system

Abstract

The system according to this embodiment aims to monitor the user's emotions and stress in real time and provide appropriate support. [Solution] The system according to the embodiment comprises a monitoring unit, a support unit, an evaluation unit, an advice unit, and a platform unit. The monitoring unit monitors the user's emotions and stress. The support unit provides customized support based on the data monitored by the monitoring unit. The evaluation unit identifies mental risk factors using a self-assessment tool. The advice unit provides expert advice based on the risk factors identified by the evaluation unit. The platform unit provides an online platform.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to monitor the user's emotions and stress in real time and provide appropriate support.

[0005] The system according to the embodiment aims to monitor the user's emotions and stress in real time and provide appropriate support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a support unit, an evaluation unit, an advice unit, and a platform unit. The monitoring unit monitors the user's emotions and stress. The support unit provides customized support based on the data monitored by the monitoring unit. The evaluation unit identifies mental risk factors using a self-assessment tool. The advice unit provides expert advice based on the risk factors identified by the evaluation unit. The platform unit provides an online platform. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the user's emotions and stress in real time and provide appropriate support. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The mental health prevention program according to an embodiment of the present invention is a system that uses AI to monitor a user's emotions and stress and provides customized support in real time. This system monitors the user's emotions and stress 24 hours a day, provides morning mood checks and relaxation guides before bedtime, and responds quickly to sudden stress signs. Furthermore, it allows users to identify mental risk factors using self-assessment tools and cultivate mental health with expert advice. It can be easily accessed and used from home or work via an online platform. For example, the user records their daily emotions and stress levels using a smartphone or wearable device. The AI ​​agent analyzes this data in real time to understand changes in the user's emotions and stress. Next, the AI ​​agent provides morning mood checks and relaxation guides before bedtime. For example, the user performs a mood check when they wake up in the morning to check their stress level for the day. It also provides relaxation guides before bedtime to help the user relax and sleep. Furthermore, the AI ​​agent responds quickly to sudden stress signs. For example, if the user suddenly feels stressed, the AI ​​agent detects the sign and immediately provides a solution. This allows the user to reduce stress early. It is also important to identify mental health risk factors using self-assessment tools. Users use these tools to identify their emotional and stress risk factors. Based on this information, the AI ​​agent provides users with the most suitable support. Furthermore, users can cultivate their mental health while receiving expert advice. The AI ​​agent collaborates with psychological counselors and psychiatrists as needed to provide professional support. This allows users to maintain their mental health with expert guidance. The online platform makes it easy to access and use from home or work. Users can access this system anytime, anywhere using their PC or smartphone. This allows users to manage their mental health at their own pace.For example, if a user experiences stress at work or school, the AI ​​agent can provide real-time support to alleviate that stress. The AI ​​agent can also provide appropriate support for family problems and financial anxieties. This allows users to protect their mental health and live fulfilling lives. In this way, mental health prevention programs can maintain mental well-being by efficiently monitoring users' emotions and stress levels and providing customized support.

[0029] The mental health prevention program according to this embodiment comprises a monitoring unit, a support unit, an evaluation unit, an advice unit, and a platform unit. The monitoring unit monitors the user's emotions and stress. For example, the monitoring unit records the user's daily emotions and stress levels using a smartphone or wearable device. The monitoring unit analyzes this data in real time to understand changes in the user's emotions and stress. Some or all of the above processing in the monitoring unit may be performed using AI or not. The support unit provides customized support based on the data monitored by the monitoring unit. For example, the support unit provides a morning mood check and a relaxation guide before bedtime. The support unit performs a mood check when the user wakes up in the morning and checks their stress level for the day. The support unit also provides a relaxation guide before bedtime to help the user relax and sleep. Some or all of the above processing in the support unit may be performed using AI or not. The evaluation unit identifies mental risk factors using a self-assessment tool. For example, the evaluation unit has the user identify their own emotional and stress risk factors using a self-assessment tool. The evaluation unit provides optimal support to users based on this information. Some or all of the processes described above in the evaluation unit may be performed using AI or not. The advice unit provides expert advice based on the risk factors identified by the evaluation unit. The advice unit provides expert support, for example, by collaborating with psychological counselors or psychiatrists. Some or all of the processes described above in the advice unit may be performed using AI or not. The platform unit provides an online platform. The platform unit allows users to access this system anytime, anywhere using a PC or smartphone, for example. Some or all of the processes described above in the platform unit may be performed using AI or not.As a result, the mental health prevention program according to this embodiment can maintain the user's mental health by efficiently monitoring their emotions and stress and providing customized support.

[0030] The monitoring unit monitors the user's emotions and stress levels. For example, the unit records the user's daily emotions and stress levels using smartphones or wearable devices. Specifically, it collects the user's heart rate, sleep patterns, activity levels, and even changes in voice and facial expressions through smartphone applications and wearable device sensors. This data is sent to a cloud server in real time for analysis by AI. The AI ​​uses machine learning algorithms to learn the user's emotional and stress patterns and detect abnormal changes or increases in stress. For example, if a user's heart rate remains higher than normal for an extended period, or if their sleep quality deteriorates, the AI ​​recognizes this as a sign of stress and notifies the user. It can also use voice analysis technology to detect changes in the user's voice tone and speaking style, thereby understanding changes in emotions. This allows the monitoring unit to grasp changes in the user's emotions and stress in real time and respond early. Furthermore, the monitoring unit can store the collected data long-term and analyze trends in the user's emotions and stress. This allows for continuous monitoring of the user's mental health and the provision of appropriate support as needed.

[0031] The support department provides customized support based on data monitored by the monitoring department. For example, the support department offers morning mood checks and bedtime relaxation guides. Specifically, users perform a mood check via a smartphone application upon waking to determine their stress level for the day. Based on the user's responses and data from the monitoring department, the AI ​​provides advice for stress management throughout the day. For instance, if a user reports a high stress level, the AI ​​provides guidance on relaxation breathing techniques and simple stretches. Before bedtime, it also provides guidance on meditation and relaxation music to help users relax and fall asleep. This allows users to relax at the end of the day and achieve high-quality sleep. Furthermore, the support department can create customized support plans tailored to the user's lifestyle and preferences. For example, if a user enjoys exercise, it can suggest an exercise program for stress relief. This allows the support department to provide individualized support tailored to the user's needs and help maintain their mental health.

[0032] The assessment department uses self-assessment tools to identify mental health risk factors. For example, users use self-assessment tools to identify their emotional and stress risk factors. Specifically, users regularly answer self-assessment questionnaires to record their emotional and stress levels. AI analyzes these responses to identify the user's risk factors. For example, if a user frequently experiences anxiety or irritability, the AI ​​recognizes this as a risk factor and suggests appropriate support. The assessment department can also analyze fluctuations in risk factors based on the user's past data and behavioral patterns. This allows for continuous assessment of the user's mental health status and early detection of risks. Furthermore, the assessment department can create individualized support plans based on the user's self-assessment results. This enables the assessment department to identify the user's mental health risk factors and build a foundation for providing appropriate support.

