JP7108238B2 - Well-being value estimation system - Google Patents

Description

The present invention relates to a well-being value estimation system, and more particularly to a system for estimating well-being values of users participating in an event.

In recent years, research on "well-being" has been advanced. "Well-being" means that a person is in good physical, mental and social condition, not just being healthy (not sick).

Conventionally, techniques for estimating a person's emotional state have been developed. For example, Patent Literature 1 discloses a technique of extracting a pitch frequency or the like from a voice uttered by a person and estimating the emotional state of the person from the correspondence relationship between the pitch frequency and the emotional state. Further, Patent Literature 2 describes a method of measuring a subject's mental state and activity level and providing the subject with a plan for improving vitality based on the relationship between them.

WO2006/132159 JP 2018-92363 A

Although the above-described techniques simply relate to estimating an individual's emotional state or improving an individual's vitality from an estimated mental state, there is room for improvement in improving the accuracy of estimating an emotional state or mental state. there were. That is, it is difficult to accurately estimate the user's well-being level (well-being value) related to the emotional state or mental state while the user is engaged in social activities (participating in events, driving a vehicle, etc.).

The present invention has been made to solve the above problems, and an object thereof is to provide a well-being value estimation system with improved accuracy in estimating a well-being value as a user's mental activity level.

In order to achieve the above object, the present invention provides a system for estimating a well-being value as a mental activity level of a user participating in an event, comprising: a management device for estimating the well-being value; and an analysis device that generates analysis data representing the user's mental state based on this biological information, and the management device measures the analysis data and the evaluation data that is the degree of the user's subjective evaluation of the event. The evaluation data indicates the degree of evaluation of a predetermined activity included in the event, and the management device calculates a first well-being value based on the evaluation data calculating a well-being estimate and calculating a second well-being estimate based on the analytical data; if the second well-being estimate for a given activity matches the first well-being estimate; calculating a wellbeing value at a predetermined activity based on the first wellbeing estimate and the second wellbeing estimate, wherein the second wellbeing estimate for the predetermined activity is equal to the first wellbeing estimate It is characterized by not calculating a well-being value in a given activity if it does not match the value.

According to the present invention configured as described above, when there is no significant difference between the first well-being (WB) estimated value and the second WB estimated value, the first WB estimated value, the second A WB value with relatively high estimation accuracy is calculated using the WB estimated value of 2 or the like. On the other hand, in the present invention, if there is a significant difference between the first WB estimated value and the second WB estimated value, the estimated accuracy of the calculated WB value is considered to be low. Not calculated. Therefore, in the present invention, it is possible to calculate only WB values with relatively high estimation accuracy without calculating WB values with low estimation accuracy.

Further, in the present invention, specifically, when the second estimated well-being value does not match the first estimated well-being value, the first estimated well-being value is different from the second estimated well-being value. is larger than a predetermined ratio.

Also, preferably in the present invention, the second well-being estimated value is an average value during a predetermined activity period.

In the present invention, preferably, the analysis device is a measurement device that measures biometric data representing the physiological state of the user and generates analysis data representing the psychological state of the user based on the biometric data. Furthermore, preferably in the present invention, the analysis device is a voice analysis device that measures voice uttered by the user and generates analysis data representing the emotional state of the user based on this voice.

According to the present invention, it is possible to provide a well-being value estimation system with improved accuracy in estimating a well-being value as a user's mental activity level.

1 is a conceptual diagram of a life active system according to an embodiment of the invention; FIG. 1 is a configuration diagram of a life active system according to an embodiment of the present invention; FIG. 4 is a graph showing a change in WB value over time; 4 is a graph showing changes over time in a user's virtual currency balance according to an embodiment of the present invention; FIG. 4 is an explanatory diagram of WB value estimation in an activity according to the embodiment of the present invention; 4 is a process flow for estimating WB values in an activity according to an embodiment of the invention; FIG. 4 is an explanatory diagram of WB value estimation in an activity according to the embodiment of the present invention; 4 is a processing flow for calculating an index value according to the embodiment of the present invention;

Hereinafter, life active systems according to embodiments of the present invention will be described with reference to the accompanying drawings. First, a schematic configuration of the life active system will be described with reference to FIGS. 1 and 2. FIG. FIG. 1 is a conceptual diagram of the life active system, and FIG. 2 is a configuration diagram of the life active system. In the following, "life active" may be referred to as "LA" and "well-being" as "WB".

In this embodiment, "well-being" means that the user negates his/her own negative feelings, acquires a sense of self-actualization, and acquires a sense of self-affirmation in relation to society (others). Self-esteem involves feeling needed and useful in society.

In addition, “life active” means a state in which the user continuously engages in activities (WB activities) that increase the level of well-being (WB value) through interaction with society through the car life (ownership and driving of a vehicle). do. Then, it can be defined as "well-aging" that a user lives in a life-active state and accumulates WB activities or WB experiences in his or her life. By continuously accumulating WB experience, the user can reach a higher WB state.

In addition, the LA system of this embodiment is configured to give economic value (virtual currency) to users who participate in events and perform WB activities. This virtual currency can be exchanged with legal currency at a predetermined exchange rate. Therefore, by participating in the event, the user can increase the WB value in physical, mental and social aspects, and is given an incentive to participate in the event in terms of economy.

As shown in FIG. 1, the life active system S (LA system S) of this embodiment is a system for increasing the WB value of user A through participation in event E by user A. As shown in FIG. Events E are, for example, travel tours, nature tours, mystery tours, volunteer activities, and the like.

The event provider B plans and implements the event E with the cooperation of the cooperator C in terms of personnel and money. Collaborators C are event venue vendors, local people, funders, and the like. User A pays various expenses related to event E (participation fee, gasoline fee, meal fee, etc.). Event provider B and cooperator C can recover operating costs from user A's expenditure. In the LA system of this embodiment, a community D is formed by A, B, and C via an event E, and various events are repeatedly held by circulating funds in the community D. there is

Next, the configuration of the LA system S of this embodiment will be described with reference to FIG. The LA system S includes a measuring device 10 mounted on the vehicle 1 of the user A, a mobile terminal 20 of the user A, and a management device 30 of the event provider B, which are configured to be wirelessly communicable. there is Wireless communication is possible between the measuring device 10 and the mobile terminal 20 and the management device 30 via a communication line (Internet line) 3 . The measuring device 10 and the mobile terminal 20 can communicate with each other using short-range wireless communication technology (for example, Bluetooth (registered trademark)).

