Support device, support method, and program

Abstract

To enrich support related to feelings compared with a conventional technique.SOLUTION: The assistance device includes an emotion information acquisition unit that acquires emotion values indicating emotions of a measurement subject in time series, a tendency estimation unit that estimates, for each tendency type, an emotion tendency that is a tendency related to an emotion using the emotion values in time series acquired by the emotion information acquisition unit, and an assistance information output unit that outputs assistance information related to the emotion tendency based on an estimation result by the tendency estimation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The disclosed technology relates to an assistance technology that provides emotional assistance to a user. [Background technology]

[0002] There is a conventional technique that provides emotional support to users. Patent Document 1 discloses an emotion estimation device that estimates emotions using biometric information. Specifically, the emotion estimation device in Patent Document 1 estimates emotions using biometric information and provides advice based on the estimation result of the current emotional state. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2023-178077 A (DENSO TEN Co., Ltd.) Summary of the Invention [Problem to be solved by the invention]

[0004] However, the emotion estimation device of Patent Document 1 has a problem in that the emotional state used to generate advice is only the current emotional state, and therefore there is a high possibility that the support regarding emotions will be insufficient.

[0005] The present disclosure is intended to solve the above-mentioned problems and aims to provide more comprehensive emotional support than ever before. [Means for solving the problem]

[0006] The support device of the present disclosure includes: an emotion information acquisition unit that acquires emotion values ​​in time series that are values ​​that indicate the emotion of the person being measured; a tendency estimation unit that estimates an emotion tendency, which is a tendency related to an emotion, for each tendency type using the time-series emotion values ​​acquired by the emotion information acquisition unit; a support information output unit that outputs support information related to emotions based on the estimation result by the tendency estimation unit; It is equipped with the following. [Effects of the Invention]

[0007] The present disclosure has the effect of providing more comprehensive emotional support than ever before. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an example of a configuration of a support device 100 according to a first embodiment of the present disclosure. [Figure 2] FIG. 2 is a flowchart showing an example of processing performed by the assistance device 100 according to the first embodiment of the present disclosure. [Figure 3] FIG. 3 is a diagram illustrating an example of the configuration of a support system 1 (1A) including a support device 100 (100A) according to the first embodiment of the present disclosure. [Figure 4] FIG. 4 is a flowchart showing an example of a data acquisition process in the assistance device 100 (100A) shown in FIG. [Figure 5] FIG. 5 is a diagram illustrating an example of emotion values. [Figure 6] FIG. 6 is a diagram illustrating an example of an emotional tendency and an example of an emotional tendency type according to the first embodiment of the present disclosure. [Figure 7] FIG. 7 is a diagram illustrating an example of the configuration of a support system 1 (1B) including a support device 100 (100B) according to the second embodiment of the present disclosure. [Figure 8] FIG. 8 is a flowchart showing an example of a data acquisition process in the assistance device 100 (100B) according to the second embodiment of the present disclosure. [Figure 9] FIG. 9 is a flowchart showing an example of a trend estimation process in the support device 100 (100B) according to the second embodiment of the present disclosure. [Figure 10]FIG. 10 is a flowchart showing an example of a support information output process in the support device 100 (100B) according to the second embodiment of the present disclosure. [Figure 11] FIG. 11 is a diagram illustrating an example of the configuration of a support system 1 (1C) including a support device 100 (100C) according to the third embodiment of the present disclosure. [Figure 12] FIG. 12 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100C) according to the third embodiment of the present disclosure. [Figure 13] FIG. 13 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100C) according to the third embodiment of the present disclosure. [Figure 14] FIG. 14 is a flowchart showing an example of a support information output process of the support device 100 (100C) according to the third embodiment of the present disclosure. [Figure 15] FIG. 15 is a diagram illustrating an example of an emotional tendency and an example of an emotional tendency type according to the third embodiment of the present disclosure. [Figure 16] FIG. 16 is a diagram illustrating an example of the configuration of a support system 1 (1D) including a support device 100 (100D) according to the fourth embodiment of the present disclosure. [Figure 17] FIG. 17 is a diagram illustrating an example of a means for receiving the state of the subject in the support device 100 (100D) according to the fourth embodiment of the present disclosure. [Figure 18] FIG. 18 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100D) according to the fourth embodiment of the present disclosure. [Figure 19] FIG. 19 is a flowchart showing an example of a support information output process in the support device 100 (100D) according to the fourth embodiment of the present disclosure. [Figure 20] FIG. 20 is a diagram illustrating an example of the configuration of a support system 1 (1E) including a support device 100 (100E) according to the fifth embodiment of the present disclosure. [Figure 21]FIG. 21A is a diagram illustrating an example of a means for accepting a subject's sleeping time in the support device 100 (100E) according to the fifth embodiment of the present disclosure, and FIG. 21B is a diagram illustrating an example of a means for indicating a transition in the sleeping time. [Figure 22] FIG. 22 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100E) according to the fifth embodiment of the present disclosure. [Figure 23] FIG. 23 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100E) according to the fifth embodiment of the present disclosure. [Figure 24] FIG. 24 is a flowchart showing an example of a support information output process in the support device 100 (100E) according to the fifth embodiment of the present disclosure. [Figure 25] FIG. 25 is a diagram illustrating an example of the configuration of a support system 1 (1F) including a support device 100 (100F) according to the sixth embodiment of the present disclosure. [Figure 26] FIG. 26 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100F) according to the sixth embodiment of the present disclosure. [Figure 27] FIG. 27 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100F) according to the sixth embodiment of the present disclosure. [Figure 28] FIG. 28 is a flowchart showing an example of a support information output process in the support device 100 (100F) according to the sixth embodiment of the present disclosure. [Figure 29] FIG. 29 is a diagram illustrating a first example of a hardware configuration for realizing the functions according to the configuration of the present disclosure. [Figure 30] FIG. 30 is a diagram illustrating a second example of a hardware configuration for realizing the functions according to the configuration of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0009] In order to explain the present disclosure in more detail, embodiments of the present disclosure will be described below with reference to the accompanying drawings.

[0010] Embodiment 1 In the first embodiment, a configuration example of a basic form for realizing the assistance technology of the present disclosure will be described.

[0011] An example of the configuration of a support device according to a first embodiment of the present disclosure will be described. FIG. 1 is a diagram illustrating an example of a configuration of a support device 100 according to a first embodiment of the present disclosure. The assistance device 100 provides assistance to the user regarding emotions. The user is, for example, an operator who operates the assistance device 100. The support device 100 estimates an emotional tendency, which is a tendency of emotions, using emotional information based on biological information of the subject, and outputs support information using the estimated emotional tendency. The subject to be measured may be the operator or a person other than the operator. For example, the operator may be a parent and the subject to be measured may be a child. A known technique can be used to obtain emotion information from biometric information, including, but not limited to, the method described below. Emotional trends are trends in emotions over time, and include, for example, trends such as emotional highs and lows over time, duration of emotional highs and lows, seasonal emotional highs and lows, monthly emotional highs and lows, day of the week emotional highs and lows, time periods of a day emotional highs and lows, and points of change in emotional highs and lows. Emotional trends may include trends other than those exemplified above, as long as they are trends in emotions over time. The assistance device 100 is realized, for example, in the form of a mobile terminal or smartphone that can be carried by the user. The assistance device 100 is also realized, for example, in the form of a terminal placed on a table. The assistance device 100 is also realized, for example, in the form of a server (server device or cloud server) that generates assistance information by communicating with a smartphone or the like using an application on the user's smartphone or the like and outputs the generated assistance information to the smartphone or the like. The assistance device 100 may also be realized in a form other than the above-mentioned examples. The support device 100 shown in FIG. 1 includes a data acquisition unit 110, a tendency estimation unit 130, and a support information output unit 150.

[0012] The data acquisition unit 110 acquires data used in the assistance device 100 . Data acquisition section 110 shown in FIG. 1 acquires emotion information. Data acquisition section 110 is configured to include emotion information acquisition section 111 .

[0013] Emotion information acquisition section 111 acquires emotion information in time series. Emotion information acquisition section 111 acquires emotion values, which are values ​​indicating the emotion of the person to be measured, in chronological order as emotion information.

[0014] The tendency estimation unit 130 estimates an emotional tendency based on time-series emotional information (emotion value). Tendency estimation section 130 uses the time-series emotion values ​​acquired by emotion information acquisition section 111 to estimate emotion tendencies, which are tendencies related to emotions, for each tendency type. For example, the trend types of emotional trends included in the estimation results by trend estimation unit 130 include one or more of the following: high and low states of emotional values ​​over time, duration of high and low states of emotional values, high and low states of emotional values ​​by season, high and low states of emotional values ​​by month, high and low states of emotional values ​​by day of the week, high and low states of emotional values ​​by time period in a day, or changing points in the high and low states of emotional values. The emotional level indicates the emotional level. The higher the positive emotional value, the more positive the emotional state, and the higher the negative emotional value, the more negative the emotional state. The emotional level is a numerical value that uses the type and strength of the emotion, such as fun, happiness, relaxation, sadness, melancholy, and sleepiness, with positive being positive and negative being negative. The duration of a high or low emotional state represents the duration of a high or low emotional state. The high and low states of the emotion value for each season represent the high and low states of the emotions for each season. The high and low emotional levels for each month represent the high and low emotional levels for each month. It shows the emotional level for each day of the week, and the emotional level for each day of the week. The emotional level for each time period in a day represents the emotional level for each time period in a day. The change point of the high / low state of the emotion value represents the change point of the high / low state of the emotion.

[0015] The support information output unit 150 outputs support information relating to emotions based on the emotional tendency. The support information output unit 150 outputs support information relating to emotions based on the estimation result by the tendency estimation unit 130. For example, the support information is information that includes any one or more of the following: emotional highs and lows over time, duration of emotional highs and lows, seasonal emotional highs and lows, monthly emotional highs and lows, day of the week emotional highs and lows, time periods of a day emotional highs and lows, or points of change in emotional highs and lows. Also, the support information is information that includes any one or more of the following: past emotional tendencies, emotional tendencies from the current time onwards, emotional factors, a plan based on emotional tendencies from the current time onwards, or advice based on emotional tendencies. The emotional high / low state indicates whether the emotion is high (positive) or low (negative), and further indicates states such as fun, joy, relaxation, sadness, depression, and sleepiness. The duration of a high or low emotional state indicates the time during which the emotional state continues to be the same or of the same magnitude. The emotional highs and lows for each season indicate the emotional state for each season, such as spring, summer, autumn, and winter. The emotional highs and lows for each month indicate the emotional state for each month. The emotional level for each day of the week indicates the emotional state for each day of the week. The emotional highs and lows for each time period of the day indicate the emotional highs and lows for each time period divided into units of one hour or more throughout the day. The change point of the emotional high or low state indicates the time, hour, day, month, and season at which the emotional high or low state changed. Furthermore, for example, the support information includes advice based on any one of emotional highs and lows, duration of emotional highs and lows, seasonal emotional highs and lows, monthly emotional highs and lows, day of the week emotional highs and lows, time periods in a day emotional highs and lows, or points of change in emotional highs and lows.

[0016] In addition to the above components, the support device 100 also includes a control unit (not shown), a storage unit (not shown), and a communication unit (not shown). A control unit (not shown) controls the entire support device 100 and each of its components. The control unit (not shown) starts up the support device 100 in accordance with, for example, an external command. The control unit (not shown) also controls the state of the support device 100 (operating state = start-up, shutdown, sleep, etc.). A storage unit (not shown) stores each piece of data used in the support device 100. The storage unit (not shown) stores, for example, output (output data) from each component in the support device 100, and outputs data requested for sending or acquisition by each component to the component that responds to the request. A communication unit (not shown) communicates with external devices. For example, communication is performed between the support device 100 (100A) and peripheral devices (e.g., an information source device and an output destination device). For example, when the support device 100 is not connected to the information source device and the output destination device via a wired connection, the communication unit (not shown) has a function of communicating between the support device 100 and the information source device and the output destination device. Furthermore, when a terminal device and a server (server device, cloud server) having the functions of the support device 100 cooperate to realize the support technology of the present disclosure, the communication unit (not shown) has a function of communicating between the terminal device and the server device. The control unit (not shown), the storage unit (not shown), and the communication unit (not shown) are the same in the embodiments described below.

[0017] Next, a processing example of the support device according to the first embodiment of the present disclosure will be described. FIG. 2 is a flowchart showing an example of processing performed by the assistance device 100 according to the first embodiment of the present disclosure. The process shown in FIG. 2 is a support method performed by the support device 100. For example, when the assistance device 100 shown in FIG. 1 is activated by receiving an operation from a user, or when a processing start condition is met, such as when an emotion value is received as emotion information, the assistance device 100 starts the processing shown in FIG. 2 ("Start").

[0018] Support device 100 then executes data acquisition processing (step ST100). In the data acquisition processing, emotion information acquisition section 111 in data acquisition section 110 of support device 100 acquires emotion values ​​as emotion information in chronological order from, for example, an external device. Emotion information acquisition section 111 outputs the emotion values ​​to tendency estimation section 130.

[0019] The support device 100 then executes a trend estimation process (step ST200). In the trend estimation process, when the trend estimation section 130 of the support device 100 receives an emotion value, it estimates an emotional tendency based on the time-series emotion information (emotion value). The trend estimation section 130 estimates an emotional tendency for each trend type, such as the time-series high and low state of emotion values, the duration of the high and low state of emotion values, the high and low state of emotion values ​​by season, the high and low state of emotion values ​​by month, the high and low state of emotion values ​​by day of the week, the high and low state of emotion values ​​by time period within a day, or the change point of the high and low state of emotion values. The trend estimation section 130 outputs the emotional tendency that is the estimation result.

[0020] The support device 100 then executes support information output processing (step ST300). In the support information output processing, the support information output unit 150 of the support device 100 outputs support information related to emotions based on the emotional tendency estimated by the tendency estimation unit 130. The support information output unit 150 generates and outputs support information using the emotional tendency.