[0033] The Advice Department provides expert advice based on risk factors identified by the Assessment Department. For example, the Advice Department collaborates with psychological counselors and psychiatrists to provide professional support. Specifically, psychological counselors and psychiatrists provide individualized advice based on risk factors identified by the user through self-assessment tools. AI analyzes user data and matches users with appropriate professionals. For example, if a user reports a high stress level, the AI ​​recommends a stress management specialist and sets up an online counseling session. The Advice Department also provides access to expert advice through an online platform, allowing users to receive professional support anytime, anywhere. Furthermore, the Advice Department can collect user feedback and continuously improve the quality of its advice. This enables the Advice Department to provide users with professional and effective support and help them maintain their mental health.

[0034] The Platform Division provides an online platform. For example, the Platform Division allows users to access the system anytime, anywhere using a PC or smartphone. Specifically, users can access the functions of the Monitoring Division, Support Division, Evaluation Division, and Advice Division through a dedicated application or website. The platform centrally manages user data, enabling each department to quickly access the information they need. AI analyzes user data and creates individualized support plans. The platform also provides online counseling functionality for users to connect with experts, allowing them to receive professional support from the comfort of their homes. Furthermore, the platform implements data encryption and access control to protect user privacy, ensuring users can use the system with peace of mind. The Platform Division provides a foundation to enhance user convenience and support the maintenance of mental health.

[0035] The monitoring unit can monitor users' emotions and stress levels 24 hours a day. For example, the monitoring unit records users' daily emotions and stress levels using smartphones or wearable devices. The monitoring unit analyzes this data in real time to understand changes in the user's emotions and stress levels. By monitoring users' emotions and stress levels 24 hours a day, the monitoring unit can respond quickly. Some or all of the above processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion and stress data into a generating AI, which can then analyze it in real time to understand changes in the user's emotions and stress levels.

[0036] The support unit can provide morning mood checks and bedtime relaxation guides. For example, the support unit can perform a mood check when the user wakes up in the morning to check their stress level for the day. The support unit can also provide a relaxation guide before bedtime to help the user relax and sleep. By providing appropriate support at the beginning and end of the user's day, the support unit helps maintain their mental health. Some or all of the processes described above in the support unit may be performed using AI or not. For example, the support unit can input the user's mood check data into a generating AI, which can then analyze the user's stress level and provide an appropriate relaxation guide.

[0037] The support unit can quickly respond to sudden stress signs. For example, if a user suddenly feels stressed, the support unit will detect the sign and immediately provide a solution. By responding quickly to sudden stress, the support unit reduces the user's stress. Some or all of the above processes in the support unit may be performed using AI or not. For example, the support unit can input the user's stress sign data into a generating AI, which can then analyze the stress signs and provide an appropriate solution.

[0038] The evaluation unit can identify mental health risk factors using a self-assessment tool. For example, the evaluation unit allows users to identify their emotional and stress risk factors using a self-assessment tool. Based on this information, the evaluation unit provides the user with the most appropriate support. By using the self-assessment tool, the evaluation unit identifies the user's mental health risk factors and provides appropriate support. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the user's self-assessment data into a generating AI, which can then analyze the risk factors and provide appropriate support.

[0039] The advice unit can provide expert advice. For example, the advice unit can collaborate with psychological counselors or psychiatrists to provide professional support. By providing expert advice, the advice unit professionally supports the user's mental health. Some or all of the above processes in the advice unit may be performed using AI or not. For example, the advice unit can input the user's risk factor data into a generating AI, which can then generate expert advice and provide it to the user.

[0040] The platform unit can provide an online platform, allowing users to easily access it from home or work. For example, the platform unit can enable users to access the system anytime, anywhere using a PC or smartphone. By providing an online platform, the platform unit ensures easy access from anywhere. Some or all of the above-described processes in the platform unit may be performed using AI or not. For example, the platform unit can input user access data into a generating AI, which can then provide the optimal access method.

[0041] The monitoring unit can prioritize monitoring highly relevant data by considering the user's geographical location during monitoring. For example, if the user is in a specific location, the monitoring unit will prioritize monitoring stressors associated with that location. If the user is traveling, the monitoring unit will prioritize monitoring data related to the environment of the travel destination. If the user is at home, the monitoring unit will prioritize monitoring data related to the home environment. In this way, by considering the user's geographical location, highly relevant data can be prioritized. Some or all of the above processing in the monitoring unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the monitoring unit can input the user's geographical location information into a generative AI, which can then prioritize monitoring highly relevant data.

[0042] The monitoring unit can analyze the user's social media activity and monitor related data during monitoring. For example, the monitoring unit can analyze the user's social media posts and monitor stress and emotional changes. The monitoring unit can analyze the user's social media interactions and identify stressors. The monitoring unit can monitor the frequency of the user's social media activity and grasp emotional fluctuations. In this way, by analyzing the user's social media activity, it is possible to monitor related data. Some or all of the above processing in the monitoring unit may be performed using or without a generative AI. For example, the monitoring unit can input the user's social media data into a generative AI, and the generative AI can monitor the related data.

[0043] The support unit can analyze the user's past stress responses and select the optimal support method when providing support. For example, the support unit can suggest effective relaxation methods based on the user's past stress responses. The support unit analyzes the user's stress response patterns and selects appropriate support methods. The support unit refers to the user's past stress responses and provides support tailored to specific situations. In this way, by analyzing past stress responses, the support unit can provide the user with the most suitable support method. Some or all of the above processes in the support unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the support unit can input the user's past stress response data into a generative AI, which can then select the optimal support method.

[0044] The support unit can customize the means of support provided based on the user's current living situation. For example, the support unit can provide support according to the user's work or school schedule. The support unit can suggest appropriate support methods according to the user's home environment. The support unit can provide the optimal means of support based on the user's health condition. In this way, more effective support can be provided by customizing the means of support according to the user's living situation. Some or all of the above processes in the support unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the support unit can input the user's living situation data into a generative AI, and the generative AI can customize the means of support.

[0045] The support unit can select the most suitable support method when providing support, taking into account the user's geographical location. For example, if the user is at home, the support unit can suggest ways to relax at home. If the user is out, the support unit can suggest ways to reduce stress while out. If the user is traveling, the support unit can suggest ways to relax at their travel destination. In this way, the support unit can provide the most suitable support method by taking into account the user's geographical location. Some or all of the above processing in the support unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the support unit can input the user's geographical location information into a generative AI, which can then select the most suitable support method.

[0046] The support unit can analyze a user's social media activity and propose support measures when providing support. For example, the support unit can analyze a user's social media posts and propose activities to reduce stress. The support unit can analyze a user's social media interactions and propose relaxation methods. The support unit can analyze the frequency of a user's social media activity and propose appropriate support measures. In this way, by analyzing a user's social media activity, appropriate support measures can be proposed. Some or all of the above processing in the support unit may be performed using generative AI, or not. For example, the support unit can input the user's social media data into a generative AI, and the generative AI can propose support measures.