The measurement device 10 is a computer device including a processor 10a, a memory 10b, a communication circuit 10c, etc., and performs processing (including multiple analysis processing) by the processor 10a executing various programs stored in the memory 10b. is configured as The measuring device 10 is connected to various sensor devices mounted on the vehicle 1, and the processor 10a receives measurement data from the sensor devices and stores the measurement data in the memory 10b. The processor 10a also transmits processed data obtained by processing the measurement data to the management device 30 via the communication circuit 10c.

The sensor device includes a heartbeat measuring device 11 attached to the steering wheel and a positioning device 12 . The heartbeat measuring device 11 is an example of a biosensor device. The heartbeat measuring device 11 measures the heartbeat variability (electrocardiographic waveform) of the driver (user A) and outputs the measurement data as biological data (heartbeat data). The positioning device 12 acquires the vehicle position using satellite positioning using a GPS (Global Positioning System) or autonomous positioning using a gyro sensor or the like.

As the biosensor device, instead of or in addition to the heartbeat measuring device 11, a resistance measuring device or a camera may be used. The resistance measuring device measures the skin resistance of user A and outputs the measured data as biological data (resistance data). Since the contact resistance of the skin changes depending on the perspiration state, the resistance data represents the perspiration state of the user A. Also, the camera captures an image of user A and outputs captured image data. The measuring device 10 performs image analysis on this image data to acquire biometric data such as the pupil diameter of user A, eye movement, behavior of the upper body including the position and orientation of the head and shoulders, and facial expression of user A. can do.

In a tense state in which a person feels mental stress, heart rate fluctuations occur, perspiration is promoted, the pupil diameter increases, and the magnitude of eye movement increases. In addition, when a person is in a tense state, the behavior of the upper body, including the position and orientation of the head and shoulders, increases when the vehicle accelerates, decelerates, or turns. In addition, when a person is in a tense state, changes appear in the facial expression (displacement distance of a specific part, etc.).

Therefore, the measuring device 10 can estimate the user's state of mind (that is, the degree of tension) by analyzing the biometric data described above. For example, the measuring device 10 measures the magnitude of biological data (frequency obtained by frequency analysis of heart rate variability, skin resistance, pupil diameter, eye movement, behavior of the upper body, movement distance of a specific part of the face). Based on this, one can estimate the degree of tension (eg, ranging from 0 lowest to 10 highest). User A is judged to be mentally in a good state of activity when he/she has a medium and optimal tension level (for example, tension level=5).

The measuring device 10 stores the analysis result data of the psychological state estimated based on the biological data and the vehicle position data in the memory 10b at any time, and transmits these data together with the vehicle identification data for specifying the vehicle 1 to the management device 30. do.

Further, in the present embodiment, the measuring device 10 is mounted on the vehicle 1, but the measuring device 10 may be configured as a portable biological information measuring device so that the user A can always carry it. For example, the measuring device 10 may be a wristband-type heart rate measuring device with a communication function.

The portable terminal 20 is a portable computer device including a processor 20a, a memory 20b, a communication circuit 20c, an input/output device 20d, a display device 20e, an acceleration sensor 20f, and the like. The mobile terminal 20 is an example of a wearable sensor terminal device. The mobile terminal 20 is configured to perform various processes by the processor 20a executing various programs stored in the memory 20b. In this embodiment, the mobile terminal 20 is configured by a smart phone, and has a voice communication function and a data communication function that the smart phone originally has.

The mobile terminal 20 also functions as various sensor terminal devices by executing various applications (programs) installed in the memory 20b. The processor 20a also transmits and receives various data to and from the management device 30 via the communication circuit 20c. The various types of data include analysis result data transmitted by the mobile terminal 20, commands transmitted by the management device 30, and the like.

The input/output device 20d includes a keyboard or touch sensor type input device, a microphone, a speaker, and the like. Note that the mobile terminal 20 may be a mobile information terminal such as a PDA, or a dedicated small computer device.

The mobile terminal 20 functions as a communication device using communication-related applications. The mobile terminal 20 accesses a website via a phone call function, an e-mail function, an SNS communication function (e.g., Facebook (registered trademark), Instagram (registered trademark), LINE (registered trademark), etc.), and the Internet line 3. It has a browser function for

In addition, the mobile terminal 20 functions as a voice analysis device using a voice analysis application. The mobile terminal 20 analyzes the voice uttered by the user A for a predetermined period (for example, 2 to 6 seconds) to estimate the emotional state of the user.

Human speech includes voluntary elements that we can control and involuntary elements that are related to emotions in the brain. In other words, the vocal cords are connected to the emotional part of the brain via the vagus nerve, and emotions in the brain affect the movement of the vocal cords. Emotions in the brain therefore affect speech parameters (fundamental frequency, sound pressure, pitch frequency, frequency distribution, velocity, intensity variation, etc.).

In this embodiment, the mobile terminal 20 establishes a correlation between predetermined voice parameters (in this example, magnitude of the fundamental frequency, intensity at the fundamental frequency) and each emotion (in this example, joy, sadness, peace of mind, and anger). is stored in the memory 20b. The mobile terminal 20 is configured to estimate the proportion of each emotion by analyzing the voice based on this table. The sum of the proportions of each emotion is constant (eg, 100%).

Further, the portable terminal 20 compares a predetermined voice parameter (pitch frequency in this example) with a predetermined value, thereby estimating the degree of excitement (degree of excitement: for example, a range of minimum 0 to maximum 10). The intensity of each emotion is estimated by multiplying the intensity by the estimated proportion of each emotion. Note that the pitch frequency is the repetition frequency of the same frequency component. The greater the pitch frequency than the predetermined value, the greater the degree of excitement.

The healthier the body and mind, the greater the intensity of positive emotions (joy, relief) than negative emotions (sadness, anger). For example, the mobile terminal 20 sums the numerical values obtained by multiplying the coefficient set for each emotion by the estimated strength of each emotion, and calculates the degree of mental activity (or health) based on this total value. is configured as For example, coefficients for positive emotions can be positive values (>0) and coefficients for negative emotions can be negative values (<0). The degree of mental health can be expressed, for example, in a range from 0 (lowest) to 100 (highest).

