Generative personal assistance

We aim to develop a system that can give feedback or instructions to a person who wishes to better do something or do new things. Unlike the existing personal assistance methods based on manually defined rules, our goal is to develop a system with the following advantages: (1) individuality, meaning that the system output must be suitable for individual persons; (2) concreteness, so that the instructions are concrete enough for users to easily understand; and (3) automaticity, so that the system performs the above process automatically. To this end, we propose two kinds of learning-based (specifically, deep learning-based) approaches: One is a novel information propagation method to generate appropriate feedback according to inputs, and the other is a novel network architecture to represent attribute variations. We believe that these approaches will also lead to a generic media generation technique that will meet a variety of demands in the near future.

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