[0021] The assistance device 100 then ends the process and waits ("End").

[0022] Next, a configuration example of a system including the support device according to the first embodiment of the present disclosure will be described. FIG. 3 is a diagram illustrating an example of the configuration of a support system 1 (1A) including a support device 100 (100A) according to the first embodiment of the present disclosure. In this specification, different symbols are used for each of the following embodiments, but components with similar symbols, such as support device 100 and support device 100 (100A), can be configured by combining some or all of them, or by omitting some of them. The support system 1 (1A) is a system including a support device 100 (100A). The support system 1 (1A) shown in FIG. 3 includes a support device 100 (100A), an information source device 600 (600A), and an output destination device 700.

[0023] The information source device 600 (600A) is a device or a group of devices that are a source of information used in the processing of the support device 100 (100A). The information source device 600 includes, for example, a biometric device that outputs biometric information. The information source device 600 may be configured integrally with the support device 100 (100A).

[0024] The output destination device 700 receives support information output by the support device 100 (100A). The output destination device 700 is, for example, an output device such as a display device or a sound output device. The output destination device 700 may be configured integrally with the support device 100 (100A).

[0025] The support device 100 (100A) has the same functions as the already explained support device 100. The support device 100 (100A) further has a function of calculating an emotion value as emotion information from biometric information. The support device 100 (100A) shown in FIG. 3 includes a data acquisition unit 110 (110A), a tendency estimation unit 130 (130A), and a support information output unit 150 (150A).

[0026] The data acquiring unit 110 (110A) has the same functions as the already explained data acquiring unit 110. The data acquiring unit 110 (110A) further acquires biometric information. Data acquisition section 110 (110A) shown in FIG. 3 includes emotion information acquisition section 111 (111A) and biological information acquisition section 112.

[0027] The biological information acquiring unit 112 acquires biological information of the subject. An example of biological information such as sleepiness level and fatigue level acquired by the biological information acquisition unit 112 will be described. The biometric information acquiring unit 112 detects and collects biometric information of the subject at a predetermined cycle. The biometric information acquiring unit 112 acquires one or more types of biometric information. When there are multiple subjects, the biometric information acquiring unit 112 acquires the biometric information of each of the multiple subjects present in the room. The biometric information acquiring unit 112 stores the detected values ​​of the acquired biometric information of the subject in a biometric information storage unit (not shown) as pre-conversion biometric information values. When the biometric information of multiple subjects is acquired, the biometric information acquiring unit 112 stores the pre-conversion biometric information values ​​for each subject in a biometric information storage unit (not shown). Here, when the biological information acquired by the biological information acquisition unit 112 is an actual measurement value, it is a value before being converted into biological information to be acquired, such as a drowsiness level, a fatigue level, etc. For this reason, here, the value of the biological information detected by the biological information acquisition unit 112 is referred to as a pre-conversion biological information value. Furthermore, the values ​​of the biometric information such as drowsiness level and fatigue level into which the pre-conversion biometric information values ​​are converted are converted under predetermined conditions. Therefore, the values ​​of the biometric information into which the pre-conversion biometric information values ​​are converted are called converted biometric information values. Furthermore, the biometric information acquiring unit 112 may reflect the tendency of the biometric information specific to the subject to be measured in the pre-conversion biometric information value, and may set the value of the biometric information adapted to the tendency of the biometric information specific to the subject to be measured as the biometric information value. Therefore, here, the value of the biometric information adapted to the tendency of the biometric information specific to the subject to be measured, and reflecting the tendency of the biometric information specific to the subject to be measured, is referred to as the adapted biometric information value. Furthermore, the biological information acquiring unit 112 may convert the pre-conversion biological information value based on predetermined upper and lower limit values, instead of the value of the biological information adapted to the tendency of the biological information unique to the subject by reflecting the tendency of the biological information unique to the subject in the pre-conversion biological information value. Therefore, here, the value of the biological information obtained by converting the pre-conversion biological information value based on predetermined upper and lower limit values, instead of the value of the biological information adapted to the tendency of the biological information unique to the subject by reflecting the tendency of the biological information unique to the subject in the pre-conversion biological information value, is referred to as the default converted biological information value. Examples of the biological information of the subject to be measured acquired by the biological information acquiring unit 112 include stress, concentration level, sleepiness level, fatigue level, and emotions. The biological information of the subject to be measured acquired by the biological information acquiring unit 112 is not limited to one type. The biological information acquiring unit 112 can simultaneously acquire multiple types of biological information for one subject to be measured. The pre-conversion biometric information value is biometric information of the subject acquired by the biometric information acquiring unit 112, and is an actual measurement value acquired by the biometric information acquiring unit 112. The pre-conversion biometric information value measured by the biometric information acquiring unit 112 is sent to a biometric information storage unit (not shown) in association with date and time information, and is stored in the biometric information storage unit (not shown). The date and time information is information on the date and time when the pre-conversion biometric information value of the subject was measured by the biometric information acquiring unit 112. The bioinformation acquiring unit 112 collects the pre-conversion bioinformation values ​​of the subject at a predetermined cycle, so that changes in the physical condition of the subject can be recorded and can be used as information for detecting an abnormal condition of the subject. Note that the bioinformation acquiring unit 112 may acquire and collect the pre-conversion bioinformation values ​​of the subject at a timing designated by the operator of the support device 100 or the subject, or at a predetermined timing. The biometric information acquiring unit 112 acquires actual measurement values ​​(pre-conversion biometric information values) obtained by, for example, a Doppler sensor (information source device 600) and converts the pre-conversion biometric information values ​​to acquire biometric information. The biometric information acquiring unit 112 may also be configured to include a sensor (information source device 600). Note that the type of sensor used by the biometric information acquiring unit 112 to acquire actual measurement values ​​is not critical as long as it is a sensor that can appropriately acquire desired biometric information. Furthermore, when acquiring multiple types of biometric information, the biometric information acquiring unit 112 can use a sensor suitable for acquiring biometric information for each type of biometric information. For example, pulse data of the subject can be acquired using a Doppler sensor. The biological information acquiring unit 112 can acquire biological information of the subject, i.e., pre-conversion biological information values ​​of the subject, based on the pulse data of the subject acquired by the Doppler sensor. Considering stress level as an example, the biological information acquiring unit 112 can estimate the stress level, which is the balance of the subject's autonomic nerves, from fluctuations in the subject's pulse. The biological information acquiring unit 112 can acquire the pre-conversion biological information value of the subject's stress level using the formula stress level=LF / HF. In this case, the biological information acquiring unit 112 extracts a high frequency (HF) fluctuation component corresponding to respiratory fluctuation and a low frequency (LF) component corresponding to Mayer wave, which is blood pressure fluctuation, from the time series data of pulse fluctuation, and compares the magnitudes of both.The biological information acquiring unit 112 can then acquire the value of LF / HF as a stress index, which is the activity level of the sympathetic nerves. Furthermore, the biological information acquiring unit 112 can use sensors such as a frequency modulated continuous wave (FMCW) sensor and an acoustic wave sensor in addition to the Doppler sensor.

[0028] Emotion information acquisition section 111 (111A) acquires emotion information in time series. Emotion information acquisition section 111 (111A) acquires emotion values, which are values ​​indicating the emotion of the person to be measured, in chronological order as emotion information. Emotion information acquisition section 111 (111A) shown in Figure 3 acquires emotion information by calculating it using biometric information acquired by biometric information acquisition section 112. Emotion information acquisition section 111 (111A) accepts known biometric information such as drowsiness level, exhaustion level, activity level, and immersion level, and calculates an emotion value by combining the values ​​of drowsiness level, exhaustion level (exhaustion level is also called fatigue level), activity level, or immersion level. As a more specific example, four types of values ​​are combined and arranged on a plane coordinate system using the known Russell circle, with a horizontal axis indicating pleasantness / unpleasantness and a vertical axis indicating arousal / calmness, and a reference axis is further provided as a basis for calculating the emotion value, and the emotion value is calculated according to the distance between the reference axis and the arrangement position (see Figure 5 described below). Alternatively, the emotion information acquisition unit 111 (111A) may be configured to acquire biological information that forms the basis of values ​​such as drowsiness level, exhaustion level, activity level, and immersion level, calculate values ​​such as drowsiness level, exhaustion level, activity level, and immersion level, and then calculate the emotion value.

[0029] The tendency estimation section 130 (130A) has a function of estimating an emotional tendency based on time-series emotional information, similar to the tendency estimation section 130 already described.

[0030] The support information output unit 150 (150A) has a function of outputting support information relating to emotions based on emotional tendencies, similar to the support information output unit 150 already described.

[0031] Next, among the processes in the support device 100 (100A), those that differ from those in the support device 100 will be described. FIG. 4 is a flowchart showing an example of a data acquisition process in the assistance device 100 (100A) shown in FIG. FIG. 5 is a diagram illustrating an example of emotion values. For example, the data acquisition unit 110A in the support device 100A shown in FIG. 3 starts the process shown in FIG. 4 when it is activated by receiving an operation from a user or when it meets a processing start condition such as receiving biometric information. ("Start")

[0032] Data acquiring section 110A then executes biometric information acquisition processing (step ST1110). In the biometric information acquisition processing, biometric information acquiring section 112 of data acquiring section 110A acquires biometric information from information source device 600A. Biometric information acquiring section 112 outputs the acquired biometric information to emotion information acquiring section 111.

[0033] Data acquisition section 110A then executes emotion information acquisition processing (step ST1120). In emotion information acquisition processing, emotion information acquisition section 111 of data acquisition section 110A acquires biometric information output from biometric information acquisition section 112. Emotion information acquisition section 111 calculates an emotion value using the biometric information. Emotion information acquisition section 111 calculates an emotion value using biometric information for estimating emotions such as "sleepiness level," "exhaustion level," "activity level," and "immersion level." Emotion information acquisition section 111 combines each piece of biometric information and analyzes each piece of biometric information. Emotion information acquisition section 111 fits the biometric information into a Russell circle that is composed of a horizontal axis representing positive and negative emotional states and a vertical axis representing alertness and calmness, and that can analyze the details of emotions such as happy, sad, and sleepy. Emotion information acquisition unit 111 uses an axis on the Russell circle where one direction is a positive value and the opposite direction is a negative value as a reference axis, as shown in Fig. 5, and quantifies each emotion arranged on the Russell circle. Note that although the reference axis is shown along the horizontal axis in Fig. 5, it is not limited to this, and for example, an axis where the stronger arousal and pleasure are indicated by a positive value and the stronger calmness and discomfort are indicated by a negative value may be used as the reference axis.

[0034] When data acquisition section 110A outputs the emotion value as emotion information to tendency estimation section 130, it ends the processing shown in FIG. 4 and goes into standby mode (“end”).

[0035] Here, the emotional tendencies and tendency types estimated in the present disclosure will be described. FIG. 6 is a diagram illustrating an example of an emotional tendency and an example of an emotional tendency type according to the first embodiment of the present disclosure. FIG. 6 shows an example of the emotion values ​​of a measurement subject, in time series, for one day, such as October 8th, 2026 (Thursday). For example, in the afternoon, the emotion value is a large positive value, indicating a positive emotional tendency, and in the morning and evening, the emotion value is a large negative value, indicating a negative emotional tendency (trend (1) high / low emotional state in Figure 6). It is also possible to estimate emotional trends, such as a sustained high positive state in the afternoon (trend (2) duration of high and low states in Figure 6). It can also be assumed that the above-mentioned emotional trends are expressed in the autumn season, in October (trend (3) seasonal and monthly highs and lows in Figure 6). It can also be assumed that the above-mentioned emotional trends are expressed on Thursdays (trend (4) high and low levels by day of the week in Figure 6). In addition, it can be estimated that the emotional state is high between 11:00 and 14:00, and low between 7:00 and 10:00 (trend (5) high and low emotional states by time period in Figure 6). Also, for example, it is possible to estimate an emotional trend, such as a change point in the emotional state at 9 pm (trend (6) change point in high / low state in FIG. 6).

[0036] This embodiment shows a configuration including the following configuration. [1] an emotion information acquisition unit that acquires emotion values ​​in time series that are values ​​that indicate the emotion of the person being measured; a tendency estimation unit that estimates an emotion tendency, which is a tendency related to an emotion, for each tendency type using the time-series emotion values ​​acquired by the emotion information acquisition unit; a support information output unit that outputs support information regarding an emotional tendency based on the estimation result by the tendency estimation unit; A support device comprising: As a result, the present disclosure has an effect of providing a support device that enables more comprehensive support regarding emotions than ever before.

[0037] This embodiment shows a configuration including the following configuration.

[12] A support method using a support device, an emotion information acquisition unit of the support device acquires emotion values ​​in time series, the emotion values ​​being values ​​indicating the emotion of the subject; a tendency estimation unit of the support device estimating an emotion tendency, which is a tendency related to an emotion, for each tendency type using the time-series emotion values ​​acquired by the emotion information acquisition unit; a support information output unit of the support device that outputs support information related to the emotional tendency based on the estimation result by the tendency estimation unit; A support method characterized by: As a result, the present disclosure has the effect of providing a support method that enables more comprehensive emotional support than ever before.

[0038] This embodiment shows a configuration including the following configuration.

[13] Computer, an emotion information acquisition unit that acquires emotion values ​​in time series that are values ​​that indicate the emotion of the person being measured; a tendency estimation unit that estimates an emotion tendency, which is a tendency related to an emotion, for each tendency type using the time-series emotion values ​​acquired by the emotion information acquisition unit; a support information output unit that outputs support information regarding an emotional tendency based on the estimation result by the tendency estimation unit; an assist device comprising: A program characterized by operating as As a result, the present disclosure has the effect of being able to provide a program that enables more comprehensive emotional support than ever before.