The mobile terminal 20 as a speech analysis device always picks up the speech uttered by the user A via a microphone. In addition, the mobile terminal 20 intermittently (every predetermined time (for example, every 10 minutes) and in response to a command from the management device 30) intermittently asks the user a question (for example, "What is the event?"). How was it?") is emitted from the speaker. Then, the mobile terminal 20 picks up the voice uttered by the user A in response to this question. The mobile terminal 20 analyzes the acquired voice data and stores the voice analysis result (emotional data representing emotional state) together with the acquisition time in the memory 20b. Note that the function as the voice analysis device may be provided in the measurement device 10 of the vehicle 1 in addition to the mobile terminal 20 . Note that such an application for voice analysis is provided by PST Corporation under the product name of "MIMOSYS (registered trademark)", for example.

In addition, the mobile terminal 20 functions as a positioning device using a positioning application. The mobile terminal 20 acquires the current position of the mobile terminal 20 based on communication with surrounding communication stations or based on satellite positioning using GPS, and records it in the memory 20b as current position data. The mobile terminal 20 transmits current location data to the management device 30 at a predetermined time. The management device 30 can generate action log data by tracking the movement trajectory of the user A based on the current position data.

In addition, the mobile terminal 20 functions as a communication analysis device using a communication analysis application. The mobile terminal 20 analyzes the text data transmitted by the user A using the e-mail function, the SNS communication function, and the browser function described above by natural language processing (text mining). In addition, the mobile terminal 20 may similarly analyze responses (eg, email replies, approval responses such as “Like”, comments posted, etc.) from friends and the like to the transmission by User A. FIG. The mobile terminal 20 transmits the analysis result to the management device 30 .

Based on an expression table that is part of the application, the mobile terminal 20 extracts specific words (“fun,” “happy,” “sad,” etc.) representing emotional states from text data sent, received, or posted by the user. , "Tatsurai", etc.) are extracted, and the emotional state of the user is analyzed from the frequency of appearance of these words. The expression table stores a large number of expressions corresponding to each of emotions. Therefore, the communication analysis result includes emotional data indicating the user's emotional state (joy, sadness, peace of mind, and anger in this example) at the time of the call. Emotion data includes the ratio of four emotions and the intensity of each. Note that the mobile terminal 20 may analyze the emotional state from the image data transmitted or posted by the user.

In addition, the mobile terminal 20 functions as a behavior measurement device using a behavior measurement application. Based on the measurement data received from the acceleration sensor 20f, the mobile terminal 20 calculates acceleration and jerk when the user moves while holding the mobile terminal 20. FIG. The obtained acceleration and jerk are recorded in the memory 20b as behavior data. The mobile terminal 20 transmits behavior data to the management device 30 at a predetermined time.

The management device 30 is a computer device (server device) including a processor 30a, a memory 30b, a communication circuit 30c, an input/output device 30d, a display device 30e, and the like. , is configured to perform various processes. In addition to programs, the memory 30b stores event data of various events provided by event providers. The event data includes information on the schedule, venue, content, participants, etc. of the event. The management device 30 receives data from the measurement device 10 and the mobile terminal 20 of the participant (user A). Also, the management device 30 transmits predetermined information and commands to the measurement device 10 and the mobile terminal 20 based on the received data and the event data.

The memory 30b of the management device 30 also stores user data of the user A (and event participants). The user data includes user A's personal data (age, gender, address, vehicle identification information, mobile terminal information, etc.), action log data, various data (positioning data, emotion data, current location data, behavior data, estimated WB value, etc.). ), virtual currency data, etc. The virtual currency data represents the amount (amount) of the virtual currency that the user A possesses.

Next, with reference to FIG. 3A, user behavior and temporal changes in WB values when participating in an event will be described. FIG. 3A is a graph showing changes in WB values over time. In FIG. 3A, user A participates in a certain event E (one-day local experience tour). The periods shown in FIG. 3A are a preparation period P1 (for example, three weeks) before user A participates in event E, an action period P2 (for example, one day) during which user A participates in event E, and event E including a cultivation period P3 (for example, 4 weeks) and a fixation period P4 (for example, 3 months).

The event E is planned to increase the WB value of the user A by having various experiences through interaction with friends and people with whom the user A comes in contact. User A uses vehicle 1 to move to the venue and enjoys event activities (contents) at event E. - 特許庁After the event E ends, the user A uses the vehicle 1 to go home. When the venue is spread over a plurality of places, the user A uses the vehicle 1 to move between the venues.

Generally, in daily life, user A is in a state where it is difficult for user A to feel WB (that is, the WB value is low). User A receives information on event E during preparation period P1 (time t0). The preparation period P1 is a period during which the user A prepares to participate in an extraordinary event E that is different from daily life.

When user A comes into contact with information on event E and desires to participate in event E is fostered within user A, this mood acts to counteract user A's self-negative feelings. Therefore, the WB value of user A increases. User A transmits participation application information from mobile terminal 20 to management device 30 in a state where the WB value is higher than usual (time t1).

During the preparation period P1, the user A can inform his friends of his participation in the event E by using the communication function (for example, SNS communication function, e-mail function) of the mobile terminal 20 . That is, user A can express his expectations for event E using text data or image data, and transmit this expression via the Internet line 3 . As a result, user A's sense of anticipation grows, and a sense of self-fulfillment (having a virtual experience of event E) is satisfied by notifying friends. Thus, in the preparation period P1, the WB value of user A is maintained higher than usual. At this time, the mobile terminal 20 (communication analysis device) analyzes the data transmitted by the user A and the contents of the response, and transmits analysis result data to the management device 30 . Also, the portable terminal 20 (speech analysis device) analyzes the speech of the user A at predetermined time intervals and based on commands from the management device 30, and transmits analysis result data. The management device 30 can use the received analysis result data to estimate the WB value.

During the action period P2, when the user A drives the vehicle 1 and heads for the event venue (time t2), self-negative emotions are further canceled and the user's sense of self-fulfillment is gradually satisfied. WB value increases. The management device 30 can receive analysis result data acquired by the measurement device 10 and the portable terminal 20 (voice analysis device) of the vehicle 1 during driving, and use the received analysis result data to estimate the WB value.

After arriving at the venue, user A experiences three major activities (contents) J1, J2, J3 that make up event E (times t3, t4, t5; each with a predetermined length of time). In each activity (for example, making local specialties, experiencing harvesting of agricultural products, etc.), user A develops a sense of self-actualization and self-affirmation, and the WB value takes a maximum value. Note that when one activity ends, the maximized WB value gradually decreases over time, but the WB value maximizes again in the next activity. Note that event E may have only one activity.

