Failure and resilience are important aspects of gameplay. This is especially important for serious and competitive games, where players need to adapt and cope with failure frequently. In such situations, emotion regulation -- the active process of modulating ones' emotions to cope and adapt to challenging situations -- becomes essential. It is one of the prominent aspects of human intelligence and promotes mental health and well-being. While there has been work on developing artificial emotional regulation assistants to help users cope with emotion regulation in the field of Intelligent Tutoring systems, little is done to incorporate such systems or ideas into (serious) video games. In this paper, we introduce a data-driven 6-phase approach to establish empathetic artificial intelligence (EAI), which operates on raw chat log data to detect key affective states, identify common sequences and emotion regulation strategies and generalizes these to make them applicable for intervention systems.