System

Abstract

An object of a system according to an embodiment is to grasp mental and emotional environments of a user in real time and take appropriate measures.SOLUTION: A system according to an embodiment includes a history data collection unit, a data analysis unit, an environment quantifying unit, an alert generation unit, and a proposal unit. The history data collection unit collects history data on the user's smartphone. The data-analysis unit analyzes the history-data collected by the history-data-collection unit using the generation AI, and compares the history-data with the database. The environment quantifying unit quantifies the mental and emotional environments based on the result of the analysis by the data analyzing unit. The alert generation unit generates an alert when the environment quantified by the environment quantifying unit exceeds a certain threshold, and notifies the user of the alert. The proposal unit makes a proposal for stopping deterioration of the situation on the basis of the alert generated by the alert generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to grasp the user's mental and emotional environment in real time and take appropriate measures.

[0005] The system according to the embodiment aims to grasp the mental and emotional environment of the user in real time and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a history data collection unit, a data analysis unit, an environment quantification unit, an alert generation unit, and a suggestion unit. The history data collection unit collects history data from the user's smartphone. The data analysis unit analyzes the history data collected by the history data collection unit using a generation AI and compares it with a database. The environment quantification unit quantifies the mental and emotional environment based on the results of the analysis by the data analysis unit. The alert generation unit generates an alert and notifies the user when the environment quantified by the environment quantification unit exceeds a certain threshold. The suggestion unit makes suggestions to prevent the situation from worsening based on the alert generated by the alert generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the mental and emotional environment of the user in real time and take appropriate measures. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The mental and emotional environment monitoring system according to an embodiment of the present invention compares the user's smartphone history with a generating AI or database to optimize the mental and emotional environment. This allows the mental and emotional environment monitoring system to issue an alert before the user's mental or emotional state deteriorates, preventing the situation from worsening.

[0029] A mental and emotional environment monitoring system according to an embodiment includes a history data collection unit, a data analysis unit, an environment quantification unit, an alert generation unit, and a suggestion unit. The history data collection unit collects history data from a user's smartphone, such as websites visited by the user, apps used, messages sent and received, call history, and location information. The history data collection unit also collects sensor information (such as acceleration, gyro, and heart rate) from the smartphone to analyze behavioral patterns in more detail. The data analysis unit uses a generation AI to analyze the history data collected by the history data collection unit and compare it with a database. For example, the generation AI uses an algorithm to detect changes in the user's behavioral patterns and emotions, and identifies history data that indicates behavioral patterns that are likely to indicate stress or emotional instability. The environment quantification unit quantifies the mental and emotional environment based on the results of the analysis by the data analysis unit. For example, the environment quantification unit expresses the user's stress level, happiness, anxiety, etc. as a percentage. The alert generation unit generates an alert and notifies the user when the environment quantified by the environment quantification unit exceeds a certain threshold. For example, an alert is issued if the stress level increases or anxiety increases. The suggestion unit makes suggestions to prevent the situation from worsening based on the alert generated by the alert generation unit. For example, these suggestions include suggestions for relaxation techniques, activities to reduce stress, and recommendations to consult with a specialist. As a result, the mental and emotional environment monitoring system according to the embodiment can monitor the mental and emotional state of the user in real time and prevent the situation from worsening. For example, if the user is feeling stressed or has increasing anxiety, an alert can be issued early and appropriate measures can be taken.

[0030] The history data collection unit collects sensor information from the smartphone and can analyze behavioral patterns in more detail. For example, the history data collection unit uses the user's smartphone's acceleration sensor to collect walking and exercise patterns and analyze changes in behavior. For example, it detects changes in daily walking speed and exercise volume. The history data collection unit also uses the smartphone's gyro sensor to record the user's posture and movements in detail and analyze signs of stress and fatigue. For example, it detects prolonged sitting or unnatural postures. The history data collection unit also uses the smartphone's heart rate sensor to monitor fluctuations in the user's heart rate in real time and analyze signs of stress or tension. For example, it detects sudden increases in heart rate or irregular rhythms. This allows for more detailed behavioral patterns to be analyzed, enabling a more accurate understanding of the user's mental and emotional state.

[0031] The history data collection unit can strengthen the collaboration between devices and also integrate data from a smartwatch or smart home device. For example, the history data collection unit integrates heart rate and sleep data from the smartwatch with historical data from the smartphone to comprehensively analyze the user's health condition. For example, it analyzes sleep quality and heart rate fluctuations. The history data collection unit also collects data from smart home devices (e.g., room temperature and lighting conditions) to analyze the relationship between the user's living environment and behavioral patterns. For example, it analyzes the impact of changes in room temperature on stress. The history data collection unit also collects voice command data from a smart speaker to analyze the user's emotional state and stress level. For example, it analyzes the frequency and content of voice commands. This strengthens the collaboration between devices and enables a more comprehensive understanding of the user's mental and emotional state.

[0032] With the user's permission, the history data collection unit can also collect data from devices of family members or friends to analyze the impact of social interactions. The history data collection unit, for example, collects history data from the smartphones of the user's family and friends and analyzes the impact of social interactions on the user's emotions and behavior. For example, it analyzes call history and message content. The history data collection unit also collects data from the smartwatches of family members and friends and analyzes the impact of joint activities on the user's stress and happiness. For example, it analyzes data on joint exercise and leisure activities. The history data collection unit also collects data from the smart home devices of family members and friends and analyzes the impact of the shared living environment on the user's emotions and behavior. For example, it analyzes shared meals and usage of the living room. By collecting data from the devices of family members and friends, the impact of social interactions can be analyzed and the user's mental and emotional state can be more comprehensively understood.

[0033] The history data collection unit collects data focusing on specific events or seasons, thereby identifying time-specific stress factors. For example, the history data collection unit collects smartphone history data of a user during the New Year holiday period and analyzes the impact of specific events or occasions on stress and emotions. For example, it analyzes data on year-end shopping and New Year's greetings. The history data collection unit also collects smartphone history data of a user during summer vacation and analyzes the impact of travel and leisure activities on emotions and behavior. For example, it analyzes location information at travel destinations and usage of leisure apps. The history data collection unit also collects history data focusing on specific events (e.g., birthdays and anniversaries) and analyzes the impact of the events on emotions and behavior. For example, it analyzes messages and call history before and after the event. By collecting data focusing on specific events or seasons, it is possible to identify time-specific stress factors and gain a more detailed understanding of the user's mental and emotional state.

[0034] The data analysis unit can integrate the user's past health data to perform more accurate analysis. For example, the data analysis unit may integrate the user's medical records into the generation AI and analyze fluctuations in stress and emotions based on past health conditions and treatment history. For example, it may analyze past diagnostic results and prescription drug data. The data analysis unit may also integrate fitness data (e.g., exercise volume and heart rate) into the generation AI to analyze the impact of exercise habits on emotions and stress. For example, it may analyze fluctuations in daily exercise volume and heart rate. The data analysis unit may also integrate past health checkup data into the generation AI to analyze the impact of fluctuations in health indicators (e.g., blood pressure and weight) on emotions and stress. For example, it may analyze the results of regular health checkups. Integrating past health data enables more accurate analysis, allowing for a more accurate understanding of the user's mental and emotional state.

[0035] The data analysis unit can provide personalized analysis results by taking into account the user's individual lifestyle or preferences. For example, the data analysis unit can integrate the user's lifestyle (e.g., eating and sleeping habits) into the generation AI to provide analysis results based on individual behavioral patterns. For example, it can analyze meal contents and sleep time. The data analysis unit can also integrate the user's preferences (e.g., hobbies and interests) into the generation AI to provide analysis results based on individual preferences. For example, it can analyze hobby activities and topics of interest. The data analysis unit can also integrate the user's lifestyle (e.g., work and vacation schedule) into the generation AI to provide analysis results based on individual lifestyle rhythms. For example, it can analyze work schedules and vacation plans. This allows for more personalized analysis results to be provided by taking into account the user's individual lifestyle and preferences.

[0036] The data analysis unit can add data on social media posts or online activities and analyze the relationship between online behavior and emotions. For example, the data analysis unit integrates social media post data into the generation AI to analyze the relationship between online behavior and emotions. For example, it performs sentiment analysis on the content of posts and comments. The data analysis unit also integrates online activities (e.g., website browsing history and online shopping history) into the generation AI to analyze the relationship between online behavior and emotions. For example, it analyzes data on articles viewed and products purchased. The data analysis unit also integrates social media interaction data (e.g., history of likes and shares) into the generation AI to analyze the relationship between online behavior and emotions. For example, it analyzes the frequency and content of interactions. In this way, by adding data on social media posts and online activities, it is possible to analyze the relationship between online behavior and emotions and gain a more detailed understanding of the user's mental and emotional state.

[0037] The data analysis unit can integrate data related to the user's work environment or work content to analyze the impact of workplace stress. For example, the data analysis unit integrates the user's work environment data (e.g., office layout and lighting conditions) into the generation AI to analyze the impact of workplace stress. For example, it analyzes the impact of the office environment on stress. The data analysis unit also integrates the user's work content data (e.g., task type and work progress) into the generation AI to analyze the impact of workplace stress. For example, it analyzes the impact of work load and progress on stress. The data analysis unit also integrates data on interpersonal relationships at work (e.g., communication history with colleagues and superiors) into the generation AI to analyze the impact of workplace stress. For example, it analyzes the impact of communication frequency and content on stress. In this way, by integrating data related to the work environment and work content, the impact of workplace stress can be analyzed and the user's mental and emotional state can be understood in more detail.

[0038] The environment quantifying unit can quantify the user's emotional state in association with other health indicators. For example, the environment quantifying unit quantifies the user's emotional state in association with sleep quality, and analyzes the relationship between emotion and sleep. For example, the environment quantifying unit quantifies the user's emotional state in association with sleep duration and sleep depth, and analyzes the relationship between emotion and sleep. For example, the environment quantifying unit quantifies the user's emotional state in association with dietary content, and analyzes the relationship between emotion and diet. For example, the environment quantifying unit quantifies the user's emotional state in association with the amount of exercise, and analyzes the relationship between emotion and exercise. For example, the environment quantifying unit quantifies the amount of daily exercise and the type of exercise, and analyzes the relationship between emotion and exercise. By quantifying the user's emotional state in association with other health indicators, it is possible to grasp emotional fluctuations in more detail.