Also, during the action period P2, the management device 30 transmits individual question information to the mobile terminal 20 of the user A after each activity based on the event data (and user A's current location data if necessary). . This individual question information is for investigating user A's degree of satisfaction with each activity J1, J2, and J3. User A can operate the mobile terminal 20 to answer a plurality of questions included in the individual question information to create individual evaluation information, and reply with this individual evaluation information within a predetermined period (time t3, t4, t5).

A plurality of questions are set so that it is possible to estimate from the individual evaluation information how much the user A has gained a sense of accomplishment, a sense of self-fulfillment, a sense of self-affirmation, etc. through each activity. Specifically, the questions may be, for example, "Would you recommend this activity to others?", "Would you like to participate in this activity again?", "Do you feel satisfied after experiencing this activity?" Was it helpful to people in the field?"

The individual evaluation information includes the user's subjective satisfaction evaluation level for each question. User A can, for example, select a subjective evaluation level from a plurality of options for each question. Choices are, for example, "very good (100 points)", "good (75 points)", "fair (50 points)", "bad (25 points)", "very bad (0 points)". . Also, the evaluation format may be a format in which the user A selects a level of evaluation from 100 points (“very good”) to 0 points (“very bad”).

When the management device 30 receives the individual evaluation information from the user A, the management device 30 can use the evaluation data included in this individual evaluation information to estimate the WB value. For example, it is estimated that the higher the average score of evaluation levels for a plurality of questions, the higher the WB value. The evaluation data is the degree of user A's subjective evaluation of the activity.

In addition, in accordance with the transmission of the individual question information (for example, when transmitting the individual question information, within a predetermined time after the transmission of the individual question information, when receiving the individual evaluation information from the user A, when the individual evaluation information A command for speech analysis may be transmitted to the portable terminal 20 within a predetermined time after reception. Upon receiving this command, the mobile terminal 20 asks the user A a question, acquires the voice data of the user, analyzes the voice data, and estimates the emotional state. The mobile terminal 20 returns analysis result data (emotional data indicating the estimated emotional state) to the management device 30 . The management device 30 can use the received analysis result data to estimate the WB value.

In addition, User A can use the communication function (particularly, the SNS communication function) of the mobile terminal 20 to share impressions of each activity with friends. That is, user A can express the excitement and satisfaction obtained in each activity using text data and image data, and transmit this expression via the Internet line 3 . Due to such calling behavior, the WB value of user A is maintained in a high state without being attenuated. Also, in the same manner as described above, the management device 30 can receive analysis result data for transmission data from the mobile terminal 20 (communication analysis device) and use this analysis result data for estimating the WB value.

In the action period P2, even when the user A drives the vehicle 1 and moves between activities, the management device 30 receives the analysis result data from the measurement device 10 and the portable terminal 20 (speech analysis device) of the vehicle 1. , the received analysis result data can be used to estimate the WB value.

After the event E ends, the user A drives the vehicle 1 to go home (time t6), and returns to his daily life. While returning home, user A can immerse himself in a sense of self-fulfillment and self-affirmation while looking back on memories of event E. The management device 30 can receive analysis result data acquired by the measurement device 10 and the portable terminal 20 (voice analysis device) of the vehicle 1 during driving, and use the received analysis result data to estimate the WB value.

In the cultivating period P3, the user A has a vivid memory of the event E, and the user A still feels that the event E is familiar. In the cultivating period P3, the acquired positive emotions (sense of self-actualization, self-affirmation) are sorted out within the user A, and a feeling of gratitude toward the people with whom the user met at the event E is cultivated. However, the WB value, which has reached a high level due to participation in event E, gradually decreases over time during the cultivating period P3.

In the cultivating period P3, the management device 30 transmits general question information to the mobile terminal 20 of the user A after a first predetermined period (for example, several days to one week) after the end of the event E (time t7). . This general question information is for surveying user A's overall level of satisfaction with event E. FIG. User A operates the mobile terminal 20 to answer a plurality of questions included in the overall question information in the same manner as in the individual question information described above, thereby creating overall evaluation information and returning this overall evaluation information. possible (time t7).

The format of the general question information is the same as the format of the individual question information described above. Questions may include, for example, "Would you recommend this event to others?", "Would you like to participate in this event again?", "Do you feel satisfied with your experience of this event?" is set as When the management device 30 receives the overall evaluation information from the user A, the management device 30 can use the evaluation data included in this overall evaluation information to estimate the WB value. Evaluation data is the degree of user A's subjective evaluation of event E. FIG. In addition, the management device 30 may transmit a command for voice analysis in the case of the general question information as well as the individual question information. In response to this command, the mobile terminal 20 can execute voice analysis processing and return analysis result data to the management device 30 .

Further, in the cultivating period P3, the management device 30 transmits follow-up information to the portable terminal 20 of the user A after a second predetermined period (for example, one week to several weeks) after the end of the event E. (Time t8). The follow-up information is provided to give User A an opportunity to express his gratitude to the event participants. In response to the follow-up information, User A can reply with thank-you delivery information expressing gratitude. The people involved in the event include all the people involved in the event E, especially the staff managing the event E, volunteer staff, local residents, and the like.

User A can create thank-you communication information by responding to a plurality of response items included in the follow-up information. Response items include, for example, items specifying the target person to be grateful for (place, time, gender, keyword, etc.), items specifying the degree of gratitude ("high", "medium", "low", etc.). . The answer items may include an item in which the user A can enter a comment, and an item specifying the transfer amount of the virtual currency, which will be described later. Therefore, the gratitude transmission information includes the target person to be thanked, the degree of gratitude to the target person, and the like.

As a result, even if the user A does not know the contact information of these target persons (for example, he was hesitant to ask for the contact information), he can easily express his gratitude to the target persons during the cultivation period P3. It can help organize positive emotions. User A may also gain a greater sense of self-fulfillment and self-affirmation. Note that if there is no target person to be thanked, the user A does not have to send the thanks transfer information.

Further, in the fixation period P4, the management device 30 transmits the storage fixation information to the portable terminal 20 of the user A after a predetermined period of time (for example, 2 to 3 months) after the end of the event E (time t9). ). This memory fixation information is to provide user A with memory fixation promotion information (photo album in this example) from event provider B in order to fix memories of event E in user A's memory. The management device 30 takes in the photo data taken by the event provider B during the holding of the event E and the photo data provided to the management device 30 by the event participants, and automatically edits these photo data using a photo album generation application. A photo album can be generated by

Management device 30 publishes the generated photo album data on the website. User A can access the photo album on the website via the Internet line 3 using the browser function of the mobile terminal 20 and the access information included in the fixing information. Note that the management device 30 may send the photo album data to the mobile terminal 20 together with the storage fixation information.

