2001年6月28日 情報 処理学会システムソフトウェアとオペレーティングシステムの研究会にて研 究発表．

「情報家電機器におけるプロアクティブな利用支援機構」

<概要>

本論文では，様々な習熟度の情報家電機器ユーザにサービス機能を能動的に提 供するプロアクティブ(能動的)な利用モデルを提案する．近年，家電機器の機 能が高機能化，多機能化しているにも関わらず，その利用形態がリアクティブ (受動的) であり，ユーザインタフェースが複雑であるため，習熟度の低いユー ザは機能を十分に使いこなせない場合が生じている．本稿で提案するプロアク ティブな利用モデルは，機器利用に必要な情報収集や機器操作学習を行い，ユー ザにサービス機能を容易に提供する．これにより，ユーザは要求を明示するこ となく操作簡便性を向上できる．本モデルを実現するために，情報家電機器の 利用支援ミドルウェアである，Proactive Support system on Networked Appliances(PSNA) を開発した．PSNAを用いることにより，プログラマはプロ アクティブな情報家電機器制御ソフトウェアを容易に構築できる．

2002年1月16日4th IEEE International Workshop on Networked Appliances(IWNA4)にて研究発表．

PRONA: A Proactive Support System for Networked Appliances

<概要>

In this paper, we propose a proactive operational model for home appliances. Recently, users of home appliances often have difficulty in operating appliances due to their high functionality, complex interface, which have many buttons, and users' lack of knowledge. The current operational model of home appliances is a reactive model, which it is necessary for users to notify appliances of operational information. This causes an increased burden on the user to understanding correct operational information. To cope with these issues we propose a proactive operational model. In this model, appliances acquire the operational information by themselves, and provide users with their services based on that information. Thus, our model eliminates the users' burden on explicitly expressing requests to the appliances to access their services. It is necessary for programmers to satisfy the autonomy of obtaining information, interaction between users and system, and operation simplification. Based on this model, we have developed a middleware, called Proactive Support System for Networked Appliances (PRONA). PRONA is realized by acquiring information necessary for appliance operation and learning the users' operational pattern. PRONA consists of three modules, which are the information acquisition module, the suggestion composition module, and the information manager module. The information acquisition module normalizes information gathered from sensors and appliances, and transmits it to the information manager module. The suggestion composition module learns the importance of related sensors when users operate the appliances. We represent the importance of related sensors as a weight, and transmit the weighted data to the information manager module. The suggestion composition module also provides the user suggestions from this weighted data. The information manager module stores this data in its database. We evaluated the use of PRONA in a proactive television application. PRONA's quantitative evaluation criteria are its processing time, the accuracy of its suggestions for user' action, and its learning time for the user' operational pattern while varying the number of proactive components.