Research

Intelligent suggestion techniques can enable low-friction selection-based input within virtual or augmented reality (VR/AR) systems. Such techniques leverage probability estimates from a target prediction model to provide users with an easy-to-use method to select the most probable target in an environment. For example, a system could highlight the predicted target and enable a user to select it with a simple click. However, as the probability estimates can be made at any time, it is unclear when an intelligent suggestion should be presented. Earlier suggestions could save a user time and effort but be less accurate. Later suggestions, on the other hand, could be more accurate but save less time and effort. This paper thus proposes a computational framework that can be used to determine the optimal timing of intelligent suggestions based on user-centric costs and benefits. A series of studies demonstrated the value of the framework for minimizing task completion time and maximizing suggestion usage and showed that it was both theoretically and empirically effective at determining the optimal timing for intelligent suggestions.