By browsing the photo album, user A recalls event E and reconfirms event E as a past event. In this way, in the fixing period P4, the event E is positioned as a past memory within the user A, and the memory of the event E is fixed in the user A's memory. Also in this fixing period P4, the WB value gradually decreases with time. However, the positive emotions obtained through event E are accumulated in user A's subconsciousness or deep psyche.

User A can transmit confirmation information to the management device 30 using the mobile terminal 20 when browsing the photo album. If user A feels that the event he participated in is favorable, he can send confirmation information back to management device 30 by selecting a confirmation button on the photo album viewing page.

When the confirmation information is returned, User A likes Event E in which he participated, and is expected to act as an "ambassador" to inform his friends and associates of the event and encourage their participation. Also, the confirmation information may be created and transmitted by the user answering a plurality of questions similar to the questions of the general question information in the stored fixed information.

Next, data for estimating the WB value in this embodiment will be described. In this embodiment, a plurality of types of data from low-order data to high-order data shown below can be used for WB value estimation.

First, biometric data (low-order data) provided by the measuring device 10 can be used to estimate the WB value. Biometric data is obtained by passive measurements of user A and represents user A's objective physiological condition. The biometric data is measured constantly or intermittently while the user A is driving the vehicle 1 . The measurement time required to obtain meaningful information from this biological data is several seconds to several minutes. Also, the physiological condition of user A may change in a few seconds to an hour. Therefore, the meaningful period (effective period) of the obtained information is considered to be several minutes to several tens of minutes during or including the time of measurement.

The measurement device 10 analyzes the psychological state (that is, the degree of tension) of the user A using biometric data, and outputs analysis result data. The management device 30 can also estimate the WB value for each measurement time using the received analysis result data and store it in the memory 30b (this WB value may be referred to as "WB _B estimated value"). The WB _B estimate, for example, ranges from 0 (lowest WB value) to 100 (highest WB value). For example, the WB _B estimate is estimated to be the highest (WB _B estimate=100) at the medium optimal tension (tension=5), and the WB _B estimate decreases as the distance from the optimum value increases. The psychological state analysis information obtained from biometric data is low-level information that is not directly linked to user A's emotions (such as emotions). Therefore, the WB _B estimated value estimated from biometric data may be unreliable.

Also, analysis result data or emotional data (intermediate data) provided by a speech analysis device can be used to estimate the WB value. Emotion data is obtained by analyzing user A's voice interactively uttered between a voice analysis device and user A, and represents user A's objective emotional state. Emotion data is an estimation result obtained by estimating user A's emotion with high accuracy. The measurement time required to obtain emotion data is about several seconds. Also, user A's emotions can typically change in a few minutes to an hour. Therefore, the validity period of the obtained information is considered to be several tens of minutes including the time of measurement.

As described above, the mobile terminal 20 outputs emotion data including the percentage of emotions and the degree of mental soundness as analysis result data. Also, the management device 30 can estimate the WB value for each measurement time using the received analysis result data and store it in the memory 30b (this WB value may be referred to as "WB _V estimated value"). The WB _V estimate, for example, ranges from 0 to 100, as does the WB _B estimate. The management device 30 can employ, for example, the degree of mental health (0 to 100) as the WB _V estimated value. Note that the WB _V estimated value estimated from voice analysis is an objective estimated value that is directly linked to user A's emotions, and is generally highly reliable. However, for example, in a long-distance walk event, although a great sense of accomplishment is obtained at the goal point, it is painful during walking between the point points. Therefore, in these types of events, the WB _V estimate may be underestimated as a whole.

Further, analysis result data (intermediate data) generated by the mobile terminal 20 as a communication analysis device can be used to estimate the WB value. However, since this analysis result data is obtained by analyzing user A's subjective transmission data, it may not necessarily represent the user's objective emotional state (the transmission data may differ from user A's thinking). case, etc.). Note that when User A sends a comment or the like using the SNS communication function, a friend or User A himself/herself may further send a comment in response to this sending. Therefore, when analyzing one call from user A including subsequent related calls, it may be necessary to continue the analysis for several minutes to several days. Therefore, the validity period of the obtained information may span the period of communication between the user and the friend.

As described above, the portable terminal 20 outputs emotion data as analysis result data through communication analysis processing. Also, the management device 30 can estimate the WB value for each analysis period using the received analysis result data and store it in the memory 30b (this WB value may be referred to as "WB _C estimated value"). The WB _C estimate, for example, ranges from 0 to 100, as does the WB _B estimate. For example, the management device 30 can multiply four emotions by the ratio of each emotion, the intensity of each emotion, and the coefficient set for each emotion, and adopt the total value of these as the WB _C value.

Also, in order to estimate the WB value, user A's subjective evaluation data (higher-order data) for each activity or event can be used. Obtaining evaluation data requires the lapse of the implementation period of each activity or event. Since the evaluation data is information subjectively evaluated by the user, it is less objective than the data obtained using the measurement device 10 or the speech analysis device. However, by analyzing the evaluation data, user A's higher emotional state for each activity or event E can be estimated.

The management device 30 receives the evaluation data from the mobile terminal 20, estimates the WB value based on the total value of each question, and stores it in the memory 30b (this WB value is sometimes referred to as "WB _E estimated value"). . For example, the ratio (p/m×100) of the total score p of all questions (maximum score of m for all questions) to the full score can be used as the WB _E estimate.

Also, action log data (higher order data) can be used to estimate the WB value. The management device 30 analyzes how much of the various activities scheduled for the event E the user A has experienced from the action log data of the user A, estimates the sense of accomplishment of the user A, 30b. In order to obtain the action log data, it is necessary to pass the implementation period of the event.

At the end of event E, the management device 30 determines how many activities the user A has participated in out of the plurality of activities prepared for the event E from the action log. For example, in event E, if user A participates in m activities out of n activities prepared, the participation ratio (n/m×100) can be used as the WB value (this WB value is " WB _L estimated value”). For example, in an event such as a long-distance walk event in which the goal is to complete the race, a method of estimating the WB _L value using action log data is effective.