[0039] This embodiment further shows an example of an embodiment including the following configuration. [2] The support information is The information includes one or more of the following: emotional highs and lows over time, duration of emotional highs and lows, seasonal emotional highs and lows, monthly emotional highs and lows, day-of-the-week emotional highs and lows, time-of-day emotional highs and lows, or points of change in emotional highs and lows. An assistance device comprising: As a result, the present disclosure further has the effect of providing an assistance device that enables further enhancement of emotional assistance. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the above assistance method, or the above program.

[0040] This embodiment further shows an example of an embodiment including the following configuration. [3] a biological information acquisition unit for acquiring biological information of a subject, the emotion information acquisition unit calculates and acquires an emotion value using the biometric information acquired by the biometric information acquisition unit; 1. A support device comprising: As a result, the present disclosure further has the effect of being able to provide a support device that makes it possible to enhance support related to emotions even in cases where emotion values ​​cannot be output externally from the device. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the above assistance method, or the above program.

[0041] Embodiment 2 In the above-described first embodiment, a basic configuration example for outputting support information related to emotions based on emotional tendencies has been described. Here, when the subject of measurement, whose emotional tendency is to be estimated, is a child, the subject has complex emotional tendencies that are unique to children. Furthermore, there are cases where the parent and child are apart and cases where they are face-to-face. For example, there are situations where the parent, who is the operator, wants to know the emotional tendency of the child when they are left alone. For example, there are situations where the parent, who is the operator, wants to know the emotional tendency of the child during opportunities to communicate with the child. Therefore, in the second embodiment, an example of a configuration in which emotional factors that are factors of emotions indicated in an emotional tendency are estimated and support information is output based on the emotional factors will be described. In embodiment 2, among the components of embodiment 2, those components that are similar to the components of embodiment 1 already described will be given the same names and similar symbols, and duplicate explanations will be omitted as appropriate.

[0042] Next, a configuration example of a support device according to a second embodiment of the present disclosure will be described. FIG. 7 is a diagram illustrating an example of the configuration of a support system 1 (1B) including a support device 100 (100B) according to the second embodiment of the present disclosure. The support system 1 (1B) shown in Fig. 7 includes a support device 100 (100B), an information source device 600 (600B), and an output destination device 700. The output destination device 700 has the same configuration as the output destination device 700 already described.

[0043] The information source device 600 (600B) may be configured to further include, in addition to the information source device 600 already described, a personal authentication device that outputs object identification information for identifying the person to be measured. In this case, the information source device 600 (600B) performs personal authentication by known information analysis processing using, for example, a captured image and / or signals from other sensors, and outputs object identification information for identifying the person to be measured.

[0044] The support device 100 (100B) shown in FIG. 7 includes a data acquisition unit 110 (110B), a tendency estimation unit 130 (130B), and a support information output unit 150 (150B).

[0045] Data acquisition section 110 (110B) shown in FIG. 7 includes emotion information acquisition section 111 and object identification information acquisition section 113. Emotion information acquisition section 111 has the same configuration as emotion information acquisition section 111 already explained. The object identification information acquisition unit 113 acquires the object identification information. The object identification information acquisition unit 113 acquires object identification information received from, for example, the information source device 600 (600B). Alternatively, the target identification information acquisition unit 113, for example, receives an operation by an operator of the support device 100 (100B) and generates and acquires target identification information based on the operation. In this case, for example, while an emotion value is being acquired from the subject to be measured, the operator of the support device 100 (100B) inputs information identifying the subject to be measured, thereby generating target identification information. This makes it possible to improve the accuracy of estimating the emotional tendency for each individual.

[0046] The tendency estimation unit 130 (130B) estimates an emotional tendency based on time-series emotional information. The tendency estimation unit 130 (130B) estimates an emotional tendency by referring to the tendency storage unit 170 (170B). Moreover, the tendency estimation unit 130 (130B) further estimates an emotional tendency for each measurement subject indicated in the subject identification information. The tendency estimation unit 130 (130B) combines the object identification information and the emotional tendency and outputs the result as an estimation. The tendency estimation unit 130 (130B) outputs the estimation result to the support information output unit 150 (150B) and the tendency storage unit 170 (170B). For example, the tendency estimation unit 130 (130B) outputs the emotional tendency at the current time to the support information output unit 150 (150B) and outputs the past emotional tendency to the tendency storage unit 170 (170B). When outputting the past emotional tendency to the tendency storage unit 170 (170B), the tendency estimation unit 130 (130B) outputs the past emotional tendency determined at intervals such as every day to the tendency storage unit 170 (170B).

[0047] The tendency storage unit 170 (170B) stores past emotion tendencies estimated by the tendency estimation unit 130B. Furthermore, the tendency holding unit 170 (170B) updates and holds the emotional tendency using the emotional tendency estimated by the tendency estimation unit 130B and the emotional tendency that has already been held. The tendency storage unit 170 (170B) can cause the tendency estimation unit 130B or the support information output unit 150 (150B) to refer to the emotion tendency stored therein. The tendency holding unit 170 (170B) may be configured outside the support device 100 (100B). Furthermore, in the support device 100 (100B), the tendency holding unit 170 (170B) can be realized in the form of a server. That is, the tendency holding unit 170 (170B) in the support device 100 (100B) is configured as a server, and everything other than the tendency holding unit 170 (170B) is configured as terminal devices. This enables the server including the tendency holding unit 170 (170B) to collect emotional tendencies of a large number of measurement subjects from a plurality of terminal devices and perform analysis processing and learning processing. By using the results to estimate an emotional tendency, the accuracy of estimating past emotional tendencies and the accuracy of estimating an emotional tendency from the current time onwards can be improved.

[0048] The support information output unit 150 (150B) outputs support information based on the emotional tendency. The support information output unit 150B predicts an emotional tendency from the current time onward using the past emotional tendency estimated by the tendency estimation unit 130B, and generates and outputs support information using the predicted emotional tendency. The support information output unit 150 (150B) shown in FIG. 7 includes a tendency prediction unit 151 (151B), an advice generation unit 153 (153B), and an emotion factor estimation unit 155 (155B).

[0049] The tendency prediction unit 151 (151B) predicts an emotional tendency from the current time onward using the emotional tendency at the current time and past emotional tendencies. The tendency prediction unit 151 (151B) predicts an emotional tendency from the current time onward by using the emotional tendency at the current time and referring to past emotional tendencies in the tendency storage unit 170 (170B). The support information output unit 150 (150B) generates and outputs support information using the emotional tendency from the current time onward, which is the result of prediction by the tendency prediction unit 151 (151B). Alternatively, the support information output unit 150 (150B) outputs support information including one or more of the emotional tendency of the subject to be measured up to the current time, the emotional tendency of the subject to be measured at the current time, or the emotional tendency of the subject to be measured after the current time.

[0050] The emotional factor estimation unit 155 (155B) estimates emotional factors based on the emotional tendency of the subject being measured. The emotional factor estimation unit 155 (155B) estimates emotional factors for the subject's emotional tendency up to the current time, the subject's emotional tendency at the current time, or the subject's emotional tendency after the current time. The emotional factor estimation unit 155 (155B) is configured with a prediction model trained based on, for example, general theories about children's emotions, and when an emotional tendency is input, outputs emotional factors indicated by the emotional tendency. General theories about children's emotions, for example, indicate factors such as sleepiness, fatigue, or anxiety as factors that cause a bad mood. Also, for example, indicate factors such as a fight, a change in environment (seat change, class change), or being scolded as factors that cause emotional instability. Also, for example, indicate factors such as a bath, a meal, or exercise as factors that provide a change of mood. Also, for example, indicate factors such as a parent's state (irritability) or a parent's state (smiling) as factors that affect emotions. The support information output unit 150 (150B) further outputs support information including the emotion factor estimated by the emotion factor estimation unit 155 (155B). Alternatively, the support information output unit 150 (150B) predicts an emotional tendency from the current time onwards using a past emotional tendency, an emotional tendency at the current time, and emotional factors, and outputs support information including advice based on the prediction result. Specifically, the support information output unit 150 (150B) predicts an emotional tendency from the current time onwards using a past emotional tendency estimated by the tendency estimation unit 130B and stored in the tendency storage unit 170 (170B), an emotional tendency at the current time estimated by the tendency estimation unit 130B, and emotional factors estimated by the emotional factor estimation unit 155 (155B), and outputs support information including advice based on the prediction result.

[0051] The advice generation unit 153 (153B) generates advice using the emotional tendency. The advice generation unit 153 (153B) generates advice using the past emotional tendency, the emotional tendency at the current time, the emotional tendency after the current time, and the emotional factor. The advice generation unit 153 (153B) is configured using, for example, a generation AI. The support information output unit 150 (150B) outputs support information including the advice generated by the advice generation unit 153 (153B).

[0052] Next, a processing example of the support device according to the second embodiment of the present disclosure will be described. FIG. 8 is a flowchart showing an example of processing by the support device 100 (100B) according to the second embodiment of the present disclosure. The process shown in FIG. 8 is a support method by the support device 100 (100B). For example, when the assistance device 100 (100B) shown in FIG. 7 is activated by receiving an operation from a user, or when a processing start condition is met, such as when an emotion value as emotion information is received, the assistance device 100 (100B) starts the processing shown in FIG. 8 ("Start").

[0053] Support device 100 (100B) then executes data acquisition processing (step ST2100). In the data acquisition processing, emotion information acquisition section 111 in data acquisition section 110 of support device 100 (100B) acquires emotion values ​​as emotion information in chronological order from, for example, an external device. Emotion information acquisition section 111 outputs the emotion values ​​to tendency estimation section 130. Furthermore, in the data acquisition process, the target identification information acquisition unit 113 of the data acquisition unit 110 (110B) acquires target identification information. The target identification information acquisition unit 113 acquires target identification information received from, for example, the information source device 600 (600B). Alternatively, the target identification information acquisition unit 113 receives, for example, an operation by an operator of the support device 100 (100B) and generates and acquires target identification information based on the operation. In this case, for example, while an emotion value is being acquired from the subject to be measured, the operator of the support device 100 (100B) inputs information identifying the subject to be measured, thereby generating target identification information. Data acquisition section 110 (110B) outputs the emotion value acquired by emotion information acquisition section 111 and the object identification information acquired by object identification information acquisition section 113 to tendency estimation section .

[0054] The support device 100 (100B) then executes a trend estimation process (step ST2300). In the trend estimation process, when the trend estimation section 130 of the support device 100 (100B) receives an emotion value, it estimates an emotional tendency based on the time-series emotion information (emotion value). The trend estimation section 130 estimates an emotional tendency for each trend type, such as the high or low state of the emotion value, the duration of the high or low state of the emotion value, the high or low state of the emotion value by season, the high or low state of the emotion value by month, the high or low state of the emotion value by day of the week, the high or low state of the emotion value by time period within a day, or the change point of the high or low state of the emotion value. The trend estimation section 130 outputs the emotional tendency that is the estimation result.

[0055] The support device 100 (100B) then executes an estimated tendency holding process (step ST2400). In the estimated tendency holding process, the tendency holding unit 170 (170B) of the support device 100 (100B) holds the emotion tendency estimated by the tendency estimation unit 130B in combination with the target identification information.

[0056] The support device 100 (100B) then executes support information output processing (step ST2500). In the support information output processing, the support information output unit 150 (150B) of the support device 100 (100B) further outputs support information including emotional factors estimated by the emotional factor estimation unit 155 (155B). The support information output unit 150 (150B) predicts an emotional tendency from the current time onwards using the past emotional tendency, the emotional tendency at the current time, and the emotional factors, and outputs support information including advice based on the prediction result. Specifically, the support information output unit 150 (150B) predicts the emotional tendency from the current time onwards using the past emotional tendency estimated by the tendency estimation unit 130B and stored in the tendency storage unit 170 (170B), the emotional tendency at the current time estimated by the tendency estimation unit 130B, and the emotional factors estimated by the emotional factor estimation unit 155 (155B), and outputs support information including advice based on the prediction results to the output destination device 700.

[0057] After outputting the support information to the output destination device 700, the support device 100 (100B) ends the process and goes into standby mode ("End").

[0058] Next, an example of a trend estimation process according to the second embodiment of the present disclosure will be described. FIG. 9 is a flowchart showing an example of a trend estimation process in the support device 100 (100B) according to the second embodiment of the present disclosure. The tendency estimation section 130 (130B) of the support device 100 (100B) starts the processing shown in FIG. 9 when it acquires the emotion value as emotion information acquired by the data acquisition section 110 and the object identification information acquired by the object identification information acquisition section 113 (“Start”).

[0059] The tendency estimation unit 130 (130B) then executes a tendency estimation process for each tendency type (step ST2310). In the tendency estimation process, the tendency estimation unit 130 (130B) outputs the emotion tendency and the object identification information as estimation results to the support information output unit 150 and the tendency storage unit 170 (170B).

[0060] The tendency estimation unit 130 (130B) outputs the emotional tendency and the object identification information as the estimation result to the support information output unit 150 and the tendency storage unit 170 (170B), and then ends the processing shown in FIG. 9 (“End”).

[0061] Next, an example of the support information output process according to the second embodiment will be described. FIG. 10 is a flowchart showing an example of a support information output process in the support device 100 (100B) according to the second embodiment of the present disclosure. The support information output unit 150 (150B) in the support device 100 (100B) starts processing ("start") when it receives the current emotional tendency as an estimation result from the tendency estimation unit 130 (130B).

[0062] The support information output unit 150 (150B) then executes a trend prediction process (step ST2510). In the trend prediction process, the trend prediction unit 151 (151B) of the support information output unit 150 (150B) references the trend holding unit 170 (170B) to acquire the emotional trend estimated by the trend estimation unit 130 (130B). The trend prediction unit 151 (151B) predicts an emotional trend from the current time onwards based on the past emotional trend estimated by the trend estimation unit 130 (130B) and the emotional trend at the current time. The trend prediction unit 151 (151B) outputs the emotional trend that is the prediction result.