[0039] The environment quantifying unit can integrate data from different devices to quantify the user's emotional state. For example, the environment quantifying unit integrates heart rate data from a smartwatch with the user's emotional state and quantifies and analyzes emotional fluctuations. For example, it associates heart rate fluctuations with an emotional score. The environment quantifying unit also integrates exercise data from a fitness tracker with the user's emotional state and quantifies and analyzes emotional fluctuations. For example, it associates the amount of exercise or type of exercise with an emotional score. The environment quantifying unit also integrates environmental data from smart home devices (e.g., room temperature and lighting conditions) with the user's emotional state and quantifies and analyzes emotional fluctuations. For example, it associates room temperature fluctuations with an emotional score. In this way, by integrating and quantifying the user's emotional state with data from different devices, it is possible to grasp emotional fluctuations in more detail.

[0040] The alert generation unit can issue an alert at the optimal timing, taking into account the user's past reactions or behavioral history. The alert generation unit, for example, builds a system that issues alerts at the optimal timing based on the user's past reaction data. For example, an alert is issued during times when stress was high in the past. The alert generation unit also analyzes the user's behavioral history and optimizes the timing of the alert. For example, an alert is issued before stress increases based on past behavioral patterns. The alert generation unit also personalizes the timing of the alert, taking into account the user's past reactions and behavioral history. For example, an alert is issued at a timing that has been effective in the past. In this way, by taking into account the user's past reactions and behavioral history, an alert can be issued at the optimal timing, preventing the situation from worsening.

[0041] The alert generation unit can individualize the content of the alert and provide the most effective message to the user. The alert generation unit, for example, builds a system that personalizes the content of the alert based on the user's past reaction data. For example, it reuses messages that were effective in the past. The alert generation unit also analyzes the user's behavioral history and optimizes the content of the alert. For example, it provides the most effective message based on past behavioral patterns. The alert generation unit also personalizes the content of the alert taking into account the individual needs and preferences of the user. For example, it provides a message that suits the user's preferences. By personalizing the content of the alert, the most effective message can be provided to the user and the situation can be prevented from worsening.

[0042] The alert generation unit can also send notifications to the user's family or friends to encourage support. The alert generation unit builds a system that sends notifications to family and friends when the user's emotional state worsens, for example. For example, a notification is sent to family when stress increases. The alert generation unit also sends messages to family and friends to encourage support depending on the user's emotional state. For example, a notification is sent to friends when anxiety increases. The alert generation unit also sends notifications to family and friends to encourage support when the user's emotional state exceeds a certain threshold. For example, a notification is sent when the emotional score is high. In this way, by sending notifications to the user's family and friends, it is possible to encourage support and prevent the situation from worsening.

[0043] The alert generation unit can provide the content of the alert through different media. The alert generation unit, for example, builds a system that provides the content of the alert through an audio message. For example, when stress increases, the alert generation unit provides audio guidance on how to relax. The alert generation unit also builds a system that provides the content of the alert through a video message. For example, when anxiety increases, the alert generation unit provides video guidance on how to relax. The alert generation unit also builds a system that provides the content of the alert through both a text message and an audio message. For example, when stress increases, the alert generation unit provides text and audio guidance on how to relax. By providing the content of the alert through different media, the user can be notified in the most effective way and the situation can be prevented from worsening.

[0044] The suggestion unit can make the most effective suggestions by taking into account the user's past successful experiences or preferences. The suggestion unit, for example, builds a system that makes the most effective suggestions based on the user's past successful experiences. For example, it re-suggests methods that have been successful in reducing stress in the past. The suggestion unit also takes into account the user's preferences and makes personalized suggestions for relaxation methods and stress reduction methods. For example, it suggests music or activities that the user likes. The suggestion unit also analyzes the user's past behavioral history and makes the most effective suggestions. For example, it re-suggests relaxation methods that have been effective in the past. In this way, by taking into account the user's past successful experiences and preferences, the most effective suggestions can be made and the situation can be prevented from worsening.

[0045] The suggestion unit can provide the suggestion content in stages, allowing the user to carry it out without difficulty. The suggestion unit, for example, builds a system that provides the suggestion content in stages, allowing the user to carry it out without difficulty. For example, it provides step-by-step guidance on stress reduction methods. The suggestion unit also adjusts the suggestion content in stages according to the user's situation. For example, the first suggestion starts with an easy method and gradually increases in difficulty. The suggestion unit also provides the suggestion content in stages, allowing the user to carry it out without difficulty. For example, it provides guidance on relaxation methods in stages, allowing the user to carry it out at their own pace. In this way, by providing the suggestion content in stages, the user can carry it out without difficulty, and it is possible to prevent the situation from worsening.

[0046] The suggestion unit can also share suggestions with the user's family or friends, and work together to improve the situation. The suggestion unit builds a system that shares suggestions with family and friends when the user's emotional state worsens, for example. For example, stress reduction methods are shared with family. The suggestion unit also shares suggestions to encourage support from family and friends depending on the user's emotional state. For example, relaxation methods are shared with friends when anxiety increases. The suggestion unit also shares suggestions with family and friends when the user's emotional state exceeds a certain threshold, and work together to improve the situation. For example, relaxation methods are shared with family when the emotional score is high. In this way, by sharing suggestions with the user's family and friends, it is possible to work together to improve the situation and prevent the situation from worsening.

[0047] The suggestion unit can provide the suggested content in different formats. For example, the suggestion unit may build a system that provides the suggested content in text. For example, it may provide text guidance on stress reduction methods. The suggestion unit may also build a system that provides the suggested content in audio. For example, it may provide audio guidance on relaxation methods. The suggestion unit may also build a system that provides the suggested content in video. For example, it may provide video guidance on stress reduction methods. By providing the suggested content in different formats, it is possible to make suggestions to the user in the most effective way and prevent the situation from worsening.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The historical data collection unit collects historical data from the user's smartphone. For example, it collects the websites the user visited, the apps they used, the messages they sent and received, their call history, and their location information. The historical data collection unit can also collect sensor information (such as acceleration, gyro, and heart rate) from the smartphone to analyze more detailed behavioral patterns. The data analysis unit uses a generation AI to analyze the historical data collected by the historical data collection unit and compare it with a database. For example, the generation AI uses an algorithm to detect changes in the user's behavioral patterns and emotions, identifying behavioral patterns that are likely to indicate stress or historical data that indicates emotional instability. The environment quantification unit quantifies the mental and emotional environment based on the results of the analysis by the data analysis unit. For example, it expresses the user's stress level, happiness, and anxiety as a percentage. The alert generation unit generates an alert and notifies the user when the environment quantified by the environment quantification unit exceeds a certain threshold. For example, it issues an alert when the stress level increases or anxiety increases. The suggestion unit makes suggestions to prevent the situation from worsening based on the alert generated by the alert generation unit. For example, the information includes suggestions for relaxation, activities to reduce stress, and recommendations for consulting a specialist. This allows the mental and emotional environment monitoring system according to the embodiment to monitor the user's mental and emotional state in real time and prevent the situation from worsening. For example, if the user is feeling stressed or experiencing increasing anxiety, an early alert can be issued and appropriate measures can be taken.

[0050] The history data collection unit collects sensor information from the smartphone and can analyze behavioral patterns in more detail. For example, it uses the user's smartphone's acceleration sensor to collect walking and exercise patterns and analyze changes in behavior. For example, it detects changes in daily walking speed and exercise volume. The history data collection unit also uses the smartphone's gyro sensor to record the user's posture and movements in detail and analyze signs of stress and fatigue. For example, it detects prolonged sitting or unnatural postures. The history data collection unit also uses the smartphone's heart rate sensor to monitor fluctuations in the user's heart rate in real time and analyze signs of stress or tension. For example, it detects sudden increases in heart rate or irregular rhythms. This allows for more detailed behavioral patterns to be analyzed, making it possible to more accurately grasp the user's mental and emotional state.

[0051] The history data collection unit can strengthen the collaboration between devices and also integrate data from a smartwatch or smart home device. For example, it can integrate heart rate and sleep data from a smartwatch with historical data from a smartphone to comprehensively analyze the user's health condition. For example, it can analyze sleep quality and heart rate fluctuations. The history data collection unit also collects data from smart home devices (e.g., room temperature and lighting conditions) to analyze the relationship between the user's living environment and behavioral patterns. For example, it can analyze the impact of changes in room temperature on stress. The history data collection unit also collects voice command data from a smart speaker to analyze the user's emotional state and stress level. For example, it can analyze the frequency and content of voice commands. This strengthens the collaboration between devices and enables a more comprehensive understanding of the user's mental and emotional state.

[0052] With the user's permission, the history data collection unit can also collect data from family or friend devices to analyze the impact of social interactions. For example, it can collect history data from the smartphones of the user's family and friends to analyze the impact of social interactions on the user's emotions and behavior. For example, it can analyze call history and message content. The history data collection unit can also collect data from the smartwatches of family and friends to analyze the impact of joint activities on the user's stress and happiness. For example, it can analyze data on joint exercise and leisure activities. The history data collection unit can also collect data from the smart home devices of family and friends to analyze the impact of joint living environments on the user's emotions and behavior. For example, it can analyze joint meals and usage of the living room. By collecting data from family and friends' devices, it is possible to analyze the impact of social interactions and obtain a more comprehensive understanding of the user's mental and emotional state.