Furthermore, in order to estimate the WB value, event participation history data obtained based on user A's action log data (higher-order data) may be considered. That is, the event participation history data represents the number of times the user A has participated in events over the past several years, the frequency of participation, the types of events he has participated in, and the like. By analyzing event participation history data in this way, it is possible to estimate user A's higher-order (social) emotional state with respect to event participation activities over several months to several years. By repeatedly participating in the event, the user A repeatedly feels a sense of self-fulfillment, which unconsciously accumulates. Such repetitive experiences of self-actualization over several years are considered to have a favorable influence on User A's personality. The management device 30 acquires the event participation history data of the user A, for example, multiplies the number of times of participation by a coefficient to calculate a cumulative WB value, and stores the WB value in the memory 30b (this WB value is referred to as "WB _R estimated value ”).

Next, a method for estimating the WB value in this embodiment will be described with reference to FIGS. 4 and 5. FIG. FIG. 4 is an explanatory diagram for estimating WB values in activities, and FIG. 5 is a processing flow for estimating WB values in activities. As described above, in principle, the management device 30 uses each single data (low-level data only, intermediate data only, high-level data only) to obtain WB values (WB _B estimated value, WB _V estimated value , WB _C estimate, WB _E estimate, WB _L estimate, WB _R estimate) can be estimated.

These WB estimated values have different time lengths of target periods depending on the data used. For example, with the WB _B and WB _V estimates, the duration of the period of interest is every few seconds to several minutes, with the WB _C estimate, every call, and with the WB _E estimate, every evaluation (that is, every activity, or event-by-event), which is event-by-event for the WB _L estimate and months to years for the WB _R estimate. For example, as shown in FIG. 4, for activity J1, one or more WB _V estimates can be estimated during its performance, but only one WB _E estimate is estimated.

In addition, the management device 30 can estimate the WB value more in line with the actual situation by combining a plurality of types of data with different time lengths of these target periods. That is, by assuring the certainty of higher-order data with lower-order data, conversely, by assuring the certainty of lower-order data with higher-order data, the guaranteed data It can be used for WB value estimation.

That is, the WB _B estimated value, WB _C estimated value, WB _E estimated value, WB _L estimated value, and WB _R estimated value, excluding the WB _V estimated value, do not necessarily reflect the user A's objective emotion data. Therefore, in the present embodiment, in estimating the WB value, the WB _B estimated value, the WB _C estimated value, the WB _E estimated value, the WB _L estimated value, and the WB _R estimated value are replaced with the WB _V estimated value as the emotion index value. configured to correct. For example, the WB value can be calculated by the following formula.
WB value = (coefficient k _V × WB _V estimated value)
+Σ (coefficient kn x WBn estimated value x WB _V estimated value) + Σ (adjusted value Mn)
(However, n = B, C, E, L, R)

That is, the value obtained by multiplying the WB _V estimated value by the coefficient k _V , the value obtained by multiplying each WBn estimated value by the coefficient kn and the WB _V estimated value, and the adjustment value Mn corresponding to each WBn estimated value. A WB value is calculated by totaling. Note that it is not necessary to use all estimates other than the WB _V estimate. Therefore, the values of the coefficients k _V and kn and the value of the adjustment value Mn can be set differently according to the combination of types of data used.

In this embodiment, when multiple types of data are used, multiple types of data obtained during periods corresponding to each other are used. For example, when estimating the WB value for activity J1 of event E, multiple types of data measured during the implementation period of activity J1 (around time t3) are used. Further, when estimating the WB value for the entire event E, data measured during the implementation period of the event E (t2 to t6) is used in principle. In addition, as the data to be used, an average value during a period or a value at a specific time can be adopted.

A case of estimating the WB value for activity J1 of event E will be specifically described. The holding time of activity J1 is set in the event data. As shown in FIG. 4, a start time t31, an end time t32, and a question information collection time (t32+predetermined time Δt) are set in the event data. Based on the read event data, the management device 30 transmits the individual question information to the mobile terminal 20 at the end time t32 (step S11 in FIG. 5), and transmits the individual evaluation information at the time t33 before the elapse of the predetermined time Δt from the transmission. It is received (step S12 in the figure).

The management device 30 calculates the WBE estimated value for the activity J1 based on the evaluation data in the individual evaluation information received at time _t33 (see Δ in FIG. 4; step S13 in FIG. 5). On the other hand, the management device 30 intermittently receives the analysis result data of the voice analysis from the mobile terminal 20, calculates the WB _V estimated value each time the analysis result data is received, and stores it in the memory 30b (● in FIG. 4). reference).

Further, when the measuring device 10 is configured as a portable biological information measuring device, the management device 30 calculates the average value of the WB _B estimated values and the value around the time t33 based on the psychological state analysis result data received during the target period. WB _B estimated value is calculated (step S14 in FIG. 5). Furthermore, the management device 30 calculates the _WBC estimated value based on the communication analysis analysis data received during the target period (step S14 in FIG. 5).

The management device 30 calculates the emotion index value using the WB _V estimated value calculated corresponding to the target period (t31 to t33) of the activity J1 (step S15 in FIG. 5). In this embodiment, as shown in FIG. 4, activity J1 is a type of activity in which the WB _V estimate is approximately constant over the period, and the plurality of WB _V estimates and WB _E estimates are approximately match. For example, the management device 30 averages a plurality of WB _V estimated values corresponding to the target period, and uses this average value as the emotion index value. Alternatively, the emotion index value may be the WB _{V estimated value at a specific time (for example, times t32, t33, etc.) or the average value of the WB V estimated} values for a specific period (for example, from t32 to t33, etc.).

The management device 30 calculates the WB value for the activity J1 based on the WB _V estimated value, the WB _E estimated value, etc., as described above (step S16 in FIG. 5). That is, the WB value is calculated using the calculated emotion index value, the WB _E estimated value, and the like.

When estimating the WB value for the entire event E, the management device 30 uses various data measured during the target period of the event E (t2 to t7) to perform the same processing as the processing steps in FIG. do. That is, the _WBE estimated value is calculated from the evaluation data in the overall evaluation information obtained at time t7 (see step S13).

Further, another WB estimated value is calculated from the psychological state analysis result data based on the biological data obtained from time t2 to t6 (time t7 if necessary), communication analysis analysis data, and action log data at event E. (see step S14). Further, an index value is calculated from the analysis result data of the voice analysis obtained from time t2 to time t6 (time t7 if necessary) (see step S15). Then, the management device 30 calculates the WB value using the calculated value (see step S16).