[0047] The evaluation unit can optimize its evaluation algorithm by referring to the user's past self-evaluation data during the evaluation process. For example, the evaluation unit adjusts the evaluation algorithm based on the user's past self-evaluation data. The evaluation unit analyzes fluctuations in the user's self-evaluation data and optimizes the evaluation algorithm. The evaluation unit refers to the user's past self-evaluation data and sets evaluation criteria according to specific situations. By doing so, the evaluation algorithm can be optimized by referring to past self-evaluation data, thereby improving the accuracy of the evaluation. Some or all of the above processes in the evaluation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the evaluation unit can input the user's past self-evaluation data into a generative AI, which can then optimize the evaluation algorithm.

[0048] The evaluation unit can adjust the timing of the evaluation based on the user's daily rhythm. For example, the evaluation unit can adjust the timing of the evaluation considering the user's sleep pattern. The evaluation unit can conduct evaluations in accordance with the user's work or school schedule. The evaluation unit can support the user's daily rhythm by conducting evaluations in accordance with the user's meal and exercise times. By adjusting the timing of the evaluation to match the user's daily rhythm, a more effective evaluation can be performed. Some or all of the above processes in the evaluation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the evaluation unit can input the user's daily rhythm data into a generative AI, and the generative AI can adjust the timing of the evaluation.

[0049] The evaluation unit can improve the accuracy of its evaluations by considering the user's geographical location information during the evaluation process. For example, if the user is in a specific location, the evaluation unit evaluates stress factors related to that location. If the user is traveling, the evaluation unit performs evaluations related to the environment of the travel destination. If the user is at home, the evaluation unit performs evaluations related to the home environment. By considering the user's geographical location information, the accuracy of the evaluation can be improved. Some or all of the above processing in the evaluation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the evaluation unit can input the user's geographical location information into a generative AI, which can then improve the accuracy of the evaluation.

[0050] The evaluation unit can analyze the user's social media activity during the evaluation process and propose evaluation methods. For example, the evaluation unit can analyze the user's social media posts and assess stress and emotional changes. The evaluation unit can analyze the user's social media interactions and identify stressors. The evaluation unit can evaluate the frequency of the user's social media activity and grasp emotional fluctuations. By analyzing the user's social media activity, it can propose appropriate evaluation methods. Some or all of the above processing in the evaluation unit may be performed using generative AI, or not. For example, the evaluation unit can input the user's social media data into a generative AI, which can then propose evaluation methods.

[0051] The advice unit can select the most suitable advice method by referring to the user's past consultation history when providing advice. For example, the advice unit proposes an effective advice method based on the user's past consultation history. The advice unit analyzes patterns in the user's consultation history and selects an appropriate advice method. The advice unit refers to the user's past consultation history and provides advice tailored to specific situations. In this way, by referring to past consultation history, the advice unit can provide the user with the most suitable advice method. Some or all of the above processes in the advice unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the advice unit can input the user's past consultation history data into a generation AI, which can then select the most suitable advice method.

[0052] The advice unit can customize the means of advice based on the user's current living situation when providing advice. For example, the advice unit can provide advice tailored to the user's work or school schedule. The advice unit can suggest appropriate advice methods according to the user's home environment. The advice unit can provide the optimal means of advice based on the user's health condition. By customizing the means of advice according to the user's living situation, more effective advice can be provided. Some or all of the above processes in the advice unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the advice unit can input the user's living situation data into a generative AI, which can then customize the means of advice.

[0053] The advice unit can select the most appropriate advice method when providing advice, taking into account the user's geographical location. For example, if the user is at home, the advice unit will suggest ways to relax at home. If the user is out, the advice unit will suggest ways to reduce stress while out. If the user is traveling, the advice unit will suggest ways to relax at their travel destination. In this way, the advice unit can provide the most appropriate advice method by taking into account the user's geographical location. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input the user's geographical location information into a generative AI, which can then select the most appropriate advice method.

[0054] The advice unit can analyze the user's social media activity and propose methods for providing advice. For example, the advice unit can analyze the user's social media posts and propose activities to reduce stress. The advice unit can analyze the user's social media interactions and propose relaxation methods. The advice unit can analyze the frequency of the user's social media activity and propose appropriate methods for providing advice. In this way, by analyzing the user's social media activity, appropriate methods for providing advice can be proposed. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input the user's social media data into a generative AI, and the generative AI can propose methods for providing advice.

[0055] The platform unit can select the optimal display method by referring to the user's past operation history when displaying the platform. For example, the platform unit can prioritize the display of frequently used functions based on the user's past operation history. The platform unit analyzes patterns in the user's operation history and selects an appropriate display method. The platform unit refers to the user's past operation history and provides a display method that suits a specific situation. In this way, by referring to past operation history, the platform unit can provide the user with the optimal display method. Some or all of the above processing in the platform unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the platform unit can input the user's past operation history data into a generation AI, and the generation AI can select the optimal display method.

[0056] The platform unit can select the optimal display method when displaying the platform, taking into account the user's device information. For example, if the user is using a smartphone, the platform unit provides a display method that matches the screen size. If the user is using a tablet, the platform unit provides a display method optimized for a large screen. If the user is using a PC, the platform unit provides a display method optimized for a PC. In this way, the optimal display method can be provided by taking into account the user's device information. Some or all of the above processing in the platform unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the platform unit can input the user's device information into a generation AI, and the generation AI can select the optimal display method.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The monitoring unit not only monitors the user's emotions and stress levels, but can also analyze the user's sleep patterns and evaluate sleep quality. For example, if a user wakes up frequently during the night, the monitoring unit collects this data and detects a decline in sleep quality. The monitoring unit can then provide advice to improve the user's sleep patterns, such as suggesting relaxation methods before bed or ways to improve the sleep environment. Furthermore, the monitoring unit can adjust the user's daytime activity level based on their sleep data, thereby improving the user's overall health.

[0059] The support team can not only monitor users' emotions and stress levels, but also analyze their eating patterns and assess their nutritional balance. For example, if a user has an unbalanced diet, the support team can collect this data and detect the nutritional imbalance. The support team can then provide advice to improve the user's eating patterns, such as suggesting balanced meal plans or nutritional supplements. Furthermore, the support team can create daily meal plans based on the user's eating data, thereby improving the user's overall health.

[0060] The evaluation unit can not only monitor the user's emotions and stress levels, but also analyze their exercise patterns and assess their exercise habits. For example, if a user is not getting enough exercise, the evaluation unit can collect this data and detect a lack of exercise. Next, the evaluation unit can provide advice to improve the user's exercise patterns. For example, it can suggest exercise routines that are easy to incorporate into daily life and the best timing for exercise. The evaluation unit can also create an exercise plan based on the user's exercise data. This can improve the user's overall health.

[0061] The advice unit not only monitors the user's emotions and stress levels, but can also analyze their social activities and assess their sociability. For example, if a user is isolated, the advice unit collects this data and detects a decline in their sociability. The advice unit can then provide advice to improve the user's social activities, such as suggesting participation in social events or joining online communities. The advice unit can also create social plans based on the user's social data, thereby improving the user's overall well-being.