[0053] The history data collection unit can collect data focusing on specific events or seasons to identify season-specific stress factors. For example, the unit can collect smartphone history data of a user during the New Year holiday period to analyze the impact of specific events or occasions on stress and emotions. For example, data on year-end shopping and New Year's greetings can be analyzed. The history data collection unit can also collect smartphone history data of a user during summer vacation to analyze the impact of travel and leisure activities on emotions and behavior. For example, the unit can analyze location information at travel destinations and usage of leisure apps. The history data collection unit can also collect history data focusing on specific events (e.g., birthdays and anniversaries) to analyze the impact of the events on emotions and behavior. For example, the unit can analyze messages and call history before and after the event. By collecting data focusing on specific events or seasons, it is possible to identify season-specific stress factors and gain a more detailed understanding of the user's mental and emotional state.

[0054] The data analysis unit can integrate the user's past health data to perform more accurate analysis. For example, it can integrate the user's medical records into the generation AI to analyze stress and emotional fluctuations based on past health conditions and treatment history. For example, it can analyze past diagnostic results and prescription drug data. The data analysis unit can also integrate fitness data (e.g., exercise volume and heart rate) into the generation AI to analyze the impact of exercise habits on emotions and stress. For example, it can analyze daily exercise volume and heart rate fluctuations. The data analysis unit can also integrate past health checkup data into the generation AI to analyze the impact of fluctuations in health indicators (e.g., blood pressure and weight) on emotions and stress. For example, it can analyze the results of regular health checkups. Integrating past health data enables more accurate analysis, allowing for a more accurate understanding of the user's mental and emotional state.

[0055] The data analysis unit can provide personalized analysis results by taking into account the user's individual lifestyle or preferences. For example, the data analysis unit can integrate the user's lifestyle (e.g., eating and sleeping habits) into the generation AI to provide analysis results based on individual behavioral patterns. For example, it can analyze the contents of meals and sleep time. The data analysis unit can also integrate the user's preferences (e.g., hobbies and interests) into the generation AI to provide analysis results based on individual preferences. For example, it can analyze hobby activities and topics of interest. The data analysis unit can also integrate the user's lifestyle (e.g., work and vacation schedule) into the generation AI to provide analysis results based on individual lifestyle rhythms. For example, it can analyze work schedules and vacation plans. This allows the data analysis unit to provide more personalized analysis results by taking into account the user's individual lifestyle and preferences.

[0056] The data analysis unit can add data on social media posts or online activities to analyze the relationship between online behavior and emotions. For example, social media post data can be integrated into the generation AI to analyze the relationship between online behavior and emotions. For example, sentiment analysis can be performed on the content of posts and comments. The data analysis unit can also integrate online activities (e.g., website browsing history and online shopping history) into the generation AI to analyze the relationship between online behavior and emotions. For example, it can analyze data on articles viewed and products purchased. The data analysis unit can also integrate social media interaction data (e.g., like and share history) into the generation AI to analyze the relationship between online behavior and emotions. For example, it can analyze the frequency and content of interactions. By adding data on social media posts and online activities, the relationship between online behavior and emotions can be analyzed, enabling a more detailed understanding of the user's mental and emotional state.

[0057] The data analysis unit can integrate data related to the user's work environment or work content to analyze the impact of workplace stress. For example, the data analysis unit can integrate the user's work environment data (e.g., office layout and lighting conditions) into the generation AI to analyze the impact of workplace stress. For example, it can analyze the impact of the office environment on stress. The data analysis unit can also integrate the user's work content data (e.g., task type and work progress) into the generation AI to analyze the impact of workplace stress. For example, it can analyze the impact of work load and progress on stress. The data analysis unit can also integrate data on interpersonal relationships at work (e.g., communication history with colleagues and superiors) into the generation AI to analyze the impact of workplace stress. For example, it can analyze the impact of communication frequency and content on stress. In this way, by integrating data related to the work environment and work content, the impact of workplace stress can be analyzed and the user's mental and emotional state can be understood in more detail.

[0058] The environment quantifying unit can quantify the user's emotional state in association with other health indicators. For example, the environment quantifying unit can quantify the user's emotional state in association with the quality of sleep, and analyze the relationship between emotions and sleep. For example, the environment quantifying unit can quantify the sleep duration and sleep depth with an emotional score. The environment quantifying unit can quantify the user's emotional state in association with the contents of meals, and analyze the relationship between emotions and meals. For example, the environment quantifying unit can quantify the nutritional balance of meals and meal timing with an emotional score. The environment quantifying unit can quantify the user's emotional state in association with the amount of exercise, and analyze the relationship between emotions and exercise. For example, the environment quantifying unit can quantify the amount of daily exercise and the type of exercise with an emotional score. By quantifying the user's emotional state in association with other health indicators, it is possible to grasp emotional fluctuations in more detail.

[0059] The environmental quantification unit can integrate and quantify the user's emotional state with data from different devices. For example, it can integrate heart rate data from a smartwatch with the user's emotional state and quantify and analyze emotional fluctuations. For example, it can associate heart rate fluctuations with an emotional score. The environmental quantification unit can also integrate exercise data from a fitness tracker with the user's emotional state and quantify and analyze emotional fluctuations. For example, it can associate the amount of exercise or type of exercise with an emotional score. The environmental quantification unit can also integrate environmental data from smart home devices (e.g., room temperature and lighting conditions) with the user's emotional state and quantify and analyze emotional fluctuations. For example, it can associate room temperature fluctuations with an emotional score. In this way, by integrating and quantifying the user's emotional state with data from different devices, it is possible to understand emotional fluctuations in more detail.

[0060] The alert generation unit can issue an alert at the optimal timing, taking into account the user's past reactions or behavioral history. For example, a system can be constructed that issues alerts at the optimal timing based on the user's past reaction data. For example, an alert can be issued during times when stress levels have increased in the past. The alert generation unit can also analyze the user's behavioral history and optimize the timing of the alert. For example, an alert can be issued before stress levels increase based on past behavioral patterns. The alert generation unit can also personalize the timing of the alert, taking into account the user's past reactions and behavioral history. For example, an alert can be issued at a time that has been effective in the past. In this way, by taking into account the user's past reactions and behavioral history, an alert can be issued at the optimal timing, preventing the situation from worsening.

[0061] The alert generation unit can individualize the content of the alert and provide the most effective message to the user. For example, a system can be built that personalizes the content of the alert based on the user's past response data. For example, messages that have been effective in the past can be reused. The alert generation unit can also analyze the user's behavioral history and optimize the content of the alert. For example, the most effective message can be provided based on past behavioral patterns. The alert generation unit can also personalize the content of the alert by taking into account the individual needs and preferences of the user. For example, a message tailored to the user's preferences can be provided. By personalizing the content of the alert, the most effective message can be provided to the user, and the situation can be prevented from worsening.

[0062] The alert generation unit can also send notifications to the user's family or friends to encourage support. For example, a system can be constructed that sends notifications to family and friends when the user's emotional state worsens. For example, a notification is sent to family when stress increases. The alert generation unit also sends messages to family and friends encouraging support depending on the user's emotional state. For example, a notification is sent to friends when anxiety increases. The alert generation unit also sends notifications to family and friends to encourage support when the user's emotional state exceeds a certain threshold. For example, a notification is sent when the emotional score is high. In this way, by sending notifications to the user's family and friends, it is possible to encourage support and prevent the situation from worsening.

[0063] The alert generation unit can provide the content of the alert through different media. For example, a system can be constructed that provides the content of the alert through an audio message. For example, when stress increases, the system can provide audio guidance on how to relax. The alert generation unit can also construct a system that provides the content of the alert through a video message. For example, when anxiety increases, the system can provide video guidance on how to relax. The alert generation unit can also construct a system that provides the content of the alert through both a text message and an audio message. For example, when stress increases, the system can provide text and audio guidance on how to relax. By providing the content of the alert through different media, the user can be notified in the most effective way and the situation can be prevented from worsening.

[0064] The suggestion unit can make the most effective suggestions by taking into account the user's past successful experiences or preferences. For example, a system can be constructed that makes the most effective suggestions based on the user's past successful experiences. For example, it can re-suggest methods that have been successful in reducing stress in the past. The suggestion unit also takes into account the user's preferences and makes personalized suggestions for relaxation methods and stress reduction methods. For example, it can suggest music or activities that the user likes. The suggestion unit can also analyze the user's past behavioral history and make the most effective suggestions. For example, it can re-suggest relaxation methods that have been effective in the past. In this way, by taking into account the user's past successful experiences and preferences, the most effective suggestions can be made, preventing the situation from worsening.

[0065] The suggestion unit can provide suggestions in stages, allowing the user to carry them out without difficulty. For example, a system can be constructed in which suggestions are provided in stages, allowing the user to carry them out without difficulty. For example, step-by-step guidance on stress reduction methods is provided. The suggestion unit also adjusts the suggestions in stages according to the user's situation. For example, the first suggestion starts with an easy method and gradually increases in difficulty. The suggestion unit also provides suggestions in stages, allowing the user to carry them out without difficulty. For example, step-by-step guidance on relaxation methods is provided, allowing the user to carry them out at their own pace. In this way, by providing suggestions in stages, the user can carry them out without difficulty, and it is possible to prevent the situation from worsening.

[0066] The suggestion unit can also share suggestions with the user's family or friends, allowing them to work together to improve the situation. For example, a system can be built that shares suggestions with family and friends when the user's emotional state worsens. For example, stress reduction methods can be shared with family. The suggestion unit can also share suggestions to encourage support from family and friends depending on the user's emotional state. For example, relaxation methods can be shared with friends when anxiety increases. The suggestion unit can also share suggestions with family and friends when the user's emotional state exceeds a certain threshold, allowing them to work together to improve the situation. For example, relaxation methods can be shared with family when the emotional score is high. In this way, by sharing suggestions with the user's family and friends, they can work together to improve the situation and prevent it from worsening.