In addition, in the above embodiment, the _{WB V} estimated value is used as an emotion index value (or an index value representing the user's mental health level). may That is, the WB _V estimate uses user A's voice as biometric information, and the WB _B estimate uses user A's biometric data as biometric information. health is estimated. Therefore, for example, when the measuring device 10 is configured as a portable biological information measuring device, the WB value may be calculated by the following formula.
WB value = (coefficient k _B × WB _B estimated value)
+Σ (coefficient kn x WBn estimated value x WB _B estimated value) + Σ (adjusted value Mn)
(However, n = V, C, E, L, R)

In this way, as analysis devices that generate analysis data representing the mental state of user A based on biometric information such as user A's voice and biometric data, the voice analysis device (portable terminal 20) and the measurement device 10 (vehicle-mounted type or portable) can be used. Note that when the measuring device 10 is used as this analysis device, the index value calculated in step S15 of FIG. 5 is the WB _B estimated value.

In the present embodiment, the measurement device 10 and the mobile terminal 20 process the measurement data, transmit the processed data (analysis result data, emotion data, etc.) to the management device 30, and the management device 30 WB values are estimated based on the data. However, the management device 30 may receive measurement data from the measurement device 10 and the mobile terminal 20 and estimate the WB value based on the measurement data.

Next, the virtual currency acquired by user A in relation to event E will be described with reference to FIG. 3B. FIG. 3B is a graph showing changes over time in the user's virtual currency balance. In the LA system S of the present embodiment, a predetermined amount of virtual currency (the unit is "P") is given to the user A according to the estimated WB value, triggered by a predetermined operation of the user A. is configured to

The virtual currency of this embodiment is set to have two types of attributes (attenuation type, unchanging type). In other words, the attributes of the virtual currency can change between decaying virtual currency and immutable virtual currency. Decay-type virtual currency has a time-decay property (for example, it halves in 10 weeks) in which the amount itself decreases with time. The decay rate may be linear, stepwise, or exponential with time. On the other hand, an immutable virtual currency is a non-decaying virtual currency and does not have time decay.

In addition, in the following description, the virtual currency given to the user A is of decay type. In addition, the attribute of decaying virtual currency may be changed from decaying type to immutable type at a predetermined timing.

In the preparation period P1, when user A transmits participation application information from mobile terminal 20 to management device 30 (time t1), management device 30 responds to reception of the participation application information and sends user A a predetermined amount ( For example, 20P) virtual currency is given. That is, at the time of sending the participation application information, it is estimated that the WB value of user A has become higher than usual. The management device 30 adds a predetermined amount to the virtual currency data of the user data. This predetermined amount may be a fixed value, or may be a value corresponding to the participation fee paid by the user A to the event provider B. FIG.

Also, in the action period P2, the management device 30 estimates the WB value for each of the activities J1 to J3 based on the individual evaluation information received from the user A and the analysis result data by voice analysis (time t3, t4, t5 ), and gives the user A a predetermined amount of virtual currency according to the estimated WB value. For example, if the estimated WB value exceeds a predetermined value, a predetermined amount (fixed amount) of virtual currency is given to user A. In FIG. 3B, a predetermined amount of virtual currency (for example, 40P, 50P, and 30P, respectively) is given to activities J1, J2, and J3.

Note that the virtual currency given during the preparation period P1 and the action period P2 is of the decay type. Therefore, attenuation at a predetermined attenuation rate starts from the time of application or after a predetermined non-attenuation period (for example, several days to several weeks) from the time of application.

Also, in the cultivating period P3, regarding the event E, the management device 30 receives the overall evaluation information received from the user A (and, if necessary, the analysis result data of the user's voice at the time of replying the overall evaluation information, WB value is estimated based on various analysis result data). The management device 30 may set the decay rate of the virtual currency in the user data based on the estimated WB value. Specifically, the higher the estimated WB value is than the predetermined value, the lower the attenuation rate can be set.

Also, in the cultivating period P3, in response to the follow-up information, when the user A uses the portable terminal 20 to reply with the thanks transmission information (time t8), the virtual transfer amount F from the user A to the event provider B currency is transferred. That is, in the virtual currency data stored in user A's user data, the management device 30 converts the declining virtual currency of the transfer amount F into the same amount of immutable virtual currency by changing the attribute. Then, the management device 30 subtracts the immutable virtual currency of the transfer amount F from the user data of the user A, and adds the immutable virtual currency to the virtual currency data of the user data of the event provider B as the immutable virtual currency.

It should be noted that the event provider B receives the immutable virtual currency of the transfer amount F on behalf of the event participants. The event provider B can send a thank-you comment from the user A together with the immutable virtual currency of the transfer amount F to one or more target persons specified in the reply of the follow-up information. In this way, by being involved in the operation of event E, event-related people can receive gratitude and obtain economic value. This provides an incentive for event participants to further improve Event E from a WB perspective.

The transfer amount F may be a specified value, or may be a value selected by the user A in the answer item regarding the transfer amount included in the follow-up information. If the transfer amount F is a specified value, for example, the specified value may be set according to the WB value estimated in the general question information (the higher the estimated WB value, the higher the specified value), A predetermined percentage (for example, 30%) of virtual currency acquired by user A in relation to event E may be set as the prescribed value. Further, the virtual currency of the transfer amount F does not have to be subtracted from the virtual currency data of the user A at the time of transfer to the event provider B.

Further, in the fixation period P4, when user A sends back confirmation information using the mobile terminal 20 in response to the stored fixation information (time t8), the management device 30 selects the attenuated virtual currency among user A's virtual currency data. A predetermined amount G of is converted into an immutable virtual currency. In this embodiment, the management device 30 converts a predetermined percentage (0 to 100%) of the decaying virtual currency that was acquired at the event E and that existed in the user's virtual currency data when the confirmation information was received into an immutable virtual currency. Convert currency. This predetermined amount G of immutable virtual currency is a reward for user A becoming an "ambassador".

Note that the predetermined percentage may be a fixed value (eg, 50%) or a variable value. When the predetermined ratio is a variable value, for example, the variable value may be set according to the size of the WB value estimated based on the overall evaluation information. For example, the larger the estimated WB value, the larger the variable value is set.

This system S functions effectively even in volunteer activities where it is not appropriate to give or receive economic value as a reward for participation, and where feelings of gratitude and connections between people are the driving force behind participation. .

Next, with reference to FIGS. 4, 6 and 7, estimation of WB values in activities will be described in more detail. FIG. 6 is an explanatory diagram for estimating the WB value in the activity, and FIG. 7 is a processing flow for calculating the index value.