[0063] The support information output unit 150 (150B) then executes emotional factor estimation processing (step ST2520). In the emotional factor estimation processing, the emotional factor estimation unit 155 (155B) of the support information output unit 150 (150B) uses the emotional tendency to estimate emotional factors that may be the cause of the emotion shown in the emotional tendency. The emotional factor estimation unit 155 (155B) estimates each emotional factor using the past emotional tendency, the emotional tendency at the current time, and the emotional tendency after the current time, and outputs the emotional factors as the estimation results.

[0064] Here, if the support information output unit 150 (150B) includes a plan generation unit 152 described later, it may be configured to execute a plan generation process (step ST2530) described later. In the plan generation process, the plan generation unit 152 of the support information output unit 150 (150B) generates a plan from the current time onwards based on the emotional tendency. The plan generation unit 152 uses the emotional tendency of each past schedule to generate a plan according to the emotional tendency of each schedule from the current time onwards.

[0065] The support information output unit 150 (150B) then executes advice generation processing (step ST2540). In the advice generation processing, the advice generation unit 153 (153B) of the support information output unit 150 (150B) generates and outputs advice using the past emotional tendency, the emotional tendency at the current time, the emotional tendency after the current time, and the emotional factors.

[0066] The support information output unit 150 (150B) then generates and outputs support information using at least one of the past emotional tendency estimated by the tendency estimation unit 130 (130B), the emotional tendency at the current time estimated by the tendency estimation unit 130 (130B), the emotional tendency from the current time onwards predicted by the tendency prediction unit 151 (151B), the emotional factor estimated by the emotional factor estimation unit 155 (155B), or the advice generated by the advice generation unit 153 (153B).

[0067] The support information output unit 150 (150B) of the support device 100B then proceeds to an end determination process ((step ST2550) ("End?")). In the termination determination process, the support information output unit 150 (150B) determines whether to terminate the processing of the support information output unit 150 (150B). The support information output unit 150 (150B) determines whether to terminate the processing of the support information output unit 150 (150B) in accordance with an external command or an execution program. For example, when support information different from the support information that has been output is requested, the support information output unit 150 (150B) determines not to terminate the processing. If the support information output unit 150 (150B) determines not to end the processing of the support information output unit 150 (150B) ("NO" in step ST2550), the process proceeds to step ST2540 and executes the process of step ST2540. When the support information output unit 150 (150B) determines that the processing of the support information output unit 150 (150B) is to be ended ("YES" in step ST2550), the support information output unit 150 (150B) ends the processing ("End").

[0068] The configuration of this embodiment makes it possible to provide the following support. An example of the support information output by the support device 100B will be described. For example, if the support information shows the results of inferring causes from a week's emotional trends, it may include advice such as, "Your emotions were lower than average on Tuesday and Thursday this week," or "You may be feeling negative (depressed, sad) emotions about a specific event." A specific event here could be, for example, a subject you dislike, a lesson, or a menu. Furthermore, the configuration of this embodiment makes it possible to provide assistance to the user (operator) particularly regarding the emotional tendencies of children. For example, support information can provide feedback on communication. For example, if a child says, "I don't want to go to school," and the parent responds, "Go to school!" (Communication Method 1), it can be confirmed that the child's emotions have changed to a negative one. On the other hand, if the parent responds, "It's okay to take a break sometimes," (Communication Method 2), it can be confirmed that the child's emotions have improved to a positive one.

[0069] This embodiment further shows an example of an embodiment including the following configuration. [3] an emotional factor estimation unit that estimates an emotional factor based on the emotional tendency; Equipped with the support information output unit outputs support information including the emotion factor estimated by the emotion factor estimation unit. 1. A support device comprising: As a result, the present disclosure has an effect of providing an assistance device that enables assistance using factors of emotional tendency. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the above assistance method, or the above program.

[0070] This embodiment further shows an example of an embodiment including the following configuration. [4] The support information output unit predicting an emotional tendency from the current time onward using the past emotional tendency estimated by the tendency estimation unit, and generating and outputting support information using the predicted emotional tendency; 3. The support device according to claim 1 or 2, wherein: As a result, the present disclosure further has the effect of being able to provide a support device that makes it possible to enhance support regarding emotions from the current time onwards. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the above assistance method, or the above program.

[0071] This embodiment further shows an example of an embodiment including the following configuration. [5] The support information is The information includes one or more of the emotional tendency of the subject up to the current time, the emotional tendency of the subject at the current time, or the emotional tendency of the subject after the current time. 3. The support device according to claim 1 or 2, wherein: As a result, the present disclosure further has the effect of being able to provide a support device that enables enhanced support regarding changes in emotions from the past to the present time onward. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the above assistance method, or the above program.

[0072] Embodiment 3 Emotional tendencies may be influenced by the schedule of the subject, such as the season, day of the week, extracurricular activities, menu, long holidays, and events (concerts, sports days). In the third embodiment, a configuration example in which the emotional tendency of a subject is estimated taking into account the subject's schedule will be described. In embodiment 3, among the components of embodiment 3, those components that are similar to the components of embodiment 1 or embodiment 2 already described will be given the same names and similar symbols, and duplicate explanations will be omitted as appropriate.

[0073] Next, a configuration example of a support device according to a third embodiment of the present disclosure will be described. FIG. 11 is a diagram illustrating an example of the configuration of a support system 1 (1C) including a support device 100 (100C) according to the third embodiment of the present disclosure. The support system 1 (1C) includes a support device 100 (100C), an information source device 600 (600C), and an output destination device 700. The output destination device 700 has the same configuration as the output destination device 700 already described.

[0074] The information source device 600 (600C) has the same configuration as any of the already described information source devices 600. The information source device 600 (600C) may further have a configuration capable of outputting a schedule for each measurement subject.

[0075] The support device 100 (100C) outputs support information based on the emotional tendency of the subject taking into account the subject's schedule. The support device 100 (100C) includes a data acquisition unit 110 (110C), a tendency estimation unit 130 (130C), and a support information output unit 150 (150C).

[0076] Data acquisition section 110 (110C) is configured to include emotion information acquisition section 111, object identification information acquisition section 113, and schedule acquisition section 114. The data acquisition unit 110 (110C) is configured to newly include a schedule acquisition unit 114 in addition to the components already described.

[0077] The schedule acquisition unit 114 acquires the schedule of the subject to be measured. A schedule is information (schedule information) that is expressed as a combination of scheduled content and scheduled time. Scheduled content is information that indicates scheduled content, such as extracurricular activities, events (concerts, sports days), menus, long vacations, etc. Scheduled time is information about the scheduled time, such as the month, day of the week, date, time zone, and time (start time, end time).

[0078] The trend estimation unit 130 (130C) has the same function as any of the trend estimation units 130 already described. The tendency estimation unit 130 (130C) further estimates an emotional tendency for each schedule acquired by the schedule acquisition unit 114.

[0079] The trend holding unit 170 (170C) has the same function as the trend holding unit 170 already described. The tendency storage unit 170 (170C) further stores the emotional tendency for each schedule estimated by the tendency estimation unit 130C. Furthermore, the tendency holding unit 170 (170C) updates and holds the emotional tendency for each schedule, using the emotional tendency for each schedule estimated by the tendency estimation unit 130B and the emotional tendency for each schedule that has already been held. The tendency storage unit 170 (170C) causes the tendency estimation unit 130B or the support information output unit 150 (150C) to refer to the emotion tendency for each schedule stored therein. The tendency holding unit 170 (170C) may be configured outside the support device 100 (100C).

[0080] The support information output unit 150 (150C) has the same functions as any of the support information output units 150 already described. The support information output unit 150 (150C) further generates and outputs support information using the emotional tendency for each schedule. The support information output unit 150 (150C) includes a tendency prediction unit 151 (151C), a plan generation unit 152 (152C), an advice generation unit 153 (153C), and an emotional factor estimation unit 155 (155C).

[0081] The tendency prediction unit 151 (151C) predicts an emotional tendency from the current time onward using the emotional tendency at the current time and past emotional tendencies. The tendency prediction unit 151 (151C) predicts an emotional tendency from the current time onward using the emotional tendency at the current time and by referring to past emotional tendencies stored in the tendency storage unit 170 (170C).

[0082] The emotional factor estimation unit 155 (155C) estimates emotional factors based on the emotional tendency of the subject to be measured, similar to the already-described emotional factor estimation unit 155. The emotional factor estimation unit 155 (155C) estimates each emotional factor of the emotional tendency of the subject to be measured up to the current time, the emotional tendency of the subject to be measured at the current time, or the emotional tendency of the subject to be measured after the current time.

[0083] The plan generation unit 152 (152C) generates a plan from the current time onwards based on the emotional tendency. The plan generation unit 152 (152C) uses the emotional tendency for each past plan to generate a plan according to the emotional tendency for each plan from the current time onwards.

[0084] The advice generating unit 153 (153C) generates advice using emotional tendencies, similar to the advice generating unit 153 already described. The advice generating unit 153 (153C) generates advice using emotional tendencies for each past schedule, emotional tendencies at the current time, emotional tendencies for each schedule after the current time, and emotional factors for each schedule.

[0085] Next, an example of a part of the data acquisition process of the support device 100 (100C) according to the third embodiment of the present disclosure will be described. FIG. 12 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100C) according to the third embodiment of the present disclosure. The data acquiring unit 110 (110C) of the support device 100 (100C) further executes a schedule acquiring process in the data acquiring process (step ST3110). In the schedule acquiring process, the schedule acquiring unit 114 of the data acquiring unit 110 (110C) acquires the schedule of the subject to be measured. The schedule acquiring unit 114 acquires the schedule, for example, through an input operation by a user who is the operator of the support device 100 (100C). Alternatively, the schedule acquiring unit 114 acquires the schedule of the subject to be measured identified by the target identification information from an information source device 600 (600C) external to the support device 100 (100C), for example, using the target identification information acquired by the target identification information acquiring unit 113. The data acquiring unit 110 (110C) outputs the schedule of the subject to the tendency estimating unit 130 (130C).

[0086] Next, an example of a part of the trend estimation process of the support device 100 (100C) according to the third embodiment of the present disclosure will be described. FIG. 13 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100C) according to the third embodiment of the present disclosure. The tendency estimation unit 130 (130C) of the support device 100 (100C) further executes tendency estimation processing for each schedule in the tendency estimation processing (step ST3310). The tendency estimation unit 130 (130C) further estimates an emotional tendency for each schedule acquired by the schedule acquisition unit 114. The tendency estimation unit 130 (130C) outputs the estimation results to the support information output unit 150 (150C) and the tendency holding unit 170 (170C). For example, the tendency estimation unit 130 (130C) outputs the emotional tendency at the current time to the support information output unit 150 (150C) and outputs past emotional tendencies to the tendency holding unit 170 (170C).

[0087] Next, an example of the support information output process of the support device 100 (100C) according to the third embodiment of the present disclosure will be described. FIG. 14 is a flowchart showing an example of a support information output process of the support device 100 (100C) according to the third embodiment of the present disclosure. The support information output unit 150 (150C) of the support device 100 (100C) starts processing when it receives the schedule information and the current emotional tendency ("start").

[0088] The support information output unit 150 (150C) then executes trend prediction processing for each schedule (step ST3510). In the trend prediction processing, the trend prediction unit 151 (151C) of the support information output unit 150 (150C) references the trend holding unit 170 (170C) to acquire the emotional trend for each schedule estimated by the trend estimation unit 130 (130C). The trend prediction unit 151 (151C) predicts the emotional trend for each schedule from the current time onwards based on the past emotional trends estimated by the trend estimation unit 130 (130C) and the emotional trend at the current time. The trend prediction unit 151 (151C) outputs the emotional trend for each schedule, which is the prediction result.

[0089] The support information output unit 150 (150C) then executes emotional factor estimation processing (step ST3520). In the emotional factor estimation processing, the emotional factor estimation unit 155 (155C) of the support information output unit 150 (150C) estimates emotional factors that are likely to be the cause of the emotion shown in the emotional tendency using the emotional tendency. The emotional factor estimation unit 155 (155B) estimates each emotional factor using the past emotional tendency, the emotional tendency at the current time, and the emotional tendency after the current time, and outputs the emotional factors as the estimation results.

[0090] The support information output unit 150 (150C) then executes a plan generation process (step ST3530). In the plan generation process, the plan generation unit 152 (152C) of the support information output unit 150 (150C) generates a plan for the current time and thereafter based on the emotional tendency. The plan generation unit 152 (152C) uses the emotional tendency for each past schedule to generate a plan according to the emotional tendency for each schedule for the current time and thereafter. For example, if the emotional tendency indicates that a positive state will occur after the cram school schedule every Friday, the plan generation unit 152 (152C) generates a plan suitable for a positive state.

[0091] The support information output unit 150 (150C) then executes advice generation processing (step ST3540). In the advice generation processing, the advice generation unit 153 (153C) of the support information output unit 150 (150C) generates advice using the emotional tendency. The advice generation unit 153 (153C) generates advice using the emotional tendency for each past schedule, the emotional tendency at the current time, the emotional tendency for each schedule after the current time, and the emotional factor for each schedule.

[0092] The support information output unit 150 (150C) then generates and outputs support information using at least one of the past emotional tendency estimated by the tendency estimation unit 130 (130C), the emotional tendency at the current time estimated by the tendency estimation unit 130 (130C), the emotional tendency from the current time onwards predicted by the tendency prediction unit 151 (151C), the emotional factor estimated by the emotional factor estimation unit 155 (155C), the plan from the current time onwards generated by the plan generation unit 152 (152C), or the advice generated by the advice generation unit 153 (153C).