[0067] The suggestion unit can provide the suggestion content in different formats. For example, a system can be constructed that provides the suggestion content in text. For example, methods for reducing stress can be provided in text. The suggestion unit can also construct a system that provides the suggestion content in audio. For example, methods for relaxation can be provided in audio. The suggestion unit can also construct a system that provides the suggestion content in video. For example, methods for reducing stress can be provided in video. By providing the suggestion content in different formats, it is possible to make suggestions to the user in the most effective way and prevent the situation from worsening.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The history data collection unit collects history data from the user's smartphone, such as the websites the user has visited, the apps they have used, the messages they have sent and received, their call history, and their location information. It also collects information from the smartphone's sensors (acceleration, gyro, heart rate, etc.), enabling more detailed analysis of behavioral patterns. Step 2: The data analysis unit uses the generation AI to analyze the historical data collected by the historical data collection unit and compare it with the database. For example, the generation AI uses an algorithm to detect changes in the user's behavioral patterns and emotions, and identifies historical data that indicates behavioral patterns that are likely to indicate stress or emotional instability. Step 3: The environmental quantification unit quantifies the mental and emotional environment based on the results of the analysis by the data analysis unit. For example, the user's stress level, happiness, anxiety, etc. are expressed as a percentage. Step 4: The alert generation unit generates an alert and notifies the user when the environment quantified by the environment quantification unit exceeds a certain threshold. For example, an alert is issued when stress levels increase or anxiety levels increase. Step 5: The suggestion unit makes suggestions based on the alerts generated by the alert generation unit to prevent the situation from worsening, such as suggestions for relaxation, activities to reduce stress, or recommendations to consult a specialist.

[0070] (Example 2) The mental and emotional environment monitoring system according to an embodiment of the present invention compares the user's smartphone history with a generating AI or database to optimize the mental and emotional environment. This allows the mental and emotional environment monitoring system to issue an alert before the user's mental or emotional state deteriorates, preventing the situation from worsening.

[0071] A mental and emotional environment monitoring system according to an embodiment includes a history data collection unit, a data analysis unit, an environment quantification unit, an alert generation unit, and a suggestion unit. The history data collection unit collects history data from a user's smartphone, such as websites visited by the user, apps used, messages sent and received, call history, and location information. The history data collection unit also collects sensor information (such as acceleration, gyro, and heart rate) from the smartphone to analyze behavioral patterns in more detail. The data analysis unit uses a generation AI to analyze the history data collected by the history data collection unit and compare it with a database. For example, the generation AI uses an algorithm to detect changes in the user's behavioral patterns and emotions, and identifies history data that indicates behavioral patterns that are likely to indicate stress or emotional instability. The environment quantification unit quantifies the mental and emotional environment based on the results of the analysis by the data analysis unit. For example, the environment quantification unit expresses the user's stress level, happiness, anxiety, etc. as a percentage. The alert generation unit generates an alert and notifies the user when the environment quantified by the environment quantification unit exceeds a certain threshold. For example, an alert is issued if the stress level increases or anxiety increases. The suggestion unit makes suggestions to prevent the situation from worsening based on the alert generated by the alert generation unit. For example, these suggestions include suggestions for relaxation techniques, activities to reduce stress, and recommendations to consult with a specialist. As a result, the mental and emotional environment monitoring system according to the embodiment can monitor the mental and emotional state of the user in real time and prevent the situation from worsening. For example, if the user is feeling stressed or has increasing anxiety, an alert can be issued early and appropriate measures can be taken.

[0072] The history data collection unit collects sensor information from the smartphone and can analyze behavioral patterns in more detail. For example, the history data collection unit uses the user's smartphone's acceleration sensor to collect walking and exercise patterns and analyze changes in behavior. For example, it detects changes in daily walking speed and exercise volume. The history data collection unit also uses the smartphone's gyro sensor to record the user's posture and movements in detail and analyze signs of stress and fatigue. For example, it detects prolonged sitting or unnatural postures. The history data collection unit also uses the smartphone's heart rate sensor to monitor fluctuations in the user's heart rate in real time and analyze signs of stress or tension. For example, it detects sudden increases in heart rate or irregular rhythms. This allows for more detailed behavioral patterns to be analyzed, enabling a more accurate understanding of the user's mental and emotional state.

[0073] The history data collection unit can strengthen the collaboration between devices and also integrate data from a smartwatch or smart home device. For example, the history data collection unit integrates heart rate and sleep data from the smartwatch with historical data from the smartphone to comprehensively analyze the user's health condition. For example, it analyzes sleep quality and heart rate fluctuations. The history data collection unit also collects data from smart home devices (e.g., room temperature and lighting conditions) to analyze the relationship between the user's living environment and behavioral patterns. For example, it analyzes the impact of changes in room temperature on stress. The history data collection unit also collects voice command data from a smart speaker to analyze the user's emotional state and stress level. For example, it analyzes the frequency and content of voice commands. This strengthens the collaboration between devices and enables a more comprehensive understanding of the user's mental and emotional state.

[0074] The historical data collection unit can use the emotion estimation function to estimate the user's emotion in real time while using a specific app or website and record the emotion as additional information in the historical data. For example, the historical data collection unit estimates the user's emotion in real time while using a social networking app and records positive or negative emotions in the historical data. For example, it performs emotion analysis of posted content and comments. The historical data collection unit also estimates the user's emotion while browsing a news site and records signs of stress or anxiety in the historical data. For example, it analyzes the content of news articles and the viewing time. The historical data collection unit also estimates the user's emotion while playing a game app and records the user's state of excitement or relaxation in the historical data. For example, it analyzes the game progress and play time. In this way, the user's emotion while using a specific app or website can be estimated in real time and recorded as additional information in the historical data, allowing for a more detailed understanding of the user's emotional state.

[0075] With the user's permission, the history data collection unit can also collect data from devices of family members or friends to analyze the impact of social interactions. The history data collection unit, for example, collects history data from the smartphones of the user's family and friends and analyzes the impact of social interactions on the user's emotions and behavior. For example, it analyzes call history and message content. The history data collection unit also collects data from the smartwatches of family members and friends and analyzes the impact of joint activities on the user's stress and happiness. For example, it analyzes data on joint exercise and leisure activities. The history data collection unit also collects data from the smart home devices of family members and friends and analyzes the impact of the shared living environment on the user's emotions and behavior. For example, it analyzes shared meals and usage of the living room. By collecting data from the devices of family members and friends, the impact of social interactions can be analyzed and the user's mental and emotional state can be more comprehensively understood.

[0076] The history data collection unit collects data focusing on specific events or seasons, thereby identifying time-specific stress factors. For example, the history data collection unit collects smartphone history data of a user during the New Year holiday period and analyzes the impact of specific events or occasions on stress and emotions. For example, it analyzes data on year-end shopping and New Year's greetings. The history data collection unit also collects smartphone history data of a user during summer vacation and analyzes the impact of travel and leisure activities on emotions and behavior. For example, it analyzes location information at travel destinations and usage of leisure apps. The history data collection unit also collects history data focusing on specific events (e.g., birthdays and anniversaries) and analyzes the impact of the events on emotions and behavior. For example, it analyzes messages and call history before and after the event. By collecting data focusing on specific events or seasons, it is possible to identify time-specific stress factors and gain a more detailed understanding of the user's mental and emotional state.

[0077] The historical data collection unit can use the emotion estimation function to estimate the user's emotions when they are in a specific location and analyze the relationship between location information and emotions. For example, the historical data collection unit estimates the user's emotions when they are at work in real time and records signs of stress and tension along with location information. For example, it analyzes emotions during meetings and work at work. The historical data collection unit also estimates the user's emotions when they are at home and records their state of relaxation and happiness along with location information. For example, it analyzes relaxation time at home and time with family. The historical data collection unit also estimates the user's emotions when they are in a specific public facility (e.g., a cafe or gym) and records emotional fluctuations along with location information. For example, it analyzes emotions while relaxing at a cafe or exercising at a gym. In this way, by estimating the user's emotions when they are in a specific location and analyzing the relationship between location information and emotions, it is possible to understand the user's mental and emotional state in more detail.

[0078] The data analysis unit can integrate the user's past health data to perform more accurate analysis. For example, the data analysis unit may integrate the user's medical records into the generation AI and analyze fluctuations in stress and emotions based on past health conditions and treatment history. For example, it may analyze past diagnostic results and prescription drug data. The data analysis unit may also integrate fitness data (e.g., exercise volume and heart rate) into the generation AI to analyze the impact of exercise habits on emotions and stress. For example, it may analyze fluctuations in daily exercise volume and heart rate. The data analysis unit may also integrate past health checkup data into the generation AI to analyze the impact of fluctuations in health indicators (e.g., blood pressure and weight) on emotions and stress. For example, it may analyze the results of regular health checkups. Integrating past health data enables more accurate analysis, allowing for a more accurate understanding of the user's mental and emotional state.

[0079] The data analysis unit can provide personalized analysis results by taking into account the user's individual lifestyle or preferences. For example, the data analysis unit can integrate the user's lifestyle (e.g., eating and sleeping habits) into the generation AI to provide analysis results based on individual behavioral patterns. For example, it can analyze meal contents and sleep time. The data analysis unit can also integrate the user's preferences (e.g., hobbies and interests) into the generation AI to provide analysis results based on individual preferences. For example, it can analyze hobby activities and topics of interest. The data analysis unit can also integrate the user's lifestyle (e.g., work and vacation schedule) into the generation AI to provide analysis results based on individual lifestyle rhythms. For example, it can analyze work schedules and vacation plans. This allows for more personalized analysis results to be provided by taking into account the user's individual lifestyle and preferences.

[0080] The data analysis unit can use the emotion estimation function to track changes in emotions based on the user's history data over the long term and analyze emotional trends. The data analysis unit, for example, tracks changes in emotions based on the user's history data over the long term and analyzes emotional trends. For example, it analyzes monthly fluctuations in emotion scores. The data analysis unit also uses the emotion estimation function to analyze changes in emotions based on the user's history data by season and identify seasonal emotional trends. For example, it analyzes emotional fluctuations in summer and winter. The data analysis unit also tracks changes in emotions based on the user's history data on a yearly basis and analyzes long-term emotional trends. For example, it analyzes fluctuations in emotion scores over the past few years. In this way, by tracking changes in emotions over the long term, it is possible to analyze emotional trends and understand the user's mental and emotional state in more detail.