In FIG. 4, the WB _V estimated value (or WB _B estimated value) is substantially constant during the activity period (time t31 to t32 or t31 to t33) and substantially matches the WB _E estimated value. That is, the WB estimated value based on the user A's objective biological information and the WB estimated value based on the user A's subjective evaluation data match. Therefore, in this situation, the WB values calculated based on these WB estimated values are considered to have relatively high estimation accuracy.

On the other hand, in FIG. 6, the WB _V estimate (or WB _B estimate) is nearly constant during the activity, but does not match the WB _E estimate. That is, there is a large difference between the WB estimated value based on user A's objective biological information and the WB estimated value based on user A's subjective evaluation data. The cause of this large difference may be inaccurate input of evaluation by user A, physical reaction of user A different from usual, and the like. Therefore, in this situation, the WB values calculated based on these WB estimated values are considered to have low estimation accuracy.

Therefore, in the present embodiment, when the WB _V estimated value (or WB _B estimated value) and the WB _E estimated value during the implementation period are approximately the same, the WB value is calculated. Not calculated. That is, in this embodiment, the WB value is estimated only when different types of WB estimation values match and the estimation accuracy is considered to be high.

FIG. 7 shows step S15 of FIG. As shown in FIG. 7, the management device 30 calculates the average value _{WB_A} of the WB _V estimated values during the activity implementation period (S21), compares the average value _{WB_A} with the WB _E estimated value, and calculates the WB _E It is determined whether or not the estimated value is within the same range as the average value _{WB_A} (S22).

In this embodiment, if the WB _E estimated value is within a range of a predetermined ratio a with respect to the average value _{WB_A} , it is determined that the WB _E estimated value is within a range that is approximately the same as the average value _{WB_A} . (eg, _{WB_A} *[1-a] _{≤WB E} estimate≤WB_A*[1+a], 0.4≤a<1). Therefore, if the WB _E estimate is significantly greater than the mean _{WB_A} and/or if the WB _E estimate is significantly less than the mean _{WB_A} , then the WB _E estimate and the mean _{WB_A} do not match. is judged.

If the WB _E estimated value is within the same range as the average value _{WB_A} (S22; Yes), the management device 30 determines that the WB _E estimated value matches the average value _{WB_A} , The average value _{WB_A} is adopted as the index value (S23), and the process proceeds to step S16 (FIG. 5). On the other hand, if the WB _E estimated value is not within the same range as the average value _{WB_A} (S22; No), the management device 30 determines that the WB _E estimated value and the average value _{WB_A} do not match. , the index value is set to "NULL" (S24), and the process ends without proceeding to the process of step S16 (FIG. 5).

Next, the operation of the system of this embodiment will be described.
The system S of this embodiment is a system for estimating the WB value as the mental activity level of the user A who participates in the event E. FIG. The system S includes a management device 30 that estimates the WB value, and an analysis device ( a speech analysis device 20 and a measurement device 10). The management device 30 is configured to calculate the WB value based on the analysis data and the evaluation data that is the degree of subjective evaluation of the event E by the user A. The evaluation data indicates the degree of evaluation of a given activity included in the event E. The management device 30 calculates a first WB estimated value (WB _E estimated value) based on the evaluation data, and calculates a second WB estimated value (WB _V estimated value, WB _B estimated value) based on the analysis data. Then, if the second WB estimate for the predetermined activity matches the first WB estimate (S22; Yes), based on the first WB estimate and the second WB estimate, a predetermined If the WB value for the activity is calculated and the second WB estimate value for the given activity does not match the first WB estimate value (S22; No), the WB value for the given activity is not calculated.

As described above, in the present embodiment, when there is no significant difference between the first WB estimated value and the second WB estimated value, the first WB estimated value, the second WB estimated value, etc. are used. Therefore, a WB value with relatively high estimation accuracy is calculated. On the other hand, in the present embodiment, when there is a significant difference between the first WB estimated value and the second WB estimated value, the estimated accuracy of the calculated WB value is considered to be low. is not calculated. Therefore, in the present embodiment, it is possible to calculate only WB values with relatively high estimation accuracy without calculating WB values with low estimation accuracy.

Further, specifically, when the second WB estimate value does not match the first WB estimate value, the first WB estimate value is a predetermined percentage (for example, 30%) of the second WB estimate value. , 40%, etc.).

Also, specifically, the second WB estimate is the average value _{WB_A} during the predetermined activity period.

In this embodiment, the analysis device is the measurement device 10 that measures biometric data representing the physiological state of the user A and generates analysis data representing the psychological state of the user A based on the biometric data. Furthermore, the analysis device may be a voice analysis device 20 that measures voice uttered by user A and generates analysis data representing the emotional state of user A based on this voice.

S life active system 1 vehicle 3 communication line 10 measuring device (analyzing device)
20 mobile terminal (analysis device)
30 management device

Claims (5)

A system for estimating a well-being value as a mental activity level of a user participating in an event,
a management device that estimates the well-being value;
an analysis device that measures the user's biometric information and generates analysis data representing the user's mental state based on the biometric information;
The management device is configured to calculate the well-being value based on the analysis data and evaluation data that is a degree of user's subjective evaluation of the event,
the evaluation data indicates the degree of evaluation of a predetermined activity included in the event;
The management device
calculating a first well-being estimate based on the evaluation data;
calculating a second well-being estimate based on the analytical data;
if the second estimate of wellbeing for the predetermined activity matches the first estimate of wellbeing, based on the first estimate of wellbeing and the second estimate of wellbeing; calculating the well-being value in the predetermined activity;
A well-being estimation system that does not calculate the well-being value in the given activity if the second well-being estimate for the given activity does not match the first well-being estimate. The case where the second estimated well-being value does not match the first estimated well-being value is the case where the first estimated well-being value is greater than the second estimated well-being value by a predetermined percentage or more. The well-being value estimation system of claim 1, wherein the well-being value estimation system comprises: 2. The wellbeing value estimation system of claim 1, wherein the second wellbeing estimate is an average value over the period of the predetermined activity. 2. The well-being value estimation system according to claim 1, wherein the analysis device is a measurement device that measures biometric data representing the physiological state of the user and generates analytical data representing the psychological state of the user based on the biometric data. . 2. The well-being value estimation system according to claim 1, wherein the analysis device is a speech analysis device that measures speech uttered by the user and generates analytical data representing the user's emotional state based on this speech.

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