Identifying user intention (what the user wishes to achieve within a system) with minimal or ideally no direct user interaction is a major goal in pervasive computing. Achieving this goal requires a clear and consistent definition of intention, a concept widely used but understood differently across various studies. In this work, we first aim to clarify the different interpretations of intention, distinguishing between implicit and explicit intention. Subsequently, we compare various existing approaches from the literature, seeking to reconcile these diverse viewpoints and establish a common foundation for future research efforts. Key words: Pervasive computing, User intention, User intent prediction, Multimodal Large Language Models.