[0062] The platform unit can not only monitor users' emotions and stress levels, but also analyze their hobbies and interests and evaluate their hobby activities. For example, if a user is not engaging in hobby activities, the platform unit can collect this data and detect a lack of hobby activity. The platform unit can then provide advice to improve the user's hobby activities, such as suggesting new hobbies or ways to make time for them. The platform unit can also create hobby plans based on the user's hobby data, thereby improving the user's overall well-being.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The monitoring unit monitors the user's emotions and stress levels. For example, the user records their daily emotions and stress levels using a smartphone or wearable device, and this data is analyzed in real time to understand changes in the user's emotions and stress levels. Step 2: The support unit provides customized support based on data monitored by the monitoring unit. For example, it provides morning mood checks and pre-sleep relaxation guides. Users check their mood upon waking up in the morning to confirm their stress level for the day. They also provide relaxation guides before bedtime to help users relax and fall asleep. Step 3: The assessment team identifies psychological risk factors using self-assessment tools. For example, users use self-assessment tools to identify their emotional and stress risk factors, and based on this information, the team provides the most appropriate support to the user. Step 4: The Advice Department provides expert advice based on the risk factors identified by the Assessment Department. For example, they collaborate with psychological counselors and psychiatrists to provide professional support. Step 5: The platform department provides an online platform. For example, it allows users to access the system anytime, anywhere using a PC or smartphone.

[0065] (Example of form 2) The mental health prevention program according to an embodiment of the present invention is a system that uses AI to monitor a user's emotions and stress and provides customized support in real time. This system monitors the user's emotions and stress 24 hours a day, provides morning mood checks and relaxation guides before bedtime, and responds quickly to sudden stress signs. Furthermore, it allows users to identify mental risk factors using self-assessment tools and cultivate mental health with expert advice. It can be easily accessed and used from home or work via an online platform. For example, the user records their daily emotions and stress levels using a smartphone or wearable device. The AI ​​agent analyzes this data in real time to understand changes in the user's emotions and stress. Next, the AI ​​agent provides morning mood checks and relaxation guides before bedtime. For example, the user performs a mood check when they wake up in the morning to check their stress level for the day. It also provides relaxation guides before bedtime to help the user relax and sleep. Furthermore, the AI ​​agent responds quickly to sudden stress signs. For example, if the user suddenly feels stressed, the AI ​​agent detects the sign and immediately provides a solution. This allows the user to reduce stress early. It is also important to identify mental health risk factors using self-assessment tools. Users use these tools to identify their emotional and stress risk factors. Based on this information, the AI ​​agent provides users with the most suitable support. Furthermore, users can cultivate their mental health while receiving expert advice. The AI ​​agent collaborates with psychological counselors and psychiatrists as needed to provide professional support. This allows users to maintain their mental health with expert guidance. The online platform makes it easy to access and use from home or work. Users can access this system anytime, anywhere using their PC or smartphone. This allows users to manage their mental health at their own pace.For example, if a user experiences stress at work or school, the AI ​​agent can provide real-time support to alleviate that stress. The AI ​​agent can also provide appropriate support for family problems and financial anxieties. This allows users to protect their mental health and live fulfilling lives. In this way, mental health prevention programs can maintain mental well-being by efficiently monitoring users' emotions and stress levels and providing customized support.

[0066] The mental health prevention program according to this embodiment comprises a monitoring unit, a support unit, an evaluation unit, an advice unit, and a platform unit. The monitoring unit monitors the user's emotions and stress. For example, the monitoring unit records the user's daily emotions and stress levels using a smartphone or wearable device. The monitoring unit analyzes this data in real time to understand changes in the user's emotions and stress. Some or all of the above processing in the monitoring unit may be performed using AI or not. The support unit provides customized support based on the data monitored by the monitoring unit. For example, the support unit provides a morning mood check and a relaxation guide before bedtime. The support unit performs a mood check when the user wakes up in the morning and checks their stress level for the day. The support unit also provides a relaxation guide before bedtime to help the user relax and sleep. Some or all of the above processing in the support unit may be performed using AI or not. The evaluation unit identifies mental risk factors using a self-assessment tool. For example, the evaluation unit has the user identify their own emotional and stress risk factors using a self-assessment tool. The evaluation unit provides optimal support to users based on this information. Some or all of the processes described above in the evaluation unit may be performed using AI or not. The advice unit provides expert advice based on the risk factors identified by the evaluation unit. The advice unit provides expert support, for example, by collaborating with psychological counselors or psychiatrists. Some or all of the processes described above in the advice unit may be performed using AI or not. The platform unit provides an online platform. The platform unit allows users to access this system anytime, anywhere using a PC or smartphone, for example. Some or all of the processes described above in the platform unit may be performed using AI or not.As a result, the mental health prevention program according to this embodiment can maintain the user's mental health by efficiently monitoring their emotions and stress and providing customized support.

[0067] The monitoring unit monitors the user's emotions and stress levels. For example, the unit records the user's daily emotions and stress levels using smartphones or wearable devices. Specifically, it collects the user's heart rate, sleep patterns, activity levels, and even changes in voice and facial expressions through smartphone applications and wearable device sensors. This data is sent to a cloud server in real time for analysis by AI. The AI ​​uses machine learning algorithms to learn the user's emotional and stress patterns and detect abnormal changes or increases in stress. For example, if a user's heart rate remains higher than normal for an extended period, or if their sleep quality deteriorates, the AI ​​recognizes this as a sign of stress and notifies the user. It can also use voice analysis technology to detect changes in the user's voice tone and speaking style, thereby understanding changes in emotions. This allows the monitoring unit to grasp changes in the user's emotions and stress in real time and respond early. Furthermore, the monitoring unit can store the collected data long-term and analyze trends in the user's emotions and stress. This allows for continuous monitoring of the user's mental health and the provision of appropriate support as needed.

[0068] The support department provides customized support based on data monitored by the monitoring department. For example, the support department offers morning mood checks and bedtime relaxation guides. Specifically, users perform a mood check via a smartphone application upon waking to determine their stress level for the day. Based on the user's responses and data from the monitoring department, the AI ​​provides advice for stress management throughout the day. For instance, if a user reports a high stress level, the AI ​​provides guidance on relaxation breathing techniques and simple stretches. Before bedtime, it also provides guidance on meditation and relaxation music to help users relax and fall asleep. This allows users to relax at the end of the day and achieve high-quality sleep. Furthermore, the support department can create customized support plans tailored to the user's lifestyle and preferences. For example, if a user enjoys exercise, it can suggest an exercise program for stress relief. This allows the support department to provide individualized support tailored to the user's needs and help maintain their mental health.

[0069] The assessment department uses self-assessment tools to identify mental health risk factors. For example, users use self-assessment tools to identify their emotional and stress risk factors. Specifically, users regularly answer self-assessment questionnaires to record their emotional and stress levels. AI analyzes these responses to identify the user's risk factors. For example, if a user frequently experiences anxiety or irritability, the AI ​​recognizes this as a risk factor and suggests appropriate support. The assessment department can also analyze fluctuations in risk factors based on the user's past data and behavioral patterns. This allows for continuous assessment of the user's mental health status and early detection of risks. Furthermore, the assessment department can create individualized support plans based on the user's self-assessment results. This enables the assessment department to identify the user's mental health risk factors and build a foundation for providing appropriate support.

