This study expands on previous surveys of computational theory of mind (ToM) focusing on four key areas. Data: We attempt to characterize data needed for this research and propose creating procedurally generated, multi-modal synthetic data for training and testing ToM systems, addressing the lack of open-source data of agent behaviors in closed environments. Metrics: We explore ToM evaluation beyond the Sally-Anne Test, considering child development stages and natural language understanding as potential measures. Model: We investigate building on recent ToM models, exploring open-ended learning in reinforcement learning, and applying neuroscientific insights to model architecture. We also examine ToM applications in everyday technologies, leveraging state-of-the-art transformer technologies and multimodal datasets. Theoretical Formalization: We aim to bridge cognitive science and psychology concepts with mathematical approaches to facilitate algorithm development in ToM.