[0093] The support information output unit 150 (150C) of the support device 100C then proceeds to an end determination process ((step ST3550) ("end?")). In the termination determination process, the support information output unit 150 (150C) determines whether to terminate the processing of the support information output unit 150 (150C). The support information output unit 150 (150C) determines whether to terminate the processing of the support information output unit 150 (150C) in accordance with an external command or an execution program. For example, when support information different from the support information that has been output is requested, the support information output unit 150 (150C) determines not to terminate the processing. When the support information output unit 150 (150C) determines not to end the processing of the support information output unit 150 (150C) ("NO" in step ST3550), the process proceeds to step ST3540 and executes the process of step ST3540. When the support information output unit 150 (150C) determines that the processing of the support information output unit 150 (150C) is to be ended ("YES" in step ST3550), the support information output unit 150 (150C) ends the processing ("End").

[0094] Next, a specific example of the support information output by the support device 100 (100C) according to this embodiment will be described. FIG. 15 is a diagram illustrating an example of an emotional tendency and an example of an emotional tendency type according to the third embodiment of the present disclosure. Schedule 3010 indicates the main activities carried out on the relevant day in the past. Schedule 3020 indicates that the breakfast menu for the relevant date in the past was "pancakes." Appointment 3030 indicates that there is a "piano" lesson in the evening of the relevant date in the past. By taking schedules into account, it can be estimated that a person tends to feel depressed before piano lessons and happy after eating pancakes. This means that when a child and parent are at home, the system can output support information including advice such as, "I tend to feel depressed in front of the piano," or "Pancakes make the time more enjoyable, so how about having some for a 3 p.m. snack?" In addition to the above, if your child is out, the system can output support information including advice such as, "Your child is scheduled to leave school for cram school later," "Recently, you have been feeling positive (happy) after cram school," and "Now is the perfect time to ask your child to help with cleaning or other chores that they normally don't feel like doing!" In addition, if a child is left at home alone, the system can generate and output support information such as, "Your child is feeling sad and depressed more often, and your emotions are declining.", "You seem to be spending more time alone during the long vacation. How about some family activities on your days off?", or "https: / / / yuuenchi.enjoy" (by searching for URLs of recommended spots). In this way, by taking into account information (plans) that may affect emotions, such as extracurricular activities and menus, the accuracy of the analysis and the quality of advice can be improved.

[0095] This embodiment shows an example of an embodiment including the following configuration. [6] a schedule acquisition unit that acquires the schedule of the measurement subject; Equipped with The trend estimating unit includes: Estimating an emotional tendency for each schedule acquired by the schedule acquisition unit; The support information output unit Support information is generated and output using the emotional tendencies for each schedule. 1. A support device comprising: As a result, the present disclosure further has the effect of being able to provide a support device that takes into account schedules and enables enhanced support according to emotional tendencies for each schedule. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the above assistance method, or the above program.

[0096] Embodiment 4 Children's emotions can be expressed through facial expressions and gestures. Therefore, in the fourth embodiment, a configuration example in which the emotional tendency of the subject is estimated by taking into consideration the state of the subject will be described. In embodiment 4, among the components of embodiment 4, those components that are similar to the components of embodiment 1, embodiment 2, or embodiment 3 already described will be given the same names and similar symbols, and duplicate explanations will be omitted as appropriate.

[0097] An example of the configuration of a support system including a support device according to a fourth embodiment of the present disclosure will be described. FIG. 16 is a diagram illustrating an example of the configuration of a support system 1 (1D) including a support device 100 (100D) according to the fourth embodiment of the present disclosure. The support system 1 (1D) shown in Fig. 16 includes a support device 100 (100D), an information source device 600 (600D), and an output destination device 700. The output destination device 700 has the same configuration as the output destination device 700 already described.

[0098] The information source device 600 (600D) has the same configuration as any of the information source devices 600 already described. The information source device 600 (600D) may further include a device having a function of detecting the state of the subject. The function of detecting the state of the subject can be realized, for example, by using a known image recognition technology.

[0099] The support device 100 (100D) shown in FIG. 16 includes a data acquisition unit 110 (110D), a tendency estimation unit 130 (130D), a support information output unit 150 (150D), and a tendency storage unit 170 (170D).

[0100] The data acquiring unit 110 (110D) has a new function of acquiring the state of the subject to be measured in addition to the functions already described. The data acquiring unit 110 (110D) further includes a subject state acquiring unit 115.

[0101] The subject state acquiring unit 115 acquires subject state information indicating the state of the subject to be measured. The target state information acquired by the target state acquisition unit 115 indicates the state of the subject, such as facial expression, gesture, or both facial expression and gesture. The target state information may also include the state of the subject, such as attitude or behavior. The target state acquisition unit 115 receives a target state through an operation by an operator, who is a user, and acquires target state information. The target state information indicates, for example, a state such as energetic, happy, expressionless, awkward, looking away, angry, sad, fidgeting, playing with hands, biting nails, running around, or talking fast.

[0102] The trend estimation unit 130 (130D) may be configured in the same manner as any of the trend estimation units 130 already described.

[0103] The tendency storage unit 170 (170D) stores past emotion tendencies estimated by the tendency estimation unit 130D. Furthermore, the tendency holding unit 170 (170D) updates and holds the emotional tendency using the emotional tendency estimated by the tendency estimation unit 130D and the emotional tendency that has already been held. The tendency storage unit 170 (170D) causes the tendency estimation unit 130D or the support information output unit 150 (150D) to refer to the emotion tendency stored therein. The tendency holding unit 170 (170D) may be configured outside the support device 100 (100D).

[0104] The support information output unit 150 (150D) has the same functions as any of the support information output units 150 already described. The support information output unit 150 (150D) further has a function of outputting support information that takes into account the target state indicated in the target state information acquired by the target state acquisition unit 115. The support information output unit 150 (150D) includes a tendency prediction unit 151 (151D), a plan generation unit 152 (152D), an advice generation unit 153 (153D), and an emotion factor estimation unit 155 (155D).

[0105] The trend prediction unit 151 (151D) of the support information output unit 150 (150D) has the same function as any of the trend prediction units 151 already described. Furthermore, the tendency prediction unit 151 (151D) of the support information output unit 150 (150D) further predicts the emotional tendency from the current time onwards based on the state of the measured subject indicated in the target state information acquired by the target state acquisition unit 115, the past emotional tendency estimated by the tendency estimation unit 130 (130D), and the emotional tendency at the current time. The support information output unit 150 (150D) can generate and output support information using the emotional tendency from the current time onwards, which is the prediction result of the tendency prediction unit 151 (151D).

[0106] The emotional factor estimation section 155 (155D) of the support information output section 150 (150D) estimates emotional factors based on the emotional tendency of the subject to be measured, similar to the already-described emotional factor estimation section 155. The emotional factor estimation section 155 (155D) estimates each emotional factor of the emotional tendency of the subject to be measured up to the current time, the emotional tendency of the subject to be measured at the current time, or the emotional tendency of the subject to be measured after the current time. The support information output unit 150 (150D) can generate and output support information using the emotion factors that are the estimation results of the emotion factor estimation unit 155 (155D).

[0107] The plan generation unit 152 (152D) of the support information output unit 150 (150D) generates a plan for the current time and thereafter based on the emotional tendency. The plan generation unit 152 (152D) generates a plan according to the emotional tendency for each schedule from the current time onward, using the emotional tendency for each past schedule. The support information output unit 150 (150D) can generate and output support information using the plan generated by the plan generation unit 152 (152D).

[0108] The advice generating unit 153 (153D) of the support information output unit 150 (150D) generates advice using emotional tendencies, similar to the advice generating unit 153 already described. The advice generating unit 153 (153D) generates advice using the emotional tendencies of each past schedule, the emotional tendencies at the current time, the emotional tendencies of each schedule after the current time, and the emotional factors of each schedule. The advice generating unit 153 (153D) further generates advice according to the target state indicated in the target state information acquired by the target state acquiring unit 115. The support information output unit 150 (150D) can generate and output support information using the advice generated by the advice generation unit 153 (153D).

[0109] The support information output unit 150 (150D) generates and outputs support information using at least one of the past emotional tendency estimated by the tendency estimation unit 130 (130D), the emotional tendency at the current time estimated by the tendency estimation unit 130 (130D), the emotional tendency from the current time onwards predicted by the tendency prediction unit 151 (151D), the emotional factor estimated by the emotional factor estimation unit 155 (155D), the plan from the current time onwards generated by the plan generation unit 152 (152D), or the advice generated by the advice generation unit 153 (153D).

[0110] Next, a processing example of the support device according to the fourth embodiment of the present disclosure will be described. FIG. 17 is a diagram illustrating an example of a means for receiving the state of the subject in the support device 100 (100D) according to the fourth embodiment of the present disclosure. FIG. 18 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100D) according to the fourth embodiment of the present disclosure. The data acquiring unit 110 (110D) executes a target state acquiring process in the data acquiring process (step ST4110). In the target state acquiring process, the target state acquiring unit 115 of the data acquiring unit 110 (110D) generates and acquires target state information by accepting a user's selection operation on a target state accepting image 4010 on a mobile terminal (smartphone) 4000, for example, as shown in FIG. 18. The data acquiring unit 110 (110D) outputs the target state information to the tendency estimating unit 130 (130D) and the support information output unit 150 (150D).

[0111] Next, an example of support information output processing in the support device 100 (100D) according to the fourth embodiment of the present disclosure will be described. FIG. 19 is a flowchart showing an example of a support information output process in the support device 100 (100D) according to the fourth embodiment of the present disclosure. The support information output unit 150 (150D) starts processing when it receives the target state information and the current emotional tendency ("start").

[0112] The support information output unit 150 (150D) then executes a trend prediction process using the state of the subject to be measured (step ST4510). In the trend prediction process, the trend prediction unit 151 (151D) of the support information output unit 150 (150D) acquires the subject state information acquired by the subject state acquisition unit 115 and acquires the past emotional tendency estimated by the trend estimation unit 130 (130D) by referring to the trend holding unit 170 (170D). The trend prediction unit 151 (151D) predicts the emotional tendency from the current time onwards based on the state of the subject to be measured indicated in the subject state information acquired by the subject state acquisition unit 115, the past emotional tendency estimated by the trend estimation unit 130 (130D), and the emotional tendency at the current time. The trend prediction unit 151 (151D) outputs the emotional tendency as the prediction result.

[0113] The support information output unit 150 (150D) then executes emotional factor estimation processing using the measurement target state (step ST4520). In the emotional factor estimation processing, the emotional factor estimation unit 155 (155D) of the support information output unit 150 (150D) estimates emotional factors that are likely to be the cause of the emotion shown in the emotional tendency using the emotional tendency. The emotional factor estimation unit 155 (155D) estimates each emotional factor using the past emotional tendency, the emotional tendency at the current time, and the emotional tendency after the current time, and outputs the emotional factors as the estimation results.

[0114] The support information output unit 150 (150D) then executes a plan generation process using the measurement target state (step ST4530). In the plan generation process, the plan generation unit 152 (152D) of the support information output unit 150 (150D) generates a plan for the current time and after using the past emotional tendency, the emotional tendency for the current time and after, and the emotional factors.

[0115] The support information output unit 150 (150D) executes advice generation processing using the state of the object to be measured (step ST4540). In the advice generation processing, the advice generation unit 153 (153D) of the support information output unit 150 (150D) generates and outputs advice using the past emotional tendency, the emotional tendency at the current time, the emotional tendency after the current time, the emotional factors, and the plan after the current time.

[0116] The support information output unit 150 (150D) then generates and outputs support information using at least one of the past emotional tendency estimated by the tendency estimation unit 130 (130D), the emotional tendency at the current time estimated by the tendency estimation unit 130 (130D), the emotional tendency from the current time onwards predicted by the tendency prediction unit 151 (151D), the emotional factor estimated by the emotional factor estimation unit 155 (155D), the plan from the current time onwards generated by the plan generation unit 152 (152D), or the advice generated by the advice generation unit 153 (153D).

[0117] The support information output unit 150 (150D) of the support device 100D then proceeds to an end determination process ((step ST4550) ("End?")). In the termination determination process, the support information output unit 150 (150D) determines whether to terminate the processing of the support information output unit 150 (150D). The support information output unit 150 (150D) determines whether to terminate the processing of the support information output unit 150 (150D) in accordance with an external command or an execution program. For example, when support information different from the support information that has been output is requested, the support information output unit 150 (150D) determines not to terminate the processing. If the support information output unit 150 (150D) determines not to end the processing of the support information output unit 150 (150D) ("NO" in step ST4550), the process proceeds to step ST4540 and executes the process of step ST4540. When the support information output unit 150 (150D) determines that the processing of the support information output unit 150 (150D) is to be ended ("YES" in step ST4550), the support information output unit 150 (150D) ends the processing ("End").

[0118] Next, a specific example of the support information output by the support device 100 (100D) according to this embodiment will be described. When a child and parent are at home and the parent inputs information about the child's condition, who is the subject of measurement, support information can be generated that includes advice such as, "There is a discrepancy between the input data and the actual emotional data," or "Your child is the type who does not easily express his or her emotions, so as a parent you may sometimes end up exaggerating, but deep down he or she appears to be feeling sad." In addition, when a child is out and a parent requests support from the support device 100 (100D) regarding the child's recent emotions, support information can be generated that includes advice such as, "Your child has been feeling sad a lot recently, and his / her emotions have been declining," "Since your child looked away when leaving the house, we predict that he / she is feeling sad outside," and "Let's make your child's favorite menu for dinner tonight." In addition, if a child is left at home and the parent requests support from the support device 100 (100D) regarding the child's emotions while away from home, support information can be generated that includes advice such as, "Three days ago, when your emotions were negative (depressed), 'irritation' was selected from your actual facial expressions and gestures." or "Your emotions are still negative (depressed) now, so you may be irritated." In this way, by combining the facial expressions and gestures entered with emotional tendencies, the accuracy of emotional predictions when the robot is left alone can be improved, and advice can be given to prevent emotions from becoming negative.

