  In today's society, information overload presents challenges in providing
optimal recommendations. Consequently, the importance of dialogue systems that
can discern and provide the necessary information through dialogue is
increasingly recognized. However, some concerns existing dialogue systems rely
on pre-trained models and need help to cope with real-time or insufficient
information. To address these concerns, models that allow the addition of
missing information to dialogue robots are being proposed. Yet, maintaining the
integrity of previous conversation history while integrating new data remains a
formidable challenge. This paper presents a novel system for dialogue robots
designed to remember user-specific characteristics by retaining past
conversation history even as new information is added.