[0081] The data analysis unit can add data on social media posts or online activities and analyze the relationship between online behavior and emotions. For example, the data analysis unit integrates social media post data into the generation AI to analyze the relationship between online behavior and emotions. For example, it performs sentiment analysis on the content of posts and comments. The data analysis unit also integrates online activities (e.g., website browsing history and online shopping history) into the generation AI to analyze the relationship between online behavior and emotions. For example, it analyzes data on articles viewed and products purchased. The data analysis unit also integrates social media interaction data (e.g., history of likes and shares) into the generation AI to analyze the relationship between online behavior and emotions. For example, it analyzes the frequency and content of interactions. In this way, by adding data on social media posts and online activities, it is possible to analyze the relationship between online behavior and emotions and gain a more detailed understanding of the user's mental and emotional state.

[0082] The data analysis unit can integrate data related to the user's work environment or work content to analyze the impact of workplace stress. For example, the data analysis unit integrates the user's work environment data (e.g., office layout and lighting conditions) into the generation AI to analyze the impact of workplace stress. For example, it analyzes the impact of the office environment on stress. The data analysis unit also integrates the user's work content data (e.g., task type and work progress) into the generation AI to analyze the impact of workplace stress. For example, it analyzes the impact of work load and progress on stress. The data analysis unit also integrates data on interpersonal relationships at work (e.g., communication history with colleagues and superiors) into the generation AI to analyze the impact of workplace stress. For example, it analyzes the impact of communication frequency and content on stress. In this way, by integrating data related to the work environment and work content, the impact of workplace stress can be analyzed and the user's mental and emotional state can be understood in more detail.

[0083] The data analysis unit can use the emotion estimation function to analyze changes in emotions based on the user's history data in comparison with other users and evaluate the relative emotional state. The data analysis unit, for example, compares changes in emotions based on the user's history data with other users and evaluates the relative emotional state. For example, comparisons are made with users of the same age or with the same occupation. The data analysis unit also uses the emotion estimation function to compare changes in emotions based on the user's history data by region and evaluate the relative emotional state. For example, comparisons are made with users living in the same region. The data analysis unit also compares changes in emotions based on the user's history data with other users by time period and evaluates the relative emotional state. For example, comparisons are made with users who are active during the same time period. In this way, by analyzing changes in emotions in comparison with other users, the relative emotional state can be evaluated and the user's mental and emotional state can be understood in more detail.

[0084] The environment quantifying unit can quantify the user's emotional state in detail by dividing it into multiple emotional categories. For example, the environment quantifying unit can quantify the user's emotional state in detail by dividing it into multiple emotional categories, such as joy, sadness, anger, and surprise, and visualize the proportion of each emotion. For example, the environment quantifying unit can display daily emotional fluctuations in a graph. The environment quantifying unit can also analyze the user's emotional state in real time using an emotion estimation function, quantifying the user's emotional state in multiple emotional categories, for example, by analyzing the proportion of emotions in specific events or situations. The environment quantifying unit can also quantify the user's emotional state in detail by dividing it into multiple emotional categories based on the user's historical data, and analyze long-term emotional trends. For example, the environment quantifying unit can analyze the proportion of each emotional category by month. By quantifying the user's emotional state in detail by dividing it into multiple emotional categories, the environment quantifying unit can grasp emotional fluctuations in more detail.

[0085] The environment quantifying unit can analyze the user's emotional state by time of day and visualize emotional fluctuations throughout the day. For example, the environment quantifying unit analyzes the user's emotional state by time of day and visualizes the emotional fluctuations throughout the day by quantifying them. For example, it displays morning, afternoon, and evening emotion scores in a graph. The environment quantifying unit also uses an emotion estimation function to analyze the user's emotional state by time of day in real time and quantify the emotional fluctuations. For example, it analyzes emotional fluctuations during a specific time period. The environment quantifying unit's emotional state by time of day based on the user's historical data and analyzes long-term emotional fluctuations. For example, it analyzes emotion scores by time of day for each week. In this way, by analyzing the user's emotional state by time of day and visualizing the emotional fluctuations throughout the day, it is possible to understand the emotional fluctuations in more detail.

[0086] The environment quantifying unit uses the emotion estimation function to quantify the user's emotional state in real time and capture instantaneous changes in emotions. The environment quantifying unit uses, for example, the emotion estimation function to analyze the user's emotional state in real time and quantify and visualize the instantaneous changes in emotions. For example, the environment quantifying unit displays a real-time emotion score in a graph. The environment quantifying unit also quantifies the user's emotional state in real time and captures instantaneous changes in emotions during specific events or situations. For example, it analyzes emotional changes during a meeting or exercise. The environment quantifying unit also quantifies the user's emotional state based on the real-time emotion estimation data and visualizes the instantaneous changes in emotions. For example, the real-time emotion score is displayed on a dashboard. In this way, by quantifying the user's emotional state in real time and capturing instantaneous changes in emotions, it is possible to understand emotional changes in more detail.

[0087] The environment quantifying unit can quantify the user's emotional state in association with other health indicators. For example, the environment quantifying unit quantifies the user's emotional state in association with sleep quality, and analyzes the relationship between emotion and sleep. For example, the environment quantifying unit quantifies the user's emotional state in association with sleep duration and sleep depth, and analyzes the relationship between emotion and sleep. For example, the environment quantifying unit quantifies the user's emotional state in association with dietary content, and analyzes the relationship between emotion and diet. For example, the environment quantifying unit quantifies the user's emotional state in association with the amount of exercise, and analyzes the relationship between emotion and exercise. For example, the environment quantifying unit quantifies the amount of daily exercise and the type of exercise, and analyzes the relationship between emotion and exercise. By quantifying the user's emotional state in association with other health indicators, it is possible to grasp emotional fluctuations in more detail.

[0088] The environment quantifying unit can integrate data from different devices to quantify the user's emotional state. For example, the environment quantifying unit integrates heart rate data from a smartwatch with the user's emotional state and quantifies and analyzes emotional fluctuations. For example, it associates heart rate fluctuations with an emotional score. The environment quantifying unit also integrates exercise data from a fitness tracker with the user's emotional state and quantifies and analyzes emotional fluctuations. For example, it associates the amount of exercise or type of exercise with an emotional score. The environment quantifying unit also integrates environmental data from smart home devices (e.g., room temperature and lighting conditions) with the user's emotional state and quantifies and analyzes emotional fluctuations. For example, it associates room temperature fluctuations with an emotional score. In this way, by integrating and quantifying the user's emotional state with data from different devices, it is possible to grasp emotional fluctuations in more detail.

[0089] When quantifying the emotional state of the user using the emotion estimation function, the environment quantifying unit can perform more detailed quantification by taking into account the intensity or duration of the emotion. For example, when quantifying the emotional state of the user using the emotion estimation function, the environment quantifying unit performs detailed quantification by taking into account the intensity of the emotion. For example, emotions such as strong joy or deep sadness are expressed numerically. Furthermore, when quantifying the emotional state of the user, the environment quantifying unit performs detailed quantification by taking into account the duration of the emotion. For example, the duration of the emotion is expressed numerically. Furthermore, when quantifying the emotional state of the user based on the emotion estimation data, the environment quantifying unit performs detailed quantification by integrating the intensity and duration of the emotion. For example, an emotion score that takes into account both the intensity and duration is expressed numerically. In this way, by taking into account the intensity and duration of the emotion when quantifying the emotional state of the user, emotional fluctuations can be grasped in more detail.

[0090] The alert generation unit can issue an alert at the optimal timing, taking into account the user's past reactions or behavioral history. The alert generation unit, for example, builds a system that issues alerts at the optimal timing based on the user's past reaction data. For example, an alert is issued during times when stress was high in the past. The alert generation unit also analyzes the user's behavioral history and optimizes the timing of the alert. For example, an alert is issued before stress increases based on past behavioral patterns. The alert generation unit also personalizes the timing of the alert, taking into account the user's past reactions and behavioral history. For example, an alert is issued at a timing that has been effective in the past. In this way, by taking into account the user's past reactions and behavioral history, an alert can be issued at the optimal timing, preventing the situation from worsening.

[0091] The alert generation unit can individualize the content of the alert and provide the most effective message to the user. The alert generation unit, for example, builds a system that personalizes the content of the alert based on the user's past reaction data. For example, it reuses messages that were effective in the past. The alert generation unit also analyzes the user's behavioral history and optimizes the content of the alert. For example, it provides the most effective message based on past behavioral patterns. The alert generation unit also personalizes the content of the alert taking into account the individual needs and preferences of the user. For example, it provides a message that suits the user's preferences. By personalizing the content of the alert, the most effective message can be provided to the user and the situation can be prevented from worsening.

[0092] The alert generation unit uses the emotion estimation function to generate a warning according to the emotional state of the user and can provide notifications in line with emotional fluctuations. The alert generation unit, for example, uses the emotion estimation function to build a system that generates alerts in line with the emotional state of the user. For example, an alert is issued when the emotion score is high. The alert generation unit also analyzes the emotional state of the user in real time and generates alerts in line with emotional fluctuations. For example, an alert is issued when emotions fluctuate rapidly. The alert generation unit also personalizes alerts in line with the emotional state of the user based on the emotion estimation data. For example, an optimal message is provided in line with emotional fluctuations. In this way, by using the emotion estimation function to generate alerts in line with the emotional state of the user and providing notifications in line with emotional fluctuations, it is possible to prevent the situation from worsening.

[0093] The alert generation unit can also send notifications to the user's family or friends to encourage support. The alert generation unit builds a system that sends notifications to family and friends when the user's emotional state worsens, for example. For example, a notification is sent to family when stress increases. The alert generation unit also sends messages to family and friends to encourage support depending on the user's emotional state. For example, a notification is sent to friends when anxiety increases. The alert generation unit also sends notifications to family and friends to encourage support when the user's emotional state exceeds a certain threshold. For example, a notification is sent when the emotional score is high. In this way, by sending notifications to the user's family and friends, it is possible to encourage support and prevent the situation from worsening.

