interactive conversations during the system operation. In the dialogue, the user tells his or her needs and the system introduces its functions. In this way, the user and the system understand each other and dynamically co-create personal settings. In this paper, in order to define SSIP, we present three functional requirements that the system must meet. We also characterize SSIP from the quality viewpoint by quality in use of the international standard SQuaRE. Finally, as a case study, we take the Mind Monitoring Service being developed by our group, and apply the proposed SSIP framework to individual adaptation of the incentive provision feature.