[0070] The Advice Department provides expert advice based on risk factors identified by the Assessment Department. For example, the Advice Department collaborates with psychological counselors and psychiatrists to provide professional support. Specifically, psychological counselors and psychiatrists provide individualized advice based on risk factors identified by the user through self-assessment tools. AI analyzes user data and matches users with appropriate professionals. For example, if a user reports a high stress level, the AI ​​recommends a stress management specialist and sets up an online counseling session. The Advice Department also provides access to expert advice through an online platform, allowing users to receive professional support anytime, anywhere. Furthermore, the Advice Department can collect user feedback and continuously improve the quality of its advice. This enables the Advice Department to provide users with professional and effective support and help them maintain their mental health.

[0071] The Platform Division provides an online platform. For example, the Platform Division allows users to access the system anytime, anywhere using a PC or smartphone. Specifically, users can access the functions of the Monitoring Division, Support Division, Evaluation Division, and Advice Division through a dedicated application or website. The platform centrally manages user data, enabling each department to quickly access the information they need. AI analyzes user data and creates individualized support plans. The platform also provides online counseling functionality for users to connect with experts, allowing them to receive professional support from the comfort of their homes. Furthermore, the platform implements data encryption and access control to protect user privacy, ensuring users can use the system with peace of mind. The Platform Division provides a foundation to enhance user convenience and support the maintenance of mental health.

[0072] The monitoring unit can monitor users' emotions and stress levels 24 hours a day. For example, the monitoring unit records users' daily emotions and stress levels using smartphones or wearable devices. The monitoring unit analyzes this data in real time to understand changes in the user's emotions and stress levels. By monitoring users' emotions and stress levels 24 hours a day, the monitoring unit can respond quickly. Some or all of the above processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion and stress data into a generating AI, which can then analyze it in real time to understand changes in the user's emotions and stress levels.

[0073] The support unit can provide morning mood checks and bedtime relaxation guides. For example, the support unit can perform a mood check when the user wakes up in the morning to check their stress level for the day. The support unit can also provide a relaxation guide before bedtime to help the user relax and sleep. By providing appropriate support at the beginning and end of the user's day, the support unit helps maintain their mental health. Some or all of the processes described above in the support unit may be performed using AI or not. For example, the support unit can input the user's mood check data into a generating AI, which can then analyze the user's stress level and provide an appropriate relaxation guide.

[0074] The support unit can quickly respond to sudden stress signs. For example, if a user suddenly feels stressed, the support unit will detect the sign and immediately provide a solution. By responding quickly to sudden stress, the support unit reduces the user's stress. Some or all of the above processes in the support unit may be performed using AI or not. For example, the support unit can input the user's stress sign data into a generating AI, which can then analyze the stress signs and provide an appropriate solution.

[0075] The evaluation unit can identify mental health risk factors using a self-assessment tool. For example, the evaluation unit allows users to identify their emotional and stress risk factors using a self-assessment tool. Based on this information, the evaluation unit provides the user with the most appropriate support. By using the self-assessment tool, the evaluation unit identifies the user's mental health risk factors and provides appropriate support. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the user's self-assessment data into a generating AI, which can then analyze the risk factors and provide appropriate support.

[0076] The advice unit can provide expert advice. For example, the advice unit can collaborate with psychological counselors or psychiatrists to provide professional support. By providing expert advice, the advice unit professionally supports the user's mental health. Some or all of the above processes in the advice unit may be performed using AI or not. For example, the advice unit can input the user's risk factor data into a generating AI, which can then generate expert advice and provide it to the user.

[0077] The platform unit can provide an online platform, allowing users to easily access it from home or work. For example, the platform unit can enable users to access the system anytime, anywhere using a PC or smartphone. By providing an online platform, the platform unit ensures easy access from anywhere. Some or all of the above-described processes in the platform unit may be performed using AI or not. For example, the platform unit can input user access data into a generating AI, which can then provide the optimal access method.

[0078] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring unit can increase the monitoring frequency to provide real-time support. If the user is relaxed, the monitoring unit can reduce the monitoring frequency to respect the user's privacy. If the user's emotions are unstable, the monitoring unit can appropriately adjust the monitoring frequency to provide the necessary support. In this way, by adjusting the monitoring frequency according to the user's emotions, more appropriate support can be provided. Some or all of the above processing in the monitoring unit is implemented using emotion estimation functions with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the monitoring unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the monitoring frequency.

[0079] The monitoring unit can analyze the user's past emotional data and select the optimal monitoring method. For example, the monitoring unit can intensify monitoring during specific time periods based on the user's past emotional data. The monitoring unit can analyze the user's emotional fluctuation patterns and intensify monitoring before stress levels rise. The monitoring unit can refer to the user's past emotional data and adjust the monitoring method according to specific events or situations. In this way, by analyzing past emotional data, the monitoring unit can provide the user with the most suitable monitoring method. Some or all of the above processes in the monitoring unit may be performed using or without a generative AI. For example, the monitoring unit can input the user's past emotional data into a generative AI, which can then select the optimal monitoring method.

[0080] The monitoring unit can adjust the timing of monitoring based on the user's daily rhythm. For example, the monitoring unit can consider the user's sleep patterns and reduce nighttime monitoring. The monitoring unit can adjust the timing of monitoring to match the user's work or school schedule. The monitoring unit can monitor according to the user's meal and exercise times to support their daily rhythm. By adjusting the timing of monitoring to match the user's daily rhythm, more effective monitoring becomes possible. Some or all of the above processing in the monitoring unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the monitoring unit can input the user's daily rhythm data into a generative AI, which can then adjust the timing of monitoring.

[0081] The monitoring unit can estimate the user's emotions and determine the priority of data to monitor based on the estimated user emotions. For example, if the user is stressed, the monitoring unit will prioritize monitoring stress-related data. If the user is relaxed, the monitoring unit will prioritize monitoring relaxation-related data. If the user's emotions are unstable, the monitoring unit will prioritize monitoring data related to emotional fluctuations. In this way, important data can be prioritized by determining the priority of data to monitor based on the user's emotions. Some or all of the above processing in the monitoring unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the monitoring unit can input user emotion data into a generative AI, and the generative AI can determine the priority of data to monitor.

[0082] The monitoring unit can prioritize monitoring highly relevant data by considering the user's geographical location during monitoring. For example, if the user is in a specific location, the monitoring unit will prioritize monitoring stressors associated with that location. If the user is traveling, the monitoring unit will prioritize monitoring data related to the environment of the travel destination. If the user is at home, the monitoring unit will prioritize monitoring data related to the home environment. In this way, by considering the user's geographical location, highly relevant data can be prioritized. Some or all of the above processing in the monitoring unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the monitoring unit can input the user's geographical location information into a generative AI, which can then prioritize monitoring highly relevant data.

[0083] The monitoring unit can analyze the user's social media activity and monitor related data during monitoring. For example, the monitoring unit can analyze the user's social media posts and monitor stress and emotional changes. The monitoring unit can analyze the user's social media interactions and identify stressors. The monitoring unit can monitor the frequency of the user's social media activity and grasp emotional fluctuations. In this way, by analyzing the user's social media activity, it is possible to monitor related data. Some or all of the above processing in the monitoring unit may be performed using or without a generative AI. For example, the monitoring unit can input the user's social media data into a generative AI, and the generative AI can monitor the related data.