[0094] The alert generation unit can provide the content of the alert through different media. The alert generation unit, for example, builds a system that provides the content of the alert through an audio message. For example, when stress increases, the alert generation unit provides audio guidance on how to relax. The alert generation unit also builds a system that provides the content of the alert through a video message. For example, when anxiety increases, the alert generation unit provides video guidance on how to relax. The alert generation unit also builds a system that provides the content of the alert through both a text message and an audio message. For example, when stress increases, the alert generation unit provides text and audio guidance on how to relax. By providing the content of the alert through different media, the user can be notified in the most effective way and the situation can be prevented from worsening.

[0095] The suggestion unit can make the most effective suggestions by taking into account the user's past successful experiences or preferences. The suggestion unit, for example, builds a system that makes the most effective suggestions based on the user's past successful experiences. For example, it re-suggests methods that have been successful in reducing stress in the past. The suggestion unit also takes into account the user's preferences and makes personalized suggestions for relaxation methods and stress reduction methods. For example, it suggests music or activities that the user likes. The suggestion unit also analyzes the user's past behavioral history and makes the most effective suggestions. For example, it re-suggests relaxation methods that have been effective in the past. In this way, by taking into account the user's past successful experiences and preferences, the most effective suggestions can be made and the situation can be prevented from worsening.

[0096] The suggestion unit can provide the suggestion content in stages, allowing the user to carry it out without difficulty. The suggestion unit, for example, builds a system that provides the suggestion content in stages, allowing the user to carry it out without difficulty. For example, it provides step-by-step guidance on stress reduction methods. The suggestion unit also adjusts the suggestion content in stages according to the user's situation. For example, the first suggestion starts with an easy method and gradually increases in difficulty. The suggestion unit also provides the suggestion content in stages, allowing the user to carry it out without difficulty. For example, it provides guidance on relaxation methods in stages, allowing the user to carry it out at their own pace. In this way, by providing the suggestion content in stages, the user can carry it out without difficulty, and it is possible to prevent the situation from worsening.

[0097] The suggestion unit uses the emotion estimation function to make suggestions according to the user's emotional state and provide measures appropriate for emotional fluctuations. The suggestion unit, for example, uses the emotion estimation function to build a system that makes suggestions according to the user's emotional state. For example, it suggests relaxation methods when the emotion score is high. The suggestion unit also analyzes the user's emotional state in real time and provides measures appropriate for emotional fluctuations. For example, it suggests stress reduction methods when emotions fluctuate suddenly. The suggestion unit also personalizes suggestions according to the user's emotional state based on the emotion estimation data. For example, it provides the optimal relaxation method depending on emotional fluctuations. In this way, by using the emotion estimation function to make suggestions according to the user's emotional state and providing measures appropriate for emotional fluctuations, it is possible to prevent the situation from worsening.

[0098] The suggestion unit can also share suggestions with the user's family or friends, and work together to improve the situation. The suggestion unit builds a system that shares suggestions with family and friends when the user's emotional state worsens, for example. For example, stress reduction methods are shared with family. The suggestion unit also shares suggestions to encourage support from family and friends depending on the user's emotional state. For example, relaxation methods are shared with friends when anxiety increases. The suggestion unit also shares suggestions with family and friends when the user's emotional state exceeds a certain threshold, and work together to improve the situation. For example, relaxation methods are shared with family when the emotional score is high. In this way, by sharing suggestions with the user's family and friends, it is possible to work together to improve the situation and prevent the situation from worsening.

[0099] The suggestion unit can provide the suggested content in different formats. For example, the suggestion unit may build a system that provides the suggested content in text. For example, it may provide text guidance on stress reduction methods. The suggestion unit may also build a system that provides the suggested content in audio. For example, it may provide audio guidance on relaxation methods. The suggestion unit may also build a system that provides the suggested content in video. For example, it may provide video guidance on stress reduction methods. By providing the suggested content in different formats, it is possible to make suggestions to the user in the most effective way and prevent the situation from worsening.

[0100] The suggestion unit uses the emotion estimation function to make suggestions according to the user's emotional state and provide measures appropriate for emotional fluctuations. The suggestion unit, for example, uses the emotion estimation function to build a system that makes suggestions according to the user's emotional state. For example, it suggests relaxation methods when the emotion score is high. The suggestion unit also analyzes the user's emotional state in real time and provides measures appropriate for emotional fluctuations. For example, it suggests stress reduction methods when emotions fluctuate suddenly. The suggestion unit also personalizes suggestions according to the user's emotional state based on the emotion estimation data. For example, it provides the optimal relaxation method depending on emotional fluctuations. In this way, by using the emotion estimation function to make suggestions according to the user's emotional state and providing measures appropriate for emotional fluctuations, it is possible to prevent the situation from worsening.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The historical data collection unit collects historical data from the user's smartphone. For example, it collects the websites the user visited, the apps they used, the messages they sent and received, their call history, and their location information. The historical data collection unit can also collect sensor information (such as acceleration, gyro, and heart rate) from the smartphone to analyze more detailed behavioral patterns. The data analysis unit uses a generation AI to analyze the historical data collected by the historical data collection unit and compare it with a database. For example, the generation AI uses an algorithm to detect changes in the user's behavioral patterns and emotions, identifying behavioral patterns that are likely to indicate stress or historical data that indicates emotional instability. The environment quantification unit quantifies the mental and emotional environment based on the results of the analysis by the data analysis unit. For example, it expresses the user's stress level, happiness, and anxiety as a percentage. The alert generation unit generates an alert and notifies the user when the environment quantified by the environment quantification unit exceeds a certain threshold. For example, it issues an alert when the stress level increases or anxiety increases. The suggestion unit makes suggestions to prevent the situation from worsening based on the alert generated by the alert generation unit. For example, the information includes suggestions for relaxation, activities to reduce stress, and recommendations for consulting a specialist. This allows the mental and emotional environment monitoring system according to the embodiment to monitor the user's mental and emotional state in real time and prevent the situation from worsening. For example, if the user is feeling stressed or experiencing increasing anxiety, an early alert can be issued and appropriate measures can be taken.

[0103] The history data collection unit collects sensor information from the smartphone and can analyze behavioral patterns in more detail. For example, it uses the user's smartphone's acceleration sensor to collect walking and exercise patterns and analyze changes in behavior. For example, it detects changes in daily walking speed and exercise volume. The history data collection unit also uses the smartphone's gyro sensor to record the user's posture and movements in detail and analyze signs of stress and fatigue. For example, it detects prolonged sitting or unnatural postures. The history data collection unit also uses the smartphone's heart rate sensor to monitor fluctuations in the user's heart rate in real time and analyze signs of stress or tension. For example, it detects sudden increases in heart rate or irregular rhythms. This allows for more detailed behavioral patterns to be analyzed, making it possible to more accurately grasp the user's mental and emotional state.

[0104] The history data collection unit can strengthen the collaboration between devices and also integrate data from a smartwatch or smart home device. For example, it can integrate heart rate and sleep data from a smartwatch with historical data from a smartphone to comprehensively analyze the user's health condition. For example, it can analyze sleep quality and heart rate fluctuations. The history data collection unit also collects data from smart home devices (e.g., room temperature and lighting conditions) to analyze the relationship between the user's living environment and behavioral patterns. For example, it can analyze the impact of changes in room temperature on stress. The history data collection unit also collects voice command data from a smart speaker to analyze the user's emotional state and stress level. For example, it can analyze the frequency and content of voice commands. This strengthens the collaboration between devices and enables a more comprehensive understanding of the user's mental and emotional state.

[0105] The historical data collection unit can use the emotion estimation function to estimate a user's emotion in real time while using a specific app or website and record the emotion as additional information in the historical data. For example, the emotion of a user using a social networking app can be estimated in real time and positive or negative emotions can be recorded in the historical data. For example, the emotion analysis of posts and comments can be performed. The historical data collection unit can also estimate a user's emotion while browsing a news site and record signs of stress or anxiety in the historical data. For example, the content of news articles and viewing time can be analyzed. The historical data collection unit can also estimate a user's emotion while playing a game app and record states of excitement or relaxation in the historical data. For example, the game progress and play time can be analyzed. In this way, the emotion of a user while using a specific app or website can be estimated in real time and recorded as additional information in the historical data, allowing for a more detailed understanding of the user's emotional state.

[0106] With the user's permission, the history data collection unit can also collect data from family or friend devices to analyze the impact of social interactions. For example, it can collect history data from the smartphones of the user's family and friends to analyze the impact of social interactions on the user's emotions and behavior. For example, it can analyze call history and message content. The history data collection unit can also collect data from the smartwatches of family and friends to analyze the impact of joint activities on the user's stress and happiness. For example, it can analyze data on joint exercise and leisure activities. The history data collection unit can also collect data from the smart home devices of family and friends to analyze the impact of joint living environments on the user's emotions and behavior. For example, it can analyze joint meals and usage of the living room. By collecting data from family and friends' devices, it is possible to analyze the impact of social interactions and obtain a more comprehensive understanding of the user's mental and emotional state.

[0107] The history data collection unit can collect data focusing on specific events or seasons to identify season-specific stress factors. For example, the unit can collect smartphone history data of a user during the New Year holiday period to analyze the impact of specific events or occasions on stress and emotions. For example, data on year-end shopping and New Year's greetings can be analyzed. The history data collection unit can also collect smartphone history data of a user during summer vacation to analyze the impact of travel and leisure activities on emotions and behavior. For example, the unit can analyze location information at travel destinations and usage of leisure apps. The history data collection unit can also collect history data focusing on specific events (e.g., birthdays and anniversaries) to analyze the impact of the events on emotions and behavior. For example, the unit can analyze messages and call history before and after the event. By collecting data focusing on specific events or seasons, it is possible to identify season-specific stress factors and gain a more detailed understanding of the user's mental and emotional state.