[0119] This embodiment further shows an example of an embodiment including the following configuration. [7] a subject state acquisition unit that acquires subject state information indicating the state of the subject; Equipped with The support information output unit predicting an emotional tendency from the current time onwards based on the state of the subject to be measured indicated in the subject state information acquired by the subject state acquisition unit, the past emotional tendency estimated by the tendency estimation unit, and the emotional tendency at the current time; 1. A support device comprising: As a result, the present disclosure further has the effect of being able to provide an assistance device that makes it possible to enhance assistance regarding emotional tendencies that take into account the state of the person to be measured. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the above assistance method, or the above program.

[0120] This embodiment further shows an example of an embodiment including the following configuration. [8] The target state information acquired by the target state acquisition unit includes facial expressions, gestures, or facial expressions and gestures of the measurement subject. 1. A support device comprising: As a result, the present disclosure further has the effect of providing a support device that makes it possible to improve the accuracy of estimating a child's emotional tendency. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the above assistance method, or the above program.

[0121] Embodiment 5. Here, the emotional tendencies we have discussed so far may be closely related to sleep. Therefore, in the fifth embodiment, a configuration example of estimating an emotional tendency taking into account the degree of drowsiness or the sleeping time will be described. In embodiment 5, among the components of embodiment 5, those components that are similar to the components of embodiment 1, embodiment 2, embodiment 3, or embodiment 4 already described will be given the same names and similar symbols, and duplicate explanations will be omitted as appropriate.

[0122] An example of the configuration of a support system 1 (1E) including a support device according to a fifth embodiment of the present disclosure will be described. FIG. 20 is a diagram illustrating an example of the configuration of a support system 1 (1E) including a support device 100 (100E) according to the fifth embodiment of the present disclosure. The support system 1 (1E) shown in FIG. 20 includes a support device 100 (100E), an information source device 600 (600E), and an output destination device 700. The destination device 700 is configured in the same manner as the destination device 700 already described.

[0123] The information source device 600 (600E) is a device that further has a function of outputting the sleepiness level of the subject and a function of outputting the sleeping time of the subject. Note that information source device 600 (600E) may be configured to have the same functions as any of the information source devices 600 already described.

[0124] The support device 100 (100E) has the same functions as the already-described support device 100. The support device 100 (100E) further has a function of estimating emotional tendencies by taking into account drowsiness and sleep. The support device 100 (100E) shown in FIG. 20 includes a data acquisition unit 110 (110E), a tendency estimation unit 130 (130E), and a support information output unit 150 (150E).

[0125] The data acquiring section 110 (110E) shown in FIG. 20 includes an emotion information acquiring section 111, an object identification information acquiring section 113, a sleepiness level acquiring section 116, and a sleeping time acquiring section 117. Emotion information acquisition section 111 has the same functions as emotion information acquisition section 111 already described. The object identification information acquisition unit 113 has the same functions as the object identification information acquisition unit 113 already described.

[0126] The sleepiness level acquiring unit 116 acquires the sleepiness level of the measurement subject in time series. The drowsiness level acquisition unit 116 acquires the drowsiness level output by the information source device 600 (600E). In the case where the support device 100E includes the biological information acquisition unit 112 already described, the drowsiness level acquisition unit 116 may be configured to acquire the drowsiness level from the biological information acquisition unit 112.

[0127] The sleeping time acquisition unit 117 acquires the sleeping time of the measurement subject. The sleeping time acquisition unit 117 acquires the sleeping time output by the information source device 600 (600E). Alternatively, the sleeping time obtaining unit 117 may be configured to obtain the sleeping time of the measurement subject through an input operation by the user who is the operator.

[0128] The tendency estimation unit 130 (130E) further estimates the emotional tendency by taking into account the sleepiness tendency and the sleeping time tendency. The tendency estimation unit 130 (130E) shown in FIG. 20 includes a sleepiness tendency estimation unit 131 (131E) and a sleeping time tendency estimation unit 132 (132E).

[0129] The drowsiness tendency estimation unit 131 (131E) estimates the drowsiness tendency using the time-series drowsiness level acquired by the drowsiness level acquisition unit 116. The drowsiness tendency estimation unit 131 (131E) estimates the tendency for each tendency type in the same manner as the tendency estimation process by the tendency estimation unit 130 already described.

[0130] The sleep time tendency estimation unit 132 (132E) estimates the tendency of sleep time using the time-series sleep time acquired by the sleep time acquisition unit 117.

[0131] The tendency estimation unit 130 (130E) further uses the drowsiness tendency estimated by the drowsiness tendency estimation unit 131 (131E) and the sleep time tendency estimated by the sleep time tendency estimation unit 132 (132E) to estimate an emotional tendency that combines the drowsiness tendency, the sleep time tendency, and the emotional tendency.

[0132] The support information output unit 150 (150E) has the same functions as any of the support information output units 150 already described. The support information output unit 150 (150E) shown in FIG. 20 further generates and outputs support information using the sleepiness tendency, the sleeping time tendency, and the emotional tendency estimated by the tendency estimation unit 130 (130E).

[0133] The tendency prediction unit 151 (151E) of the support information output unit 150 (150E) has the same function as any of the already-described tendency prediction units 151. The tendency prediction unit 151 (151E) can further predict an emotional tendency using a sleepiness tendency, a sleeping time tendency, or a sleepiness tendency and a sleeping time tendency.

[0134] The emotional factor estimation unit 155 (155E) of the support information output unit 150 (150E) has the same function as any of the already-described tendency prediction units 151. The emotional factor estimation unit 155 (155E) can further estimate an emotional factor using a sleepiness tendency, a sleeping time tendency, or a sleepiness tendency and a sleeping time tendency.

[0135] The plan generation unit 152 (152E) of the support information output unit 150 (150E) has the same function as any of the already-described tendency prediction units 151. The plan generation unit 152 (152E) can further generate a plan using drowsiness tendency, sleeping time tendency, or drowsiness tendency and sleeping time tendency.

[0136] The advice generating unit 153 (153E) of the support information output unit 150 (150E) has the same function as any of the already-described tendency predicting units 151. The advice generating unit 153 (153E) can further generate advice using drowsiness tendency, sleeping time tendency, or drowsiness tendency and sleeping time tendency.

[0137] The tendency holding section 170 (170E), like any of the tendency holding sections 170 already described, holds the past emotion tendencies estimated by the tendency estimation section 130E. Furthermore, the tendency holding unit 170 (170E) updates and holds the emotional tendency using the emotional tendency estimated by the tendency estimation unit 130E and the emotional tendency that has already been held. The tendency storage unit 170 (170E) causes the tendency estimation unit 130D or the support information output unit 150 (150E) to refer to the emotion tendency stored therein. The tendency holding unit 170 (170E) may be configured outside the support device 100 (100E).

[0138] Next, a processing example of the support device according to the fifth embodiment of the present disclosure will be described. FIG. 21A is a diagram illustrating an example of a means for accepting a subject's sleeping time in the support device 100 (100E) according to the fifth embodiment of the present disclosure, and FIG. 21B is a diagram illustrating an example of a means for indicating a transition in the sleeping time. FIG. 22 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100E) according to the fifth embodiment of the present disclosure. The data acquiring unit 110 (110E) of the support device 100 (100E) starts processing upon receiving the drowsiness level, and then executes a drowsiness level acquisition process (step ST5110). In the drowsiness level acquisition process, the drowsiness level acquiring unit 116 of the data acquiring unit 110 (110E) acquires the drowsiness level from, for example, the biological information used to calculate the emotion value. Alternatively, the drowsiness level acquiring unit 116 acquires, for example, the drowsiness level output by the external information source device 600 (600E). The data acquiring unit 110 (110E) outputs the sleepiness level acquired by the sleepiness level acquiring unit 116 to the tendency estimating unit 130 (130E) in chronological order.

[0139] The data acquiring unit 110 (110E) then executes a sleep time acquisition process (step ST5120). In the sleep time acquisition process, the sleep time acquiring unit 117 of the data acquiring unit 110 (110E) acquires the sleep time by, for example, an operator performing an input operation on an image "Today's Sleep Time" 5010 displayed on the mobile terminal (smartphone) 5000 shown in FIG. 21A. Alternatively, the sleep time may be acquired by an application on the mobile terminal (smartphone) 5000. The acquired sleep time is displayed so that the sleep time trend can be seen, for example, as in the image "Weekly Sleep Time" 5020 shown in FIG. 21B. The data acquiring unit 110 (110E) outputs the sleeping time acquired by the sleeping time acquiring unit 117 to the tendency estimating unit 130 (130E).

[0140] Next, an example of a part of the trend estimation process of the support device 100 (100E) will be described. FIG. 23 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100E) according to the fifth embodiment of the present disclosure. The tendency estimation unit 130 (130E) of the support device 100 (100E) executes a drowsiness tendency estimation process in the tendency estimation process (step ST5310). In the drowsiness tendency estimation process, the drowsiness tendency estimation unit 131 (131E) of the tendency estimation unit 130 (130E) estimates a drowsiness tendency using the time-series drowsiness degree acquired by the drowsiness degree acquisition unit 116. The drowsiness tendency estimation unit 131 (131E) estimates a drowsiness tendency for each tendency type using the time-series drowsiness degree.

[0141] The tendency estimation unit 130 (130E) then executes a sleep time tendency estimation process (step ST5320). In the sleep time tendency estimation process, the sleep time tendency estimation unit 132 (132E) of the tendency estimation unit 130 (130E) estimates a sleep time tendency using the sleep time acquired by the sleep time acquisition unit 117.

[0142] The tendency estimation unit 130 (130E) further uses the drowsiness tendency estimated by the drowsiness tendency estimation unit 131 (131E) and the sleep time tendency estimated by the sleep time tendency estimation unit 132 (132E) to combine the drowsiness tendency, the sleep time tendency, and the emotional tendency to estimate the emotional tendency.

[0143] Next, an example of the support information output process in the support device 100 (100E) will be described. FIG. 24 is a flowchart showing an example of a support information output process in the support device 100 (100E) according to the fifth embodiment of the present disclosure. The support information output unit 150 (150E) of the support device 100 (100E) starts processing when it receives the current emotional tendency ("start").

[0144] The support information output unit 150 (150E) then executes a trend prediction process that takes into account the drowsiness trend and the sleeping time trend (step ST5510). In the trend prediction process, the trend prediction unit 151 (151E) of the support information output unit 150 (150E) predicts an emotional trend from the current time onwards based on the emotional trend to which the sleep tendency and sleeping time trend have been taken into account by the trend estimation unit 130 (130E). The trend prediction unit 151 (151E) refers to the trend storage unit 170 (170E) to acquire the past emotional trend estimated by the trend estimation unit 130 (130E). The trend prediction unit 151 (151E) predicts an emotional trend from the current time onwards based on the past emotional trend estimated by the trend estimation unit 130 (130E) and the emotional trend at the current time. The trend prediction unit 151 (151E) outputs the emotional trend that is the prediction result.

[0145] The support information output unit 150 (150E) then executes emotional factor estimation processing that takes into account the sleep tendency and sleeping time (step ST5520). In the emotional factor estimation processing, the emotional factor estimation unit 155 (155E) of the support information output unit 150 (150E) estimates emotional factors that are likely to be the cause of the emotion shown in the emotional tendency based on the emotional tendency to which the sleep tendency and sleeping time have been added by the tendency estimation unit 130 (130E). The emotional factor estimation unit 155 (155E) estimates each emotional factor using the past emotional tendency estimated by the tendency estimation unit 130 (130E), the emotional tendency at the current time, and the emotional tendency from the current time onwards predicted by the tendency prediction unit 151 (151E), and outputs the emotional factors as the estimation results.

[0146] The support information output unit 150 (150E) then executes a plan generation process that takes into account the sleep tendency and sleeping time (step ST5530). In the plan generation process, the plan generation unit 152 (152E) of the support information output unit 150 (150E) generates a plan for the current time and beyond based on the emotional tendency to which the sleep tendency and sleeping time have been taken into account by the tendency estimation unit 130 (130E). The plan generation unit 152 (152E) generates a plan for the current time and beyond using the past emotional tendency estimated by the tendency estimation unit 130 (130E), the emotional tendency at the current time, the emotional tendency for the current time and beyond predicted by the tendency prediction unit 151 (151E), and the emotional factors estimated by the emotional factor estimation unit 155 (155E).

[0147] The support information output unit 150 (150E) then executes advice generation processing that takes into account the sleep tendency and sleeping duration (step ST5540). In the advice generation processing, the advice generation unit 153 (153E) of the support information output unit 150 (150E) generates advice based on the emotional tendency to which the sleep tendency and sleeping duration have been taken into account by the tendency estimation unit 130 (130E). The advice generation unit 153 (153E) generates and outputs advice using the past emotional tendency estimated by the tendency estimation unit 130 (130E), the emotional tendency at the current time, the emotional tendency from the current time onwards predicted by the tendency prediction unit 151 (151E), the emotional factors estimated by the emotional factor estimation unit 155 (155E), and the plan generated by the plan generation unit 152 (152E).

[0148] The support information output unit 150 (150E) then generates and outputs support information using at least one of the past emotional tendency estimated by the tendency estimation unit 130 (130E), the emotional tendency at the current time estimated by the tendency estimation unit 130 (130E), the emotional tendency from the current time onwards predicted by the tendency prediction unit 151 (151E), the emotional factor estimated by the emotional factor estimation unit 155 (155E), the plan from the current time onwards generated by the plan generation unit 152 (152E), or the advice generated by the advice generation unit 153 (153E).