[0084] The support unit can estimate the user's emotions and adjust the support content based on the estimated emotions. For example, if the user is feeling stressed, the support unit can suggest ways to relax. If the user is relaxed, the support unit can suggest positive activities. If the user's emotions are unstable, the support unit can provide support to stabilize those emotions. In this way, by adjusting the support content according to the user's emotions, more appropriate support can be provided. Some or all of the above processing in the support unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the support unit can input user emotion data into a generative AI, and the generative AI can adjust the support content.

[0085] The support unit can analyze the user's past stress responses and select the optimal support method when providing support. For example, the support unit can suggest effective relaxation methods based on the user's past stress responses. The support unit analyzes the user's stress response patterns and selects appropriate support methods. The support unit refers to the user's past stress responses and provides support tailored to specific situations. In this way, by analyzing past stress responses, the support unit can provide the user with the most suitable support method. Some or all of the above processes in the support unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the support unit can input the user's past stress response data into a generative AI, which can then select the optimal support method.

[0086] The support unit can customize the means of support provided based on the user's current living situation. For example, the support unit can provide support according to the user's work or school schedule. The support unit can suggest appropriate support methods according to the user's home environment. The support unit can provide the optimal means of support based on the user's health condition. In this way, more effective support can be provided by customizing the means of support according to the user's living situation. Some or all of the above processes in the support unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the support unit can input the user's living situation data into a generative AI, and the generative AI can customize the means of support.

[0087] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is stressed, the support unit will prioritize stress reduction support. If the user is relaxed, the support unit will prioritize support for positive activities. If the user's emotions are unstable, the support unit will prioritize support for emotional stabilization. This allows important support to be provided preferentially by determining the priority of support based on the user's emotions. Some or all of the above processing in the support unit may be performed using generative AI, or not. For example, the support unit can input user emotion data into a generative AI, which can then determine the priority of support.

[0088] The support unit can select the most suitable support method when providing support, taking into account the user's geographical location. For example, if the user is at home, the support unit can suggest ways to relax at home. If the user is out, the support unit can suggest ways to reduce stress while out. If the user is traveling, the support unit can suggest ways to relax at their travel destination. In this way, the support unit can provide the most suitable support method by taking into account the user's geographical location. Some or all of the above processing in the support unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the support unit can input the user's geographical location information into a generative AI, which can then select the most suitable support method.

[0089] The support unit can analyze a user's social media activity and propose support measures when providing support. For example, the support unit can analyze a user's social media posts and propose activities to reduce stress. The support unit can analyze a user's social media interactions and propose relaxation methods. The support unit can analyze the frequency of a user's social media activity and propose appropriate support measures. In this way, by analyzing a user's social media activity, appropriate support measures can be proposed. Some or all of the above processing in the support unit may be performed using generative AI, or not. For example, the support unit can input the user's social media data into a generative AI, and the generative AI can propose support measures.

[0090] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user emotions. For example, if the user is feeling stressed, the evaluation unit sets evaluation criteria for stress reduction. If the user is relaxed, the evaluation unit sets criteria for evaluating positive emotions. If the user's emotions are unstable, the evaluation unit sets criteria for evaluating emotional stability. By adjusting the evaluation criteria according to the user's emotions, a more appropriate evaluation can be made. Some or all of the above processing in the evaluation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the evaluation unit can input user emotion data into a generative AI, and the generative AI can adjust the evaluation criteria.

[0091] The evaluation unit can optimize its evaluation algorithm by referring to the user's past self-evaluation data during the evaluation process. For example, the evaluation unit adjusts the evaluation algorithm based on the user's past self-evaluation data. The evaluation unit analyzes fluctuations in the user's self-evaluation data and optimizes the evaluation algorithm. The evaluation unit refers to the user's past self-evaluation data and sets evaluation criteria according to specific situations. By doing so, the evaluation algorithm can be optimized by referring to past self-evaluation data, thereby improving the accuracy of the evaluation. Some or all of the above processes in the evaluation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the evaluation unit can input the user's past self-evaluation data into a generative AI, which can then optimize the evaluation algorithm.

[0092] The evaluation unit can adjust the timing of the evaluation based on the user's daily rhythm. For example, the evaluation unit can adjust the timing of the evaluation considering the user's sleep pattern. The evaluation unit can conduct evaluations in accordance with the user's work or school schedule. The evaluation unit can support the user's daily rhythm by conducting evaluations in accordance with the user's meal and exercise times. By adjusting the timing of the evaluation to match the user's daily rhythm, a more effective evaluation can be performed. Some or all of the above processes in the evaluation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the evaluation unit can input the user's daily rhythm data into a generative AI, and the generative AI can adjust the timing of the evaluation.

[0093] The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated user emotions. For example, if the user is feeling stressed, the evaluation unit will prioritize stress reduction evaluations. If the user is relaxed, the evaluation unit will prioritize positive emotional evaluations. If the user's emotions are unstable, the evaluation unit will prioritize emotional stability evaluations. In this way, by determining the priority of evaluations based on the user's emotions, important evaluations can be prioritized. Some or all of the above processing in the evaluation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the evaluation unit can input user emotion data into a generative AI, and the generative AI can determine the priority of evaluations.

[0094] The evaluation unit can improve the accuracy of its evaluations by considering the user's geographical location information during the evaluation process. For example, if the user is in a specific location, the evaluation unit evaluates stress factors related to that location. If the user is traveling, the evaluation unit performs evaluations related to the environment of the travel destination. If the user is at home, the evaluation unit performs evaluations related to the home environment. By considering the user's geographical location information, the accuracy of the evaluation can be improved. Some or all of the above processing in the evaluation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the evaluation unit can input the user's geographical location information into a generative AI, which can then improve the accuracy of the evaluation.

[0095] The evaluation unit can analyze the user's social media activity during the evaluation process and propose evaluation methods. For example, the evaluation unit can analyze the user's social media posts and assess stress and emotional changes. The evaluation unit can analyze the user's social media interactions and identify stressors. The evaluation unit can evaluate the frequency of the user's social media activity and grasp emotional fluctuations. By analyzing the user's social media activity, it can propose appropriate evaluation methods. Some or all of the above processing in the evaluation unit may be performed using generative AI, or not. For example, the evaluation unit can input the user's social media data into a generative AI, which can then propose evaluation methods.

[0096] The advice unit can estimate the user's emotions and adjust the content of the advice based on the estimated emotions. For example, if the user is feeling stressed, the advice unit will suggest ways to relax. If the user is relaxed, the advice unit will suggest positive activities. If the user's emotions are unstable, the advice unit will provide advice to stabilize their emotions. In this way, by adjusting the content of the advice according to the user's emotions, more appropriate advice can be provided. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input the user's emotion data into a generative AI, and the generative AI can adjust the content of the advice.

[0097] The advice unit can select the most suitable advice method by referring to the user's past consultation history when providing advice. For example, the advice unit proposes an effective advice method based on the user's past consultation history. The advice unit analyzes patterns in the user's consultation history and selects an appropriate advice method. The advice unit refers to the user's past consultation history and provides advice tailored to specific situations. In this way, by referring to past consultation history, the advice unit can provide the user with the most suitable advice method. Some or all of the above processes in the advice unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the advice unit can input the user's past consultation history data into a generation AI, which can then select the most suitable advice method.

