Kabushiki Kaisha Toshiba

Yuzo Okamoto

Kenta Cho

Masanori Hattori

Naoki Iketani

Hisao Setoguchi

Masayuki Okamoto

Takahiro Kawamura

Tomohiro Yamasaki

Hideo Umeki

According to one embodiment, an action history search device receives an inquiry from a user and outputs a inquiry time and a target of inquiry, decides a range for searching action history information representing history of the user's action together with a time of the user's action using the target of inquiry, and calculates an elapsed time from the time of the user's action within the range to the inquiry time. The device judges using the elapsed time and a narrowing-down model a probability on each response candidate to the inquiry based on the history of the action within the range. The narrowing-down model is used for judging according to the elapsed time the probability that the response candidate to the inquiry obtained from the history of the action is the user's desired response. The device outputs the response candidate according to the probability.

Action history search device

In general, embodiments provide an inter-facility movement estimation apparatus including: a movement map which stores location information about facilities and movement times therebetween; a positioning unit which obtains user's location information when starting a movement; a movement candidate list which stores given movement path candidates; a movement situation estimating unit which estimates a user's movement situation; a movement path estimating unit which selects, whenever the movement situation is changed, estimated movement path candidates from the given movement path candidates based on the nearest facility and the user's movement situation; and an adjusting unit which changes the likelihoods of the estimated movement path candidates, wherein one of the estimated movement path candidates having a highest likelihood thereamong is specified as an actual movement path of the user.

Inter-facility movement estimation apparatus, method and program

In one embodiment, a motion determination apparatus includes: a basic motion determination module determining a basic motion of a user, based on a sensor signal for detecting a motion of the user; a basic motion recording module recording the basic motion in a temporal sequence; a motion scene determination table configured to store the basic motion and a condition under which the basic motion occurs, for each motion scene; a motion scene determination module determining that the basic motion recorded in the temporal sequence corresponds to a certain motion scene, based on the motion scene determination table; a detailed label determination table storing a detailed label indicating a detailed motion in the motion scene, for each motion scene; and a detailed label determination module determining that the basic motion included in the motion scene corresponds to a certain detailed label, based on the detailed label determination table.

Motion determination apparatus, method and computer readable medium

External data acquired via an input device during display of presentation information on a display device is recorded, and feature amounts are calculated from the external data. An importance level is calculated by integrating the feature amounts for each section during the display period of the presentation information. First headline information of a part of the presentation information corresponding to a section having an importance level which exceeds a predetermined threshold is generated, and is presented to be aligned along the time axis. Previous display contents of the presentation information are searched for in response to a selection operation of the first headline information, and are displayed on the display device. Second headline information of a part of the presentation information corresponding to the section when the selection operation was made is generated, and is presented on the display device.

Information presentation method and information presentation apparatus

A context estimation rule is applied to spare time on a user's schedule, and contexts are estimated. On the basis of a task template, an input task is divided into plural subtasks. A task recommendation rule for recommending tasks to do from the relation between contexts and metadata of tasks is applied to the spare time of the user, and tasks to do in the spare time are recommended on the basis of the contexts of the spare time and the metadata of the subtasks. The recommended tasks are managed as schedules.

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Yokohama, Japan

Yuzo Okamoto