There is a growing expectation for the realization of proactive home-assistant robots that can assist users in their daily lives. It is essential to develop a framework that closely observes the user’s surrounding context, selectively extracts relevant information, and infers the user’s needs to proactively propose appropriate assistance. In this study, we first extend the Do-I-Demand dataset to define expected proactive assistance actions in domestic situations, where users make ambiguous utterances. These behaviors were defined based on common patterns of support that a majority of users would expect from a robot. We subsequently constructed a framework that infers users’ expected assistance actions from ambiguous utterances through commonsense reasoning. We explored two approaches: (1) multi-step reasoning using COMET as a commonsense reasoning engine, and (2) direct reasoning using large language models. Our experimental results suggest that both the multi-step and direct reasoning methods can successfully derive necessary assistance actions even when dealing with ambiguous user utterances.