[0108] The historical data collection unit can use the emotion estimation function to estimate the user's emotions when they are in a specific location and analyze the relationship between location information and emotions. For example, the user's emotions when they are at work can be estimated in real time and signs of stress or tension can be recorded along with location information. For example, emotions during meetings or while working at work can be analyzed. The historical data collection unit can also estimate the user's emotions when they are at home and record their state of relaxation or happiness along with location information. For example, it can analyze relaxation time at home or time with family. The historical data collection unit can also estimate the user's emotions when they are in a specific public facility (e.g., a cafe or gym) and record emotional fluctuations along with location information. For example, it can analyze emotions while relaxing at a cafe or exercising at a gym. In this way, by estimating the user's emotions when they are in a specific location and analyzing the relationship between location information and emotions, it is possible to understand the user's mental and emotional state in more detail.

[0109] The data analysis unit can integrate the user's past health data to perform more accurate analysis. For example, it can integrate the user's medical records into the generation AI to analyze stress and emotional fluctuations based on past health conditions and treatment history. For example, it can analyze past diagnostic results and prescription drug data. The data analysis unit can also integrate fitness data (e.g., exercise volume and heart rate) into the generation AI to analyze the impact of exercise habits on emotions and stress. For example, it can analyze daily exercise volume and heart rate fluctuations. The data analysis unit can also integrate past health checkup data into the generation AI to analyze the impact of fluctuations in health indicators (e.g., blood pressure and weight) on emotions and stress. For example, it can analyze the results of regular health checkups. Integrating past health data enables more accurate analysis, allowing for a more accurate understanding of the user's mental and emotional state.

[0110] The data analysis unit can provide personalized analysis results by taking into account the user's individual lifestyle or preferences. For example, the data analysis unit can integrate the user's lifestyle (e.g., eating and sleeping habits) into the generation AI to provide analysis results based on individual behavioral patterns. For example, it can analyze the contents of meals and sleep time. The data analysis unit can also integrate the user's preferences (e.g., hobbies and interests) into the generation AI to provide analysis results based on individual preferences. For example, it can analyze hobby activities and topics of interest. The data analysis unit can also integrate the user's lifestyle (e.g., work and vacation schedule) into the generation AI to provide analysis results based on individual lifestyle rhythms. For example, it can analyze work schedules and vacation plans. This allows the data analysis unit to provide more personalized analysis results by taking into account the user's individual lifestyle and preferences.

[0111] The data analysis unit can use the emotion estimation function to track emotional changes based on the user's history data over the long term and analyze emotional trends. For example, the data analysis unit may track emotional changes based on the user's history data over the long term and analyze emotional trends. For example, the data analysis unit may analyze monthly fluctuations in emotion scores. The data analysis unit may also use the emotion estimation function to analyze emotional changes based on the user's history data by season and identify seasonal emotional trends. For example, the data analysis unit may analyze emotional fluctuations in summer and winter. The data analysis unit may also track emotional changes based on the user's history data on a yearly basis and analyze long-term emotional trends. For example, the data analysis unit may analyze fluctuations in emotion scores over the past few years. In this way, by tracking emotional changes over the long term, emotional trends can be analyzed and the user's mental and emotional state can be understood in more detail.

[0112] The data analysis unit can add data on social media posts or online activities to analyze the relationship between online behavior and emotions. For example, social media post data can be integrated into the generation AI to analyze the relationship between online behavior and emotions. For example, sentiment analysis can be performed on the content of posts and comments. The data analysis unit can also integrate online activities (e.g., website browsing history and online shopping history) into the generation AI to analyze the relationship between online behavior and emotions. For example, it can analyze data on articles viewed and products purchased. The data analysis unit can also integrate social media interaction data (e.g., like and share history) into the generation AI to analyze the relationship between online behavior and emotions. For example, it can analyze the frequency and content of interactions. By adding data on social media posts and online activities, the relationship between online behavior and emotions can be analyzed, enabling a more detailed understanding of the user's mental and emotional state.

[0113] The data analysis unit can integrate data related to the user's work environment or work content to analyze the impact of workplace stress. For example, the data analysis unit can integrate the user's work environment data (e.g., office layout and lighting conditions) into the generation AI to analyze the impact of workplace stress. For example, it can analyze the impact of the office environment on stress. The data analysis unit can also integrate the user's work content data (e.g., task type and work progress) into the generation AI to analyze the impact of workplace stress. For example, it can analyze the impact of work load and progress on stress. The data analysis unit can also integrate data on interpersonal relationships at work (e.g., communication history with colleagues and superiors) into the generation AI to analyze the impact of workplace stress. For example, it can analyze the impact of communication frequency and content on stress. In this way, by integrating data related to the work environment and work content, the impact of workplace stress can be analyzed and the user's mental and emotional state can be understood in more detail.

[0114] The data analysis unit can use the emotion estimation function to analyze changes in emotions based on the user's history data in comparison with other users and evaluate the relative emotional state. For example, the emotion changes based on the user's history data can be compared with other users to evaluate the relative emotional state. For example, a comparison can be made with users of the same age or with the same occupation. The data analysis unit can also use the emotion estimation function to compare changes in emotions based on the user's history data by region and evaluate the relative emotional state. For example, a comparison can be made with users living in the same region. The data analysis unit can also compare changes in emotions based on the user's history data by time period with other users and evaluate the relative emotional state. For example, a comparison can be made with users who are active during the same time period. In this way, by analyzing changes in emotions in comparison with other users, the relative emotional state can be evaluated and the user's mental and emotional state can be understood in more detail.

[0115] The environment quantifying unit can quantify the user's emotional state in detail by dividing it into multiple emotional categories. For example, the user's emotional state can be quantified by dividing it into multiple emotional categories, such as joy, sadness, anger, and surprise, and the proportion of each emotion can be visualized. For example, the environment quantifying unit can display daily emotional fluctuations in a graph. The environment quantifying unit can also analyze the user's emotional state in real time using an emotion estimation function, quantifying it into multiple emotional categories, for example, by analyzing the proportion of emotions in specific events or situations. The environment quantifying unit can also quantify the user's emotional state in detail by dividing it into multiple emotional categories based on the user's historical data, and analyze long-term emotional trends. For example, the environment quantifying unit can analyze the proportion of each emotional category by month. This allows the user's emotional state to be quantified in detail by dividing it into multiple emotional categories, enabling the user to understand emotional fluctuations in more detail.

[0116] The environment quantifying unit can analyze the user's emotional state by time of day and visualize emotional fluctuations throughout the day. For example, the environment quantifying unit can analyze the user's emotional state by time of day and visualize the emotional fluctuations throughout the day by quantifying them. For example, the environment quantifying unit can display morning, afternoon, and evening emotion scores in a graph. The environment quantifying unit can also use an emotion estimation function to analyze the user's emotional state by time of day in real time and quantify the emotional fluctuations. For example, the environment quantifying unit can analyze the emotional state by time of day based on the user's historical data and analyze long-term emotional fluctuations. For example, the environment quantifying unit can analyze the emotional state by time of day by analyzing the emotion score by time of day for each week. This allows the user to understand the emotional fluctuations in more detail by analyzing the user's emotional state by time of day and visualizing the emotional fluctuations throughout the day.

[0117] The environment quantification unit uses the emotion estimation function to quantify the user's emotional state in real time and capture instantaneous changes in emotions. For example, the emotion estimation function is used to analyze the user's emotional state in real time and quantify and visualize the instantaneous changes in emotions. For example, the real-time emotion score is displayed in a graph. The environment quantification unit also quantifies the user's emotional state in real time and captures instantaneous changes in emotions during specific events or situations. For example, it analyzes emotional changes during a meeting or while exercising. The environment quantification unit also quantifies the user's emotional state based on the real-time emotion estimation data and visualizes the instantaneous changes in emotions. For example, the real-time emotion score is displayed on a dashboard. In this way, by quantifying the user's emotional state in real time and capturing instantaneous changes in emotions, it is possible to understand emotional changes in more detail.

[0118] The environment quantifying unit can quantify the user's emotional state in association with other health indicators. For example, the environment quantifying unit can quantify the user's emotional state in association with the quality of sleep, and analyze the relationship between emotions and sleep. For example, the environment quantifying unit can quantify the sleep duration and sleep depth with an emotional score. The environment quantifying unit can quantify the user's emotional state in association with the contents of meals, and analyze the relationship between emotions and meals. For example, the environment quantifying unit can quantify the nutritional balance of meals and meal timing with an emotional score. The environment quantifying unit can quantify the user's emotional state in association with the amount of exercise, and analyze the relationship between emotions and exercise. For example, the environment quantifying unit can quantify the amount of daily exercise and the type of exercise with an emotional score. By quantifying the user's emotional state in association with other health indicators, it is possible to grasp emotional fluctuations in more detail.

[0119] The environmental quantification unit can integrate and quantify the user's emotional state with data from different devices. For example, it can integrate heart rate data from a smartwatch with the user's emotional state and quantify and analyze emotional fluctuations. For example, it can associate heart rate fluctuations with an emotional score. The environmental quantification unit can also integrate exercise data from a fitness tracker with the user's emotional state and quantify and analyze emotional fluctuations. For example, it can associate the amount of exercise or type of exercise with an emotional score. The environmental quantification unit can also integrate environmental data from smart home devices (e.g., room temperature and lighting conditions) with the user's emotional state and quantify and analyze emotional fluctuations. For example, it can associate room temperature fluctuations with an emotional score. In this way, by integrating and quantifying the user's emotional state with data from different devices, it is possible to understand emotional fluctuations in more detail.

[0120] When quantifying the emotional state of the user using the emotion estimation function, the environment quantifying unit can perform more detailed quantification by taking into account the intensity or duration of the emotion. For example, when quantifying the emotional state of the user using the emotion estimation function, detailed quantification is performed by taking into account the intensity of the emotion. For example, emotions such as strong joy or deep sadness are expressed numerically. Furthermore, when quantifying the emotional state of the user, the environment quantifying unit performs detailed quantification by taking into account the duration of the emotion. For example, the duration of the emotion is expressed numerically. Furthermore, when quantifying the emotional state of the user based on the emotion estimation data, the environment quantifying unit performs detailed quantification by integrating the intensity and duration of the emotion. For example, an emotion score that takes into account both the intensity and duration is expressed numerically. In this way, by taking into account the intensity and duration of the emotion when quantifying the emotional state of the user, emotional fluctuations can be grasped in more detail.