[0149] The support information output unit 150 (150E) of the support device 100E then proceeds to an end determination process ((step ST5550) ("End?")). In the termination determination process, the support information output unit 150 (150E) determines whether to terminate the processing of the support information output unit 150 (150E). The support information output unit 150 (150E) determines whether to terminate the processing of the support information output unit 150 (150E) in accordance with an external command or an execution program. For example, when support information different from the support information that has been output is requested, the support information output unit 150 (150E) determines not to terminate the processing. When the support information output unit 150 (150E) determines not to end the processing of the support information output unit 150 (150E) ("NO" in step ST5550), the process proceeds to step ST5540 and executes the processing of step ST5540. When the support information output unit 150 (150E) determines that the processing of the support information output unit 150 (150E) is to be ended (step ST5550 "YES"), the support information output unit 150 (150E) ends the processing ("end").

[0150] Next, a specific example of the support information output by the support device 100 (100E) according to this embodiment will be described. The assistance device 100 (100E) can provide assistance regarding emotions that takes sleep into consideration by clarifying the relationship between emotions and sleep and performing trend analysis. Specifically, the system can output support information including advice such as, "You are feeling depressed and irritable more and more often," "Also, you haven't had enough sleep for the past three days and are feeling sleepy, so it may be because of lack of sleep," and "Take a nice, long bath and get ready for bed early." In addition, specifically, it can output support information including advice such as, "You tend to go to bed later on weekends," and "By maintaining a regular lifestyle rhythm so that you don't have negative feelings on Mondays when you go to school, you may be able to reduce your feeling of uneasiness (sleepiness)." In this way, by visualizing the relationship between sleep and fatigue or irritability, children can be encouraged to take action to improve their condition.

[0151] This embodiment further shows an example of an embodiment including the following configuration. [9] a drowsiness level acquisition unit that acquires the drowsiness level of the subject in time series; a drowsiness tendency estimation unit that estimates drowsiness tendency using the time-series drowsiness degree acquired by the drowsiness degree acquisition unit; a sleep time acquisition unit that acquires the sleep time of the measurement subject; a sleep time tendency estimation unit that estimates a sleep time tendency using the time-series sleep time acquired by the sleep time acquisition unit; Equipped with The support information output unit generating and outputting support information using the sleepiness tendency, the sleeping time tendency, and the emotional tendency estimated by the tendency estimation unit; 1. A support device comprising: As a result, the present disclosure further has the effect of being able to provide an assistance device that enables enhanced assistance based on the relationship between emotional tendencies, drowsiness, and sleep. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the above assistance method, or the above program.

[0152] Embodiment 6 The emotional tendency of the subject may be related to the environmental conditions surrounding the subject. Therefore, in the sixth embodiment, a configuration example of estimating an emotional tendency taking into account the surrounding environment of the subject will be described. In the sixth embodiment, among the components of the sixth embodiment, those components and their functions that are similar to those of the components of the first, second, third, fourth, or fifth embodiment already described are indicated by the same component names and the same symbols, and redundant explanations of those components are omitted as appropriate.

[0153] A configuration example of a support device and a system including the support device according to a sixth embodiment of the present disclosure will be described. FIG. 25 is a diagram illustrating an example of the configuration of a support system 1 (1F) including a support device 100 (100F) according to the sixth embodiment of the present disclosure. 25 includes a support system 1 (1F) including a support device 100 (100F), an information source device 600 (600F), and a destination device 700. The destination device 700 functions in the same manner as the destination device 700 already described.

[0154] The information source device 600 (600F) further outputs subject surroundings information relating to the surroundings of the subject. The information source device 600 (600F) is configured to have the same functions as the information source device 600 already described.

[0155] The support device 100 (100F) shown in FIG. 25 includes a data acquisition unit 110 (110F), a tendency estimation unit 130 (130F), a support information output unit 150 (150F), and a tendency holding unit 170 (170F).

[0156] The data acquisition unit 110 (110F) is configured to have the same functions as the data acquisition unit 110 already described. The data acquisition unit 110 (110F) further acquires target surroundings information. The data acquisition unit 110 (110F) includes a target surroundings information acquisition unit 118. The subject surroundings information acquisition unit 118 acquires subject surroundings information indicating the environmental conditions of the subject to be measured. The subject surroundings information is information about the environment and weather around the subject. The environment includes, for example, information on lighting, air flow, indoor temperature, indoor humidity, etc. The weather includes, for example, information on air pressure, outdoor temperature, outdoor humidity, and weather.

[0157] The trend estimation unit 130 (130F) has the functions of the trend estimation unit 130 already described. The tendency estimation unit 130 (130F) shown in FIG. 25 further estimates an emotional tendency for each environmental situation indicated in the object surrounding information acquired by the object surrounding information acquisition unit 118.

[0158] The support information output unit 150 (150F) has the same functions as the support information output unit 150 already described. The support information output unit 150 (150F) further generates and outputs support information using the emotional tendency for each environmental situation estimated by the tendency estimation unit 130 (130F). The support information output unit 150 (150F) shown in FIG. 25 includes a tendency prediction unit 151 (151F), a plan generation unit 152 (152F), an advice generation unit 153 (153F), and an emotional factor estimation unit 155 (155F).

[0159] The tendency prediction unit 151 (151F) of the support information output unit 150 (150F) has the same function as any of the already-described tendency prediction units 151. The tendency prediction unit 151 (151E) further has a function of predicting an emotional tendency from the current time onward by using a past emotional tendency that takes into account the environmental situation.

[0160] The emotion factor estimation unit 155 (155F) of the support information output unit 150 (150F) It has the same functions as any of the already-described tendency prediction units 151. The emotion factor estimation unit 155 (155E) further has a function of estimating an emotion factor using an emotion tendency that takes into account the environmental situation.

[0161] The plan generation unit 152 (152F) of the support information output unit 150 (150F) It has the same functions as any of the already-described tendency prediction units 151. The plan generation unit 152 (152E) further has a function of generating a plan using an emotional tendency that takes into account the environmental situation.

[0162] The advice generation unit 153 (153F) of the support information output unit 150 (150F) The advice generating unit 153 (153E) has the same function as any of the already-described tendency predicting units 151. The advice generating unit 153 (153E) further has a function of generating advice using an emotional tendency that takes into account the environmental situation.

[0163] The trend holding unit 170 (170F) has the same function as the trend holding unit 170 already described. The tendency storage unit 170 (170F) further stores the emotion tendency for each environmental situation estimated by the tendency estimation unit 130 (130F). Furthermore, the tendency holding unit 170 (170F) further updates and holds the emotional tendency for each environmental situation by using the emotional tendency for each environmental situation estimated by the tendency estimation unit 130 (130F) and the emotional tendency for each environmental situation that has already been held.

[0164] Next, a processing example of the support device according to the sixth embodiment of the present disclosure will be described. FIG. 26 is a flowchart showing an example of a part of the data acquisition process of the support device 100 (100F) according to the sixth embodiment of the present disclosure. The data acquiring unit 110 (110F) of the support device 100 (100F) further executes a target surrounding information acquiring process in the data acquiring process (step ST6110). In the target surrounding information acquiring process, the target surrounding information acquiring unit 118 of the data acquiring unit 110 (110F) acquires target surrounding information indicating the environmental condition of the measured subject. The target surrounding information acquiring unit 118 acquires the target surrounding information from, for example, the information source device 600 (600F).

[0165] Next, an example of a part of the trend estimation process of the support device 100 (100F) according to the sixth embodiment of the present disclosure will be described. FIG. 27 is a flowchart showing an example of a part of the trend estimation process of the support device 100 (100F) according to the sixth embodiment of the present disclosure. The tendency estimation unit 130 (130F) further executes the tendency estimation process taking into account the target surrounding information in the tendency estimation process (step ST6310). In the tendency estimation process, the tendency estimation unit 130 (130F) estimates an emotional tendency for each environmental situation indicated in the target surrounding information acquired by the target surrounding information acquisition unit 118.

[0166] Next, an example of support information output processing in the support device 100 (100F) according to the sixth embodiment of the present disclosure will be described. FIG. 28 is a flowchart showing an example of a support information output process in the support device 100 (100F) according to the sixth embodiment of the present disclosure. The support information output unit 150 (150F) of the support device 100 (100F) starts processing when it receives the emotional tendency estimated by the tendency estimation unit 130 (130F). ("Start")

[0167] The support information output unit 150 (150F) then executes a trend prediction process that takes into account the target peripheral information (step ST6510). In the trend prediction process, the trend prediction unit 151 (151F) of the support information output unit 150 (150F) predicts an emotional trend from the current time onwards based on the emotional trend to which the target peripheral information has been added by the trend estimation unit 130 (130F). The trend prediction unit 151 (151F) acquires past emotional trends estimated by the trend estimation unit 130 (130F) by referring to the trend storage unit 170 (170F). The trend prediction unit 151 (151F) predicts an emotional trend from the current time onwards based on the past emotional trends estimated by the trend estimation unit 130 (130F) and the emotional trend at the current time. The trend prediction unit 151 (151F) outputs the emotional trend that is the prediction result.

[0168] The support information output unit 150 (150F) then executes emotional factor estimation processing that takes into account the object peripheral information (step ST6520). In the emotional factor estimation processing, the emotional factor estimation unit 155 (155F) of the support information output unit 150 (150F) estimates emotional factors that are likely to be the cause of the emotions shown in the emotional tendency based on the emotional tendency to which the object peripheral information has been added by the tendency estimation unit 130 (130F). The emotional factor estimation unit 155 (155F) estimates each emotional factor using the past emotional tendency estimated by the tendency estimation unit 130 (130F), the emotional tendency at the current time, and the emotional tendency from the current time onwards predicted by the tendency prediction unit 151 (151F), and outputs the emotional factors as the estimation results.

[0169] The support information output unit 150 (150F) then executes a plan generation process that takes into account the target peripheral information (step ST6530). In the plan generation process, the plan generation unit 152 (152F) of the support information output unit 150 (150F) generates a plan for the current time and beyond based on the emotional tendency to which the target peripheral information has been added by the tendency estimation unit 130 (130F). The plan generation unit 152 (152F) generates a plan for the current time and beyond using the past emotional tendency estimated by the tendency estimation unit 130 (130F), the emotional tendency at the current time, the emotional tendency for the current time and beyond predicted by the tendency prediction unit 151 (151F), and the emotional factors estimated by the emotional factor estimation unit 155 (155F).

[0170] The support information output unit 150 (150F) then executes advice generation processing that takes into account the target peripheral information (step ST6540). In the advice generation processing, the advice generation unit 153 (153F) of the support information output unit 150 (150F) generates advice based on the emotional tendency to which the target peripheral information has been added by the tendency estimation unit 130 (130F). The advice generation unit 153 (153F) generates and outputs advice using the past emotional tendency estimated by the tendency estimation unit 130 (130F), the emotional tendency at the current time, the emotional tendency from the current time onwards predicted by the tendency prediction unit 151 (151F), the emotional factors estimated by the emotional factor estimation unit 155 (155F), and the plan generated by the plan generation unit 152 (152F).

[0171] The support information output unit 150 (150F) then generates and outputs support information using at least one of the past emotional tendency estimated by the tendency estimation unit 130 (130F), the emotional tendency at the current time estimated by the tendency estimation unit 130 (130F), the emotional tendency from the current time onwards predicted by the tendency prediction unit 151 (151F), the emotional factor estimated by the emotional factor estimation unit 155 (155F), the plan from the current time onwards generated by the plan generation unit 152 (152F), or the advice generated by the advice generation unit 153 (153F).

[0172] The support information output unit 150 (150F) of the support device 100F then proceeds to an end determination process ((step ST6550) ("End?")). In the termination determination process, the support information output unit 150 (150E) determines whether to terminate the processing of the support information output unit 150 (150F). The support information output unit 150 (150E) determines whether to terminate the processing of the support information output unit 150 (150F) in accordance with an external command or an execution program. For example, when support information different from the support information that has been output is requested, the support information output unit 150 (150F) determines not to terminate the processing. When the support information output unit 150 (150F) determines not to end the processing of the support information output unit 150 (150F) ("NO" in step ST6550), the process proceeds to step ST6540 and executes the process of step ST6540. When the support information output unit 150 (150F) determines that the processing of the support information output unit 150 (150F) is to be ended ("YES" in step ST6550), the support information output unit 150 (150E) ends the processing ("End").

[0173] Next, a specific example of the support information output by the support device 100 (100F) according to this embodiment will be described. For example, support information including advice such as "Dark indoor lighting tends to make you feel depressed" or "You have plans to study later, so you should brighten the lights" can be generated. In addition, support information can be generated that includes advice such as, "When the air pressure is low, you tend to feel depressed.", "The air pressure may drop later.", and "Doing XX tends to make you feel more positive, so why not try doing XX to change your mood?"

[0174] This embodiment further shows an example of an embodiment including the following configuration.

[10] a target surrounding information acquisition unit that acquires target surrounding information indicating the environmental condition of the person to be measured; Equipped with The trend estimating unit includes: Furthermore, an emotional tendency is estimated for each environmental situation indicated in the object surrounding information acquired by the object surrounding information acquisition unit; The support information output unit further generating and outputting support information using the emotional tendency for each environmental situation estimated by the tendency estimation unit; 1. A support device comprising: As a result, the present disclosure further has the effect of being able to provide an assistance device that makes it possible to enhance assistance that takes into account the environmental conditions of the person to be measured. Furthermore, the present disclosure achieves the same effects as those described above by applying the above configuration to a system including the assistance device, the above assistance method, or the above program.

[0175] Here, a hardware configuration for realizing the functions of the present disclosure will be described. FIG. 29 is a diagram illustrating a first example of a hardware configuration for realizing the functions according to the configuration of the present disclosure. FIG. 30 is a diagram illustrating a second example of a hardware configuration for realizing the functions according to the configuration of the present disclosure. Each of the assistance devices 100, 100A, 100B, 100C, 100D, 100E, and 100F of the present disclosure is realized by hardware such as that shown in FIG. 29 or FIG.