[0098] The advice unit can customize the means of advice based on the user's current living situation when providing advice. For example, the advice unit can provide advice tailored to the user's work or school schedule. The advice unit can suggest appropriate advice methods according to the user's home environment. The advice unit can provide the optimal means of advice based on the user's health condition. By customizing the means of advice according to the user's living situation, more effective advice can be provided. Some or all of the above processes in the advice unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the advice unit can input the user's living situation data into a generative AI, which can then customize the means of advice.

[0099] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated emotions. For example, if the user is feeling stressed, the advice unit will prioritize stress-reducing advice. If the user is relaxed, the advice unit will prioritize advice for positive activities. If the user's emotions are unstable, the advice unit will prioritize advice for emotional stabilization. This allows important advice to be provided preferentially by determining the priority of advice based on the user's emotions. Some or all of the above processing in the advice unit may be performed using generative AI, or it may be performed without generative AI. For example, the advice unit can input user emotion data into a generative AI, which can then determine the priority of advice.

[0100] The advice unit can select the most appropriate advice method when providing advice, taking into account the user's geographical location. For example, if the user is at home, the advice unit will suggest ways to relax at home. If the user is out, the advice unit will suggest ways to reduce stress while out. If the user is traveling, the advice unit will suggest ways to relax at their travel destination. In this way, the advice unit can provide the most appropriate advice method by taking into account the user's geographical location. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input the user's geographical location information into a generative AI, which can then select the most appropriate advice method.

[0101] The advice unit can analyze the user's social media activity and propose methods for providing advice. For example, the advice unit can analyze the user's social media posts and propose activities to reduce stress. The advice unit can analyze the user's social media interactions and propose relaxation methods. The advice unit can analyze the frequency of the user's social media activity and propose appropriate methods for providing advice. In this way, by analyzing the user's social media activity, appropriate methods for providing advice can be proposed. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input the user's social media data into a generative AI, and the generative AI can propose methods for providing advice.

[0102] The platform unit can estimate the user's emotions and adjust the platform's display method based on the estimated emotions. For example, if the user is stressed, the platform unit provides an interface with calming colors. If the user is relaxed, the platform unit provides an interface with bright colors. If the user's emotions are unstable, the platform unit provides a simple and highly visible interface. By adjusting the platform's display method according to the user's emotions, a more comfortable user experience can be provided. Some or all of the above processing in the platform unit is implemented using emotion estimation functions with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the platform unit can input user emotion data into the generative AI, which can then adjust the platform's display method.

[0103] The platform unit can select the optimal display method by referring to the user's past operation history when displaying the platform. For example, the platform unit can prioritize the display of frequently used functions based on the user's past operation history. The platform unit analyzes patterns in the user's operation history and selects an appropriate display method. The platform unit refers to the user's past operation history and provides a display method that suits a specific situation. In this way, by referring to past operation history, the platform unit can provide the user with the optimal display method. Some or all of the above processing in the platform unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the platform unit can input the user's past operation history data into a generation AI, and the generation AI can select the optimal display method.

[0104] The platform unit can estimate the user's emotions and adjust the platform's operating procedures based on the estimated emotions. For example, if the user is stressed, the platform unit simplifies the operating procedures. If the user is relaxed, the platform unit provides detailed operating procedures. If the user's emotions are unstable, the platform unit simplifies the operating procedures to make them easier to use. In this way, by adjusting the operating procedures according to the user's emotions, a more user-friendly platform can be provided. Some or all of the above processing in the platform unit is implemented using emotion estimation functions with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, the platform unit can input user emotion data into the generative AI, and the generative AI can adjust the platform's operating procedures.

[0105] The platform unit can select the optimal display method when displaying the platform, taking into account the user's device information. For example, if the user is using a smartphone, the platform unit provides a display method that matches the screen size. If the user is using a tablet, the platform unit provides a display method optimized for a large screen. If the user is using a PC, the platform unit provides a display method optimized for a PC. In this way, the optimal display method can be provided by taking into account the user's device information. Some or all of the above processing in the platform unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the platform unit can input the user's device information into a generation AI, and the generation AI can select the optimal display method.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The monitoring unit not only monitors the user's emotions and stress levels, but can also analyze the user's sleep patterns and evaluate sleep quality. For example, if a user wakes up frequently during the night, the monitoring unit collects this data and detects a decline in sleep quality. The monitoring unit can then provide advice to improve the user's sleep patterns, such as suggesting relaxation methods before bed or ways to improve the sleep environment. Furthermore, the monitoring unit can adjust the user's daytime activity level based on their sleep data, thereby improving the user's overall health.

[0108] The support team can not only monitor users' emotions and stress levels, but also analyze their eating patterns and assess their nutritional balance. For example, if a user has an unbalanced diet, the support team can collect this data and detect the nutritional imbalance. The support team can then provide advice to improve the user's eating patterns, such as suggesting balanced meal plans or nutritional supplements. Furthermore, the support team can create daily meal plans based on the user's eating data, thereby improving the user's overall health.

[0109] The evaluation unit can not only monitor the user's emotions and stress levels, but also analyze their exercise patterns and assess their exercise habits. For example, if a user is not getting enough exercise, the evaluation unit can collect this data and detect a lack of exercise. Next, the evaluation unit can provide advice to improve the user's exercise patterns. For example, it can suggest exercise routines that are easy to incorporate into daily life and the best timing for exercise. The evaluation unit can also create an exercise plan based on the user's exercise data. This can improve the user's overall health.

[0110] The advice unit not only monitors the user's emotions and stress levels, but can also analyze their social activities and assess their sociability. For example, if a user is isolated, the advice unit collects this data and detects a decline in their sociability. The advice unit can then provide advice to improve the user's social activities, such as suggesting participation in social events or joining online communities. The advice unit can also create social plans based on the user's social data, thereby improving the user's overall well-being.

[0111] The platform unit can not only monitor users' emotions and stress levels, but also analyze their hobbies and interests and evaluate their hobby activities. For example, if a user is not engaging in hobby activities, the platform unit can collect this data and detect a lack of hobby activity. The platform unit can then provide advice to improve the user's hobby activities, such as suggesting new hobbies or ways to make time for them. The platform unit can also create hobby plans based on the user's hobby data, thereby improving the user's overall well-being.

[0112] The monitoring unit can estimate the user's emotions and, based on the estimated emotions, display the user's stress level in real time. For example, if a user is feeling stressed, the monitoring unit will collect that data and display a high stress level. Next, the monitoring unit can suggest appropriate coping strategies according to the user's stress level. For example, it may suggest deep breathing or meditation techniques. The monitoring unit can also periodically monitor the user's stress level to check whether the stress has decreased. This allows users to understand their own stress levels and practice appropriate coping strategies.

[0113] The support unit can estimate the user's emotions and, based on those estimates, suggest music to improve the user's mood. For example, if the user is feeling stressed, the support unit will suggest relaxing music. Next, if the user is relaxed, the support unit will suggest music to maintain a positive mood. Furthermore, if the user's emotions are unstable, the support unit can also suggest music to stabilize those emotions. In this way, users can improve their mood by listening to music that matches their emotions.