[0121] The alert generation unit can issue an alert at the optimal timing, taking into account the user's past reactions or behavioral history. For example, a system can be constructed that issues alerts at the optimal timing based on the user's past reaction data. For example, an alert can be issued during times when stress levels have increased in the past. The alert generation unit can also analyze the user's behavioral history and optimize the timing of the alert. For example, an alert can be issued before stress levels increase based on past behavioral patterns. The alert generation unit can also personalize the timing of the alert, taking into account the user's past reactions and behavioral history. For example, an alert can be issued at a time that has been effective in the past. In this way, by taking into account the user's past reactions and behavioral history, an alert can be issued at the optimal timing, preventing the situation from worsening.

[0122] The alert generation unit can individualize the content of the alert and provide the most effective message to the user. For example, a system can be built that personalizes the content of the alert based on the user's past response data. For example, messages that have been effective in the past can be reused. The alert generation unit can also analyze the user's behavioral history and optimize the content of the alert. For example, the most effective message can be provided based on past behavioral patterns. The alert generation unit can also personalize the content of the alert by taking into account the individual needs and preferences of the user. For example, a message tailored to the user's preferences can be provided. By personalizing the content of the alert, the most effective message can be provided to the user, and the situation can be prevented from worsening.

[0123] The alert generation unit uses the emotion estimation function to generate a warning according to the emotional state of the user and can provide notifications in line with emotional fluctuations. For example, a system is constructed that uses the emotion estimation function to generate alerts in line with the emotional state of the user. For example, an alert is issued when the emotion score is high. The alert generation unit also analyzes the emotional state of the user in real time and generates alerts in line with emotional fluctuations. For example, an alert is issued when emotions fluctuate rapidly. The alert generation unit also personalizes alerts in line with the emotional state of the user based on the emotion estimation data. For example, an optimal message is provided in line with emotional fluctuations. In this way, by using the emotion estimation function to generate alerts in line with the emotional state of the user and providing notifications in line with emotional fluctuations, it is possible to prevent the situation from worsening.

[0124] The alert generation unit can also send notifications to the user's family or friends to encourage support. For example, a system can be constructed that sends notifications to family and friends when the user's emotional state worsens. For example, a notification is sent to family when stress increases. The alert generation unit also sends messages to family and friends encouraging support depending on the user's emotional state. For example, a notification is sent to friends when anxiety increases. The alert generation unit also sends notifications to family and friends to encourage support when the user's emotional state exceeds a certain threshold. For example, a notification is sent when the emotional score is high. In this way, by sending notifications to the user's family and friends, it is possible to encourage support and prevent the situation from worsening.

[0125] The alert generation unit can provide the content of the alert through different media. For example, a system can be constructed that provides the content of the alert through an audio message. For example, when stress increases, the system can provide audio guidance on how to relax. The alert generation unit can also construct a system that provides the content of the alert through a video message. For example, when anxiety increases, the system can provide video guidance on how to relax. The alert generation unit can also construct a system that provides the content of the alert through both a text message and an audio message. For example, when stress increases, the system can provide text and audio guidance on how to relax. By providing the content of the alert through different media, the user can be notified in the most effective way and the situation can be prevented from worsening.

[0126] The suggestion unit can make the most effective suggestions by taking into account the user's past successful experiences or preferences. For example, a system can be constructed that makes the most effective suggestions based on the user's past successful experiences. For example, it can re-suggest methods that have been successful in reducing stress in the past. The suggestion unit also takes into account the user's preferences and makes personalized suggestions for relaxation methods and stress reduction methods. For example, it can suggest music or activities that the user likes. The suggestion unit can also analyze the user's past behavioral history and make the most effective suggestions. For example, it can re-suggest relaxation methods that have been effective in the past. In this way, by taking into account the user's past successful experiences and preferences, the most effective suggestions can be made, preventing the situation from worsening.

[0127] The suggestion unit can provide suggestions in stages, allowing the user to carry them out without difficulty. For example, a system can be constructed in which suggestions are provided in stages, allowing the user to carry them out without difficulty. For example, step-by-step guidance on stress reduction methods is provided. The suggestion unit also adjusts the suggestions in stages according to the user's situation. For example, the first suggestion starts with an easy method and gradually increases in difficulty. The suggestion unit also provides suggestions in stages, allowing the user to carry them out without difficulty. For example, step-by-step guidance on relaxation methods is provided, allowing the user to carry them out at their own pace. In this way, by providing suggestions in stages, the user can carry them out without difficulty, and it is possible to prevent the situation from worsening.

[0128] The suggestion unit can use the emotion estimation function to make suggestions according to the user's emotional state and provide measures appropriate to emotional fluctuations. For example, a system can be constructed using the emotion estimation function to make suggestions according to the user's emotional state. For example, a relaxation method can be suggested when the emotion score is high. The suggestion unit can also analyze the user's emotional state in real time and provide measures appropriate to emotional fluctuations. For example, a stress reduction method can be suggested when emotions fluctuate rapidly. The suggestion unit can also personalize suggestions according to the user's emotional state based on the emotion estimation data. For example, an optimal relaxation method can be provided depending on emotional fluctuations. In this way, the emotion estimation function can be used to make suggestions according to the user's emotional state and provide measures appropriate to emotional fluctuations, thereby preventing the situation from worsening.

[0129] The suggestion unit can also share suggestions with the user's family or friends, allowing them to work together to improve the situation. For example, a system can be built that shares suggestions with family and friends when the user's emotional state worsens. For example, stress reduction methods can be shared with family. The suggestion unit can also share suggestions to encourage support from family and friends depending on the user's emotional state. For example, relaxation methods can be shared with friends when anxiety increases. The suggestion unit can also share suggestions with family and friends when the user's emotional state exceeds a certain threshold, allowing them to work together to improve the situation. For example, relaxation methods can be shared with family when the emotional score is high. In this way, by sharing suggestions with the user's family and friends, they can work together to improve the situation and prevent it from worsening.

[0130] The suggestion unit can provide the suggestion content in different formats. For example, a system can be constructed that provides the suggestion content in text. For example, methods for reducing stress can be provided in text. The suggestion unit can also construct a system that provides the suggestion content in audio. For example, methods for relaxation can be provided in audio. The suggestion unit can also construct a system that provides the suggestion content in video. For example, methods for reducing stress can be provided in video. By providing the suggestion content in different formats, it is possible to make suggestions to the user in the most effective way and prevent the situation from worsening.

[0131] The suggestion unit can use the emotion estimation function to make suggestions according to the user's emotional state and provide measures appropriate to emotional fluctuations. For example, a system can be constructed using the emotion estimation function to make suggestions according to the user's emotional state. For example, a relaxation method can be suggested when the emotion score is high. The suggestion unit can also analyze the user's emotional state in real time and provide measures appropriate to emotional fluctuations. For example, a stress reduction method can be suggested when emotions fluctuate rapidly. The suggestion unit can also personalize suggestions according to the user's emotional state based on the emotion estimation data. For example, an optimal relaxation method can be provided depending on emotional fluctuations. In this way, the emotion estimation function can be used to make suggestions according to the user's emotional state and provide measures appropriate to emotional fluctuations, thereby preventing the situation from worsening.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The history data collection unit collects history data from the user's smartphone, such as the websites the user has visited, the apps they have used, the messages they have sent and received, their call history, and their location information. It also collects information from the smartphone's sensors (acceleration, gyro, heart rate, etc.), enabling more detailed analysis of behavioral patterns. Step 2: The data analysis unit uses the generation AI to analyze the historical data collected by the historical data collection unit and compare it with the database. For example, the generation AI uses an algorithm to detect changes in the user's behavioral patterns and emotions, and identifies historical data that indicates behavioral patterns that are likely to indicate stress or emotional instability. Step 3: The environmental quantification unit quantifies the mental and emotional environment based on the results of the analysis by the data analysis unit. For example, the user's stress level, happiness, anxiety, etc. are expressed as a percentage. Step 4: The alert generation unit generates an alert and notifies the user when the environment quantified by the environment quantification unit exceeds a certain threshold. For example, an alert is issued when stress levels increase or anxiety levels increase. Step 5: The suggestion unit makes suggestions based on the alerts generated by the alert generation unit to prevent the situation from worsening, such as suggestions for relaxation, activities to reduce stress, or recommendations to consult a specialist.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0153] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0159] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0174] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0175] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0176] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0177] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0178] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0179] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0180] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0181] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0182] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0183] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0184] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0185] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0186] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0187] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0188] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0190] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0191] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0192] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0193] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0194] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0195] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0196] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0197] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0198] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0199] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0200] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A history data collection unit that collects history data on the user's smartphone; a data analysis unit that analyzes the history data collected by the history data collection unit using a generation AI and compares the data with a database; an environment quantifying unit that quantifies the mental and emotional environment based on the results of the analysis by the data analyzing unit; an alert generation unit that generates an alert and notifies a user when the environment quantified by the environment quantification unit exceeds a certain threshold; a suggestion unit that makes a suggestion to prevent the situation from worsening based on the alert generated by the alert generation unit. A system characterized by:

2. The history data collection unit Collecting smartphone sensor information and analyzing more detailed behavioral patterns 2. The system of claim 1.

3. The history data collection unit Enhanced device integration, integrating data from smartwatches or smart home devices 2. The system of claim 1.

4. The history data collection unit Estimates users' emotions in real time while using a specific app or website, and records this as additional information in their historical data.

2. The system of claim 1.

5. The history data collection unit With your permission, collect data from your family or friends' devices to analyze the impact of your social interactions 2. The system of claim 1.

6. The history data collection unit Focus data on specific events or seasons to identify time-specific stressors 2. The system of claim 1.

7. The history data collection unit Estimate the user's emotions when they are in a specific location and analyze the relationship between location information and emotions 2. The system of claim 1.

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