[0176] As shown in FIG. 29, each of the support devices 100, 100A, 100B, 100C, 100D, 100E, and 100F is configured with, for example, a processor 10001, a memory 10002, an input / output interface 10003, and a communication circuit 10004. The processor 10001 and the memory 10002 are, for example, installed in a computer. The memory 10002 stores the computer, data acquisition units 110, 110A, 110B, 110C, 110D, 110E, and 110F, emotion information acquisition unit 111, biological information acquisition unit 112, object identification information acquisition unit 113, schedule acquisition unit 114, object state acquisition unit 115, drowsiness level acquisition unit 116, sleep time acquisition unit 117, object surrounding information acquisition unit 118, tendency estimation units 130, 130A, 130B, 130C, 130D, 130E, and 130F, drowsiness tendency estimation units 131, 131E, and 131F, sleep time tendency estimation units 132, 132E, and 132F, and support information output unit The memory stores programs for causing the units 150, 150A, 150B, 150C, 150D, 150E, and 150F, the trend prediction units 151, 151B, 151C, 151D, 151E, and 151F, the plan generation units 152, 152C, 152D, 152E, and 152F, the advice generation units 153, 153B, 153C, 153D, 153E, and 153F, the emotional factor estimation units 155, 155B, 155C, 155D, 155E, and 155F, part of the trend holding units 170, 170B, 170C, 170D, 170E, and 170F, and a control unit not shown to function. The processor 10001 reads out and executes the program stored in the memory 10002, whereby the data acquisition units 110, 110A, 110B, 110C, 110D, 110E, 110F, emotion information acquisition unit 111, biological information acquisition unit 112, object identification information acquisition unit 113, schedule acquisition unit 114, object state acquisition unit 115, drowsiness level acquisition unit 116, sleep time acquisition unit 117, object surrounding information acquisition unit 118, tendency estimation units 130, 130A, 130B, 130C, 130D, 130E, 130F, drowsiness tendency estimation units 131, 131E, 131F, sleep time tendency estimation unit 132, and the like are provided. 32, 132E, 132F, support information output units 150, 150A, 150B, 150C, 150D, 150E, 150F, trend prediction units 151, 151B, 151C, 151D, 151E, 151F, plan generation units 152, 152C, 152D, 152E, 152F, advice generation units 153, 153B, 153C, 153D, 153E, 153F, emotional factor estimation units 155, 155B, 155C, 155D, 155E, 155F, part of trend holding units 170, 170B, 170C, 170D, 170E, 170F, and functions of a control unit not shown are realized. Furthermore, memory 10002 or other memories not shown implement parts of trend holders 170, 170B, 170C, 170D, 170E, and 170F, as well as a storage unit not shown. Furthermore, the communication circuit 10004 realizes a communication unit (not shown).

[0177] The processor 10001 is, for example, a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor, a microcontroller, or a digital signal processor (DSP). Memory 10002 may be a non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable Read Only Memory) or flash memory, or a magnetic disk such as a hard disk or flexible disk, or an optical disk such as a CD (Compact Disc) or DVD (Digital Versatile Disc), or a magneto-optical disk. The processor 10001 and the memory 10002 or the communication circuit 10004 are connected in a state where they can transmit data to each other. The processor 10001, the memory 10002, and the communication circuit 10004 are also connected in a state where they can transmit data to other hardware via the input / output interface 10003.

[0178] Or, in the support devices 100, 100A, 100B, 100C, 100D, 100E, 100F, the data acquisition units 110, 110A, 110B, 110C, 110D, 110E, 110F, the emotion information acquisition unit 111, the biological information acquisition unit 112, the object identification information acquisition unit 113, the schedule acquisition unit 114, the object state acquisition unit 115, the drowsiness level acquisition unit 116, the sleeping time acquisition unit 117, the object surrounding information acquisition unit 118, the tendency estimation units 130, 130A, 130B, 130C, 130D, 130E, 130F, the drowsiness tendency estimation units 131, 131E, 131F, the sleeping time tendency estimation units 132, 132E, 132F, the support information As shown in FIG. 30 , part of the functions of the output units 150, 150A, 150B, 150C, 150D, 150E, and 150F, the trend prediction units 151, 151B, 151C, 151D, 151E, and 151F, the plan generation units 152, 152C, 152D, 152E, and 152F, the advice generation units 153, 153B, 153C, 153D, 153E, and 153F, the emotional factor estimation units 155, 155B, 155C, 155D, 155E, and 155F, the trend holding units 170, 170B, 170C, 170D, 170E, and 170F, and a control unit (not shown) may be realized by a dedicated processing circuit 20001.

[0179] The processing circuit 20001 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), an FPGA (Field-Programmable Gate Array), an SoC (System-on-a-Chip), or a system LSI (Large-Scale Integration). Furthermore, the memory 20002 or another memory not shown implements a storage unit not shown. Memory 20002 may be a non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable Read Only Memory) or flash memory, or a magnetic disk such as a hard disk or flexible disk, or an optical disk such as a CD (Compact Disc) or DVD (Digital Versatile Disc), or a magneto-optical disk. Furthermore, the communication circuit 20004 realizes a communication unit (not shown). The processing circuit 20001 and the memory 20002 or the communication circuit 20004 are connected in a state where they can transmit data to each other. In addition, the processing circuit 20001, the memory 20002, and the communication circuit 20004 are connected in a state where they can transmit data to each other and to other hardware via the input / output interface 20003. In addition, in the support devices 100, 100A, 100B, 100C, 100D, 100E, 100F, the data acquisition units 110, 110A, 110B, 110C, 110D, 110E, 110F, emotion information acquisition unit 111, biological information acquisition unit 112, object identification information acquisition unit 113, schedule acquisition unit 114, object state acquisition unit 115, drowsiness level acquisition unit 116, sleeping time acquisition unit 117, object surrounding information acquisition unit 118, tendency estimation units 130, 130A, 130B, 130C, 130D, 130E, 130F, drowsiness tendency estimation units 131, 131E, 131F, sleeping time tendency estimation units 132, 132E, 132F, support information The information output units 150, 150A, 150B, 150C, 150D, 150E, 150F, the trend prediction units 151, 151B, 151C, 151D, 151E, 151F, the plan generation units 152, 152C, 152D, 152E, 152F, the advice generation units 153, 153B, 153C, 153D, 153E, 153F, the emotional factor estimation units 155, 155B, 155C, 155D, 155E, 155F, part of the trend holding units 170, 170B, 170C, 170D, 170E, 170F, and the functions of a control unit (not shown) may be realized by separate processing circuits or may be realized collectively by a processing circuit.

[0180] Or, in the support devices 100, 100A, 100B, 100C, 100D, 100E, 100F, the data acquisition units 110, 110A, 110B, 110C, 110D, 110E, 110F, the emotion information acquisition unit 111, the biological information acquisition unit 112, the object identification information acquisition unit 113, the schedule acquisition unit 114, the object state acquisition unit 115, the drowsiness level acquisition unit 116, the sleeping time acquisition unit 117, the object surrounding information acquisition unit 118, the tendency estimation units 130, 130A, 130B, 130C, 130D, 130E, 130F, the drowsiness tendency estimation units 131, 131E, 131F, the sleeping time tendency estimation units 132, 132E, 132F, and the support information output units 150, 150A, 150B, 150C, 150D, 150E, 150F. B, 150C, 150D, 150E, 150F, trend prediction units 151, 151B, 151C, 151D, 151E, 151F, plan generation units 152, 152C, 152D, 152E, 152F, advice generation units 153, 153B, 153C, 153D, 153E, 153F, emotional factor estimation units 155, 155B, 155C, 155D, 155E, 155F, trend holding units 170, 170B, 170C, 170D, 170E, 170F, and part of the functions of a control unit (not shown) may be realized by the processor 10001 and memory 10002, and the remaining functions may be realized by the processing circuit 20001.

[0181] It should be noted that, within the scope of this disclosure, the embodiments may be freely combined, any component of each embodiment may be modified, or any component of each embodiment may be omitted.

[0182] The present disclosure can provide more comprehensive emotional support than ever before, and is therefore suitable for use in, for example, a support device that provides emotional advice to a user. [Explanation of symbols]

[0183] 1, 1A, 1B, 1C, 1D, 1E, 1F Support system, 100, 100A, 100B, 100C, 100D, 100E, 100F Support device, 110, 110A, 110B, 110C, 110D, 110E, 110F Data acquisition unit, 111 Emotion information acquisition unit, 112 Biometric information acquisition unit, 113 Object identification information acquisition unit, 114 Schedule acquisition unit, 115 Object state acquisition unit, 116 Drowsiness level acquisition unit, 117 Sleep time acquisition unit, 118 Object surrounding information acquisition unit, 130, 130A, 130B, 130C, 130D, 130E, 130F Tendency estimation unit, 131, 131E, 131F Drowsiness tendency estimation unit, 132, 132E, 132F Sleep time trend estimation section, 150,150A,150B,150C,150D,150E,150F Support information output section, 151,151B,151C,151D,151E,151F Trend prediction section, 152,152C,152D,152E,152F Plan generation section, 153,153B,153C,153D,153E,153F Advice generation section, 155,155B,155C,155D,155E,155F Emotional factor estimation section, 170,170B,170C,170D,170E,170F Trend holding part, 600, 600A, 600B, 600C, 600D, 600E, 600F Information source device, 700 output destination device, 3010 schedule, 3020 schedule, 3030 schedule, 4000 mobile terminal (smartphone), 4010 target state reception image, 5000 mobile terminal (smartphone), 5010 image "Today's sleep time", 5020 image "Weekly sleep time", 10001 processor, 10002 memory, 10003 input / output interface, 10004 communication circuit, 20001 processing circuit, 20002 memory, 20003 input / output interface, 20004 communication circuit.

Claims

1. an emotion information acquisition unit that acquires emotion values ​​in time series that are values ​​that indicate the emotion of the person being measured; a tendency estimation unit that estimates an emotion tendency, which is a tendency related to an emotion, for each tendency type using the time-series emotion values ​​acquired by the emotion information acquisition unit; a support information output unit that outputs support information regarding an emotional tendency based on the estimation result by the tendency estimation unit; A support device comprising:

2. The support information is The information includes one or more of the following: emotional highs and lows over time, duration of emotional highs and lows, seasonal emotional highs and lows, monthly emotional highs and lows, day-of-the-week emotional highs and lows, time-of-day emotional highs and lows, or points of change in emotional highs and lows.

2. The support device according to claim 1, wherein the support device is a computer.

3. an emotional factor estimation unit that estimates an emotional factor based on the emotional tendency; Equipped with the support information output unit outputs support information including the emotion factor estimated by the emotion factor estimation unit.

3. The support device according to claim 1 or 2.

4. The support information output unit predicting an emotional tendency from the current time onward using the past emotional tendency estimated by the tendency estimation unit, and generating and outputting support information using the predicted emotional tendency; 3. The support device according to claim 1 or 2.

5. The support information is The information includes one or more of the emotional tendency of the subject up to the current time, the emotional tendency of the subject at the current time, or the emotional tendency of the subject after the current time.

3. The support device according to claim 1 or 2.

6. a schedule acquisition unit that acquires the schedule of the measurement subject; Equipped with The trend estimating unit includes: Estimating an emotional tendency for each schedule acquired by the schedule acquisition unit; The support information output unit Support information is generated and output using the emotional tendencies for each schedule.

3. The support device according to claim 1 or 2.

7. a subject state acquisition unit that acquires subject state information indicating the state of the subject; Equipped with The support information output unit predicting an emotional tendency from the current time onwards based on the state of the subject to be measured indicated in the subject state information acquired by the subject state acquisition unit, the past emotional tendency estimated by the tendency estimation unit, and the emotional tendency at the current time; 3. The support device according to claim 1 or 2.

8. The target state information acquired by the target state acquisition unit includes facial expressions, gestures, or facial expressions and gestures of the measurement subject.

8. The support device according to claim 7.

9. a drowsiness level acquisition unit that acquires the drowsiness level of the subject in time series; a drowsiness tendency estimation unit that estimates drowsiness tendency using the time-series drowsiness degree acquired by the drowsiness degree acquisition unit; a sleep time acquisition unit that acquires the sleep time of the measurement subject; a sleep time tendency estimation unit that estimates a sleep time tendency using the time-series sleep time acquired by the sleep time acquisition unit; Equipped with The support information output unit generating and outputting support information using the sleepiness tendency, the sleeping time tendency, and the emotional tendency estimated by the tendency estimation unit; 3. The support device according to claim 1 or 2.

10. a target surrounding information acquisition unit that acquires target surrounding information indicating the environmental condition of the person to be measured; Equipped with The trend estimating unit includes: Furthermore, an emotional tendency is estimated for each environmental situation indicated in the object surrounding information acquired by the object surrounding information acquisition unit; The support information output unit further generating and outputting support information using the emotional tendency for each environmental situation estimated by the tendency estimation unit; 3. The support device according to claim 1 or 2.

11. a biological information acquisition unit for acquiring biological information of a subject, the emotion information acquisition unit calculates and acquires an emotion value using the biometric information acquired by the biometric information acquisition unit; 3. The support device according to claim 1 or 2.

12. A support method using a support device, an emotion information acquisition unit of the support device acquires emotion values ​​in time series, the emotion values ​​being values ​​indicating the emotion of the subject; a tendency estimation unit of the support device estimating an emotion tendency, which is a tendency related to an emotion, for each tendency type using the time-series emotion values ​​acquired by the emotion information acquisition unit; a support information output unit of the support device that outputs support information related to the emotional tendency based on the estimation result by the tendency estimation unit; A support method characterized by:

13. Computer, an emotion information acquisition unit that acquires emotion values ​​in time series that are values ​​that indicate the emotion of the person being measured; a tendency estimation unit that estimates an emotion tendency, which is a tendency related to an emotion, for each tendency type using the time-series emotion values ​​acquired by the emotion information acquisition unit; a support information output unit that outputs support information regarding an emotional tendency based on the estimation result by the tendency estimation unit; an assist device comprising: A program characterized by operating as

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