Agi Corporation

Other

Pst Corporation, Inc.

Japan Mathematical Institute Inc.

Daicel Corporation

Advanced Generation Interface, Inc.

Mitsuyoshi, Shunji

Shunji Mitsuyoshi

Shuji Shinohara

Hiroshi Okamoto

Masahiro Haitsuka

Fumihiro Miyoshi

Hidetoshi Kozono

Kaoru Ogata

Marina Takahashi

Fumiaki Monma

A regression model reflecting causality between variation of an explanatory variable and variation of a response variable is constructed. A prediction apparatus predicts characteristic values of a product by using process data obtained from a production facility. The prediction apparatus includes a process data acquisition unit that reads the process data from a storage device that stores the process data obtained from the production facility, and a prediction model generation unit that generates a prediction model on the basis of causality information that defines a combination of first process data and second process data or a value corresponding to the second process data. The first process data and the second process data or the value corresponding to the second process data are included in the read process data. The first process data is used as a predetermined explanatory variable. The second process data or the value corresponding to the second process data is used as response variable. The prediction model has learned features of the process data obtained from the production facility. The prediction model generation unit generates the prediction model and determines a positive/negative variation direction of the response variable in accordance with a positive/negative variation direction of the predetermined explanatory variable.

Prediction apparatus, prediction method, and program

An estimation method and device comprising at least one feature amount is calculated with sound data uttered by a subject, a degree of a psychosomatic state of the subject is calculated based on the calculated feature amount, and the psychosomatic state of the subject are estimated based on the calculated degree.

Estimation method, estimation program, estimation device, and estimation system

Includes an extraction unit that extracts first information on a physiological state of a subject and second information on at least one of an emotion and organ activity of the subject from information on a physiology of the subject; a calculation unit that obtains a degree of similarity between changes over time indicated by the first information and the second information which are extracted and calculates a shift amount from a predetermined state in which a homeostasis in the subject is maintained based on the obtained degree of similarity; and an estimation unit that estimates a pathology of the subject based on the calculated shift amount.

Estimation device, program, estimation method, and estimation system

An acquisition unit acquiring data including sound information uttered by a subject; a detection unit detecting feelings of the subject using the acquired data; a decision unit deciding weighting coefficients for data in accordance with the feelings of the subject; a calculation unit calculating energy which involves in human emotions and organ activities; a plurality of elements outputting signals each of which indicates the change in a state of the element as homeostasis in each of the human emotions and the organ activities; an emergence unit deciding feelings and intentions in accordance with the signals output from the elements; and a control unit performing phase transition of the state of the elements when an amount of change in the state of at least one element is equal to or less than a predetermined amount or when the state of at least one element is in a predetermined state.

Intention emergence device, intention emergence method, and intention emergence program

A speech analyzer includes a speech acquiring section, a frequency converting section, an autocorrelation section, and a pitch detection section. The frequency converting section converts the speech signal acquired by the speech acquiring section into a frequency spectrum. The autocorrelation section determines an autocorrelation waveform by shifting the frequency spectrum along the frequency axis. The pitch detection section determines the pitch frequency from the distance between two local crests or troughs of the autocorrelation waveform.

Speech analyzer detecting pitch frequency, speech analyzing method, and speech analyzing program

The present invention provides a will-expression modeling device simulating human will-expression responding to an direction input given from the outside, which includes an interest interpretation section, an emotion creating section and a will-expression section. The interest interpretation section collates the direction input with a predetermined hedonic interest relationship table and outputs a mood factor representing pleasure, displeasure or the like. The emotion creating section prepares a plurality of emotional states obtained by modeling human emotions as data and causes state transitions to occur in the emotional states according to the mood factor to simulate a change in human emotion responding to the direction input. The will-expression section prepares in advance a plurality of mental-will states obtained by modeling human will as data and selects a new mental-will state from the emotional states or a 'combination of a mental-will state and an emotional state'.

Will expression model device, psychological effect program, and will expression simulation method

An inspiration model device according to the invention is provided with a spontaneous emotion unit, a knowledge database, and a conception unit. The spontaneous emotion unit prepares, as data, plural emotion states in advance, which are obtained by modeling human emotions, and causes state transitions to occur repeatedly between the emotion states according a stochastic model of the Schr&#xf6;dinger equation. The knowledge database simulates a human inspiration source that is influenced by sensibility by classifying externally collected knowledge data depending on the degrees of correlation with the emotion states, and accumulates the knowledge data. When receiving an external input, the conception unit simulates human conception by combining the external input with an emotion state of the spontaneous emotion unit and searching the knowledge database for related knowledge data using, as a search key, the combination or the like.

Inspirational model device, spontaneous emotion model device, and related methods and programs

An object of the invention is to provide an emotion detecting method capable of detecting emotion of a human accurately, and provide sensibility generating method capable of outputting sensibility akin to that of a human. An intensity, a tempo, and intonation in each word of a voice are detected based on an inputted voice signal, amounts of change are obtained for the detected contents, respectively, and signals expressing each states of emotion of anger, sadness, and pleasure are generated based on the amounts of change. A partner's emotion or situation information is inputted, and thus instinctive motivation information is generated. Moreover, emotion information including basic emotion parameters of pleasure, anger, and sadness is generated, which is controlled based on the individuality information.

Growing community of inventors

Tokyo, Japan

Shunji Mitsuyoshi