Nippon Telegraph and Telephone Corporation

Akihiro Chiba

Kazuhiro Yoshida

Naoki Asanoma

Shinichiro Eitoku

Tsutomu Yabuuchi

Kana Eguchi

Hisashi Kurasawa

Shozo Azuma

Takuya Goda

A monetary risk prediction apparatus according to an embodiment includes: a predictive model recording unit that records a predictive model to predict time-series data showing a future asset amount of a user; and prediction means for receiving evaluation data that is time-series data including an asset amount and a numeric value showing a health condition of the user, inputting the evaluation data to the predictive model recorded on the predictive model recording unit, and outputting the time-series data showing the future asset amount of the user predicted by the predictive model according to the input.

Forecasting device, method, and program for predicting user financial assets and health

A display information generation device includes an importance calculation unit () and a display information generation unit (). The importance calculation unit receives at least one user object for at least one user (U), a plurality of speaker objects for a plurality of speakers (S, S) having a conversation with the at least one user, and a plurality of annotations with respect to each object generated for the at least one user object and the plurality of speaker objects, and calculates importance of the conversation between the one user and each of the plurality of speakers based on at least one of the plurality of speaker objects or the plurality of annotations. The display information generation unit determines a display form of each of the plurality of annotations generated with respect to the plurality of speaker objects based on the importance of the conversation, and generates display information on the plurality of annotations each having the display form.

Display information generation apparatus, display information generation method, and display information generation program

In an embodiment of this invention, in a learning phase, a state estimation device acquires activity state data and biometric data at that time from user terminals of a plurality of users, generates a regression formula representing the relationship between the biometric data and the activity state data using a regression analysis method on the basis of these pieces of measurement data, and calculates a difference between the coefficients of the regression formula of all users and each user to generate a coefficient correction regression formula representing a relationship between the difference of the coefficient and an average value of the biometric data. In an estimation phase, the state estimation device acquires biometric data of a new user, corrects a coefficient value of an activity state estimation regression formula to a coefficient value for the new user on the basis of the average value of the biometric data and the coefficient correction regression formula, and estimates the activity state of the new user using the regression formula having the corrected coefficient.

State estimation device, method and program

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Musashino, Japan

Akihiro Chiba