[0114] The evaluation unit can estimate the user's emotions and automatically generate an emotional diary based on the estimated emotions. For example, if the user is feeling stressed, the evaluation unit collects that data and records it in the emotional diary. Next, if the user is relaxed, the evaluation unit records that data in the emotional diary. The evaluation unit can also record data if the user's emotions are unstable. This allows the user to understand the changes in their emotions and check emotional trends.

[0115] The advice unit can estimate the user's emotions and, based on that estimation, suggest activities that are appropriate for the user's emotions. For example, if the user is feeling stressed, the advice unit will suggest activities that help them relax. Next, if the user is relaxed, the advice unit will suggest activities to maintain a positive mood. Furthermore, if the user's emotions are unstable, the advice unit can also suggest activities to stabilize those emotions. In this way, users can improve their mood by engaging in activities that are appropriate for their emotions.

[0116] The platform can estimate the user's emotions and, based on that estimation, provide an interface that responds to the user's emotions. For example, if the user is stressed, the platform provides an interface with calming colors. Next, if the user is relaxed, the platform provides an interface with bright colors. Furthermore, if the user's emotions are unstable, the platform can provide a simple and highly visible interface. This allows users to have a comfortable user experience by using an interface that matches their emotions.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The monitoring unit monitors the user's emotions and stress levels. For example, the user records their daily emotions and stress levels using a smartphone or wearable device, and this data is analyzed in real time to understand changes in the user's emotions and stress levels. Step 2: The support unit provides customized support based on data monitored by the monitoring unit. For example, it provides morning mood checks and pre-sleep relaxation guides. Users check their mood upon waking up in the morning to confirm their stress level for the day. They also provide relaxation guides before bedtime to help users relax and fall asleep. Step 3: The assessment team identifies psychological risk factors using self-assessment tools. For example, users use self-assessment tools to identify their emotional and stress risk factors, and based on this information, the team provides the most appropriate support to the user. Step 4: The Advice Department provides expert advice based on the risk factors identified by the Assessment Department. For example, they collaborate with psychological counselors and psychiatrists to provide professional support. Step 5: The platform department provides an online platform. For example, it allows users to access the system anytime, anywhere using a PC or smartphone.

[0119] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0122] Each of the multiple elements described above, including the monitoring unit, support unit, evaluation unit, advice unit, and platform unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors the user's emotions and stress using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A analyzes the data in real time. The support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides customized support to the user. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and identifies mental risk factors using a self-assessment tool. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides expert advice. The platform unit is implemented, for example, by the control unit 46A of the smart device 14, and allows the user to access an online platform. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the monitoring unit, support unit, evaluation unit, advice unit, and platform unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors the user's emotions and stress using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A analyzes the data in real time. The support unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and provides customized support to the user. The evaluation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and identifies mental risk factors using a self-assessment tool. The advice unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and provides expert advice. The platform unit is implemented, for example, in the control unit 46A of the smart glasses 214, and allows the user to access an online platform. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the monitoring unit, support unit, evaluation unit, advice unit, and platform unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the user's emotions and stress using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A analyzes the data in real time. The support unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, and provides customized support to the user. The evaluation unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, and identifies mental risk factors using a self-assessment tool. The advice unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, and provides expert advice. The platform unit is implemented in, for example, the control unit 46A of the headset terminal 314, and allows the user to access an online platform. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] As shown in Figure 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.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the monitoring unit, support unit, evaluation unit, advice unit, and platform unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors the user's emotions and stress using the camera 42 and microphone 238 of the robot 414, and the control unit 46A analyzes the data in real time. The support unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and provides customized support to the user. The evaluation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and identifies mental risk factors using a self-assessment tool. The advice unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and provides expert advice. The platform unit is implemented, for example, in the control unit 46A of the robot 414, and allows the user to access an online platform. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0172] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0181] 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.

[0182] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0184] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) A monitoring unit that monitors users' emotions and stress, A support unit provides customized support based on data monitored by the aforementioned monitoring unit, The assessment unit uses self-assessment tools to identify mental health risk factors, An advisory unit provides expert advice based on the risk factors identified by the aforementioned evaluation unit, A platform unit that provides an online platform, A system characterized by the following features. (Note 2) The aforementioned monitoring unit, Monitor users' emotions and stress levels 24 / 7. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support unit is It provides a morning mood check and a relaxation guide before bedtime. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit is Respond quickly to sudden signs of stress The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit, Use self-assessment tools to identify mental health risk factors. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice section, Providing expert advice The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned platform unit is We provide an online platform that allows users to easily access it from home or work. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, Analyze users' past emotional data to select the optimal monitoring method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, During monitoring, the timing of monitoring is adjusted based on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, It estimates user sentiment and prioritizes data to monitor based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, During monitoring, the system prioritizes monitoring of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned monitoring unit, During monitoring, the system analyzes users' social media activity and monitors relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned support unit is The system estimates the user's emotions and adjusts the support provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned support unit is When providing support, we analyze the user's past stress responses to select the most appropriate support method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned support unit is When providing support, customize the support methods based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned support unit is When providing support, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned support unit is When providing support, we analyze the user's social media activity and suggest support methods. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, During the evaluation process, the evaluation algorithm is optimized by referring to the user's past self-assessment data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, During the evaluation process, the timing of the evaluation will be adjusted based on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, It estimates the user's emotions and determines the priority of evaluations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, During evaluation, the accuracy of the evaluation is improved by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, During the evaluation process, we will analyze users' social media activity and propose evaluation methods. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice section, It estimates the user's emotions and adjusts the content of the advice based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, When providing advice, the system selects the most appropriate advice method by referring to the user's past consultation history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, When providing advice, customize the method of advice based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned advice section, When providing advice, the optimal advice method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned advice section, When providing advice, we analyze the user's social media activity and suggest methods for providing advice. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned platform unit is The platform estimates user sentiment and adjusts how it displays information based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned platform unit is When displaying the platform, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned platform unit is The system estimates user sentiment and adjusts the platform's operating procedures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned platform unit is When displaying the platform, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A monitoring unit that monitors users' emotions and stress, A support unit provides customized support based on data monitored by the aforementioned monitoring unit, The assessment unit uses self-assessment tools to identify mental health risk factors, An advisory unit provides expert advice based on the risk factors identified by the aforementioned evaluation unit, A platform unit that provides an online platform, A system characterized by the following features.

2. The aforementioned monitoring unit, Monitor users' emotions and stress levels 24 / 7. The system according to feature 1.

3. The aforementioned support unit is It provides a morning mood check and a relaxation guide before bedtime. The system according to feature 1.

4. The aforementioned support unit is Respond quickly to sudden signs of stress The system according to feature 1.

5. The evaluation unit, Use self-assessment tools to identify mental health risk factors. The system according to feature 1.

6. The aforementioned advice section, Providing expert advice The system according to feature 1.

7. The aforementioned platform unit is We provide an online platform that allows users to easily access it from home or work. The system according to feature 1.

8. The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system according to feature 1.

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