system

Abstract

The system according to this embodiment aims to provide individualized resilience enhancement programs based on the user's psychological state, track progress in real time, and provide feedback. [Solution] The system according to the embodiment comprises an analysis unit, a provision unit, a tracking unit, and a feedback unit. The analysis unit analyzes the user's psychological state. The provision unit provides an individualized resilience enhancement program based on the results analyzed by the analysis unit. The tracking unit tracks the user's progress. The feedback unit provides real-time feedback and encouragement based on the progress data obtained by the tracking unit.

Description

Technical Field

[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 performed by at least one processor, the method including receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 conventional technology, it has not been sufficiently carried out to provide an individual resilience enhancement program based on the user's mental state, track the progress in real time, and give feedback.

[0005] The system according to the embodiment aims to provide an individual resilience enhancement program based on the user's mental state, track the progress in real time, and give feedback.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a provision unit, a tracking unit, and a feedback unit. The analysis unit analyzes the user's psychological state. The provision unit provides an individualized resilience enhancement program based on the results analyzed by the analysis unit. The tracking unit tracks the user's progress. The feedback unit provides real-time feedback and encouragement based on the progress data obtained by the tracking unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide individualized resilience enhancement programs based on the user's psychological state, track progress in real time, and provide feedback. [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, etc. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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) An AI resilience coach according to an embodiment of the present invention is a personalized AI assistant service for enhancing a user's mental resilience and adaptability. This AI resilience coach analyzes the user's psychological state through daily check-ins and interactions, and provides an individualized resilience enhancement program. Through short daily sessions, the user can learn skills such as stress management, emotional control, and building positive thought patterns. The AI ​​coach tracks the user's progress and provides real-time feedback and encouragement. Furthermore, the AI ​​resilience coach intervenes immediately in times of crisis or high stress, providing urgent support and coping strategies. In addition, with the user's permission, it collaborates with family and professionals to build a comprehensive support system. For example, the AI ​​resilience coach helps the user continuously cultivate resilience in their daily life, enabling them to effectively cope with various life challenges. For instance, the user can deepen their self-understanding and develop the ability to recover positively even in difficult situations. In this way, the AI ​​resilience coach can enhance the user's mental resilience and adaptability.

[0029] The AI ​​resilience coach according to this embodiment comprises an analysis unit, a provision unit, a tracking unit, and a feedback unit. The analysis unit analyzes the user's psychological state. The analysis unit analyzes the user's psychological state using, for example, AI. For example, the analysis unit can measure the user's stress level, emotional state, psychological health, etc. The provision unit provides an individualized resilience enhancement program based on the results analyzed by the analysis unit. The provision unit determines, for example, the training content and session frequency appropriate for the user using AI. For example, the provision unit provides a program that teaches skills such as stress management, emotional control, and building positive thinking patterns according to the user's psychological state. The tracking unit tracks the user's progress. The tracking unit evaluates the user's achievement level, improvement level, goal achievement rate, etc., using, for example, AI. For example, the tracking unit periodically collects user progress data to grasp long-term progress. The feedback unit provides real-time feedback and encouragement based on the progress data obtained by the tracking unit. The feedback unit provides appropriate feedback to the user using, for example, AI. For example, the feedback unit adjusts the frequency and timing of feedback according to the user's progress. This allows the AI ​​resilience coach according to the embodiment to analyze the user's psychological state, provide a personalized resilience enhancement program, track progress, and provide real-time feedback and encouragement.

[0030] The analysis department analyzes the user's psychological state. Specifically, it uses AI to analyze the user's psychological state. The AI ​​collects text data, voice data, and biosensor data entered by the user, and uses this data to measure the user's stress level, emotional state, and psychological health. For example, it analyzes text data entered by the user in a diary format using natural language processing technology to perform emotional analysis. This makes it possible to detect changes in the user's emotions and signs of stress. In addition, by analyzing voice data, it is possible to infer the emotional state from changes in voice tone and speaking style. Furthermore, it analyzes biosensor data such as heart rate and skin electrical activity obtained from wearable devices worn by the user to quantitatively evaluate the stress level. In this way, the analysis department can integrate multifaceted data to gain a detailed understanding of the user's psychological state.

[0031] The service provider offers personalized resilience enhancement programs based on the results analyzed by the analysis department. Specifically, AI is used to determine the appropriate training content and session frequency for each user. The AI ​​considers the user's psychological state and past training history to generate the optimal program. For example, it may offer breathing exercises and meditation sessions for stress management, cognitive behavioral therapy exercises for emotional control, and positive psychology workshops for building positive thinking patterns. These programs are customized to the user's needs and can effectively enhance resilience. The service provider also collects user feedback in real time and adjusts the program content and progress as needed. This ensures that users always receive optimal training and can experience improved resilience.

[0032] The tracking unit monitors the user's progress. Specifically, it uses AI to evaluate the user's achievement level, improvement level, and goal attainment rate. The AI ​​collects data from the user's training and uses this data to quantitatively evaluate their progress. For example, it records the number of exercises performed, the duration, and the number of goals achieved, and analyzes this data to evaluate progress. It also tracks changes in the user's psychological state to evaluate the effectiveness of the training. For example, it analyzes the results of regularly conducted psychological state assessment tests to understand changes in the user's stress level and emotional state. This allows the tracking unit to grasp the user's long-term progress in detail and continuously evaluate the effectiveness of the training.

[0033] The feedback unit provides real-time feedback and encouragement based on progress data obtained by the tracking unit. Specifically, it uses AI to provide appropriate feedback to users. The AI ​​analyzes the user's progress and adjusts the content and timing of feedback according to the degree of achievement and improvement. For example, if a user achieves a goal, it sends a message of praise to further motivate them. If progress has stalled, it provides encouraging messages and advice for improvement. This ensures that users always receive appropriate support and can maintain their motivation to continue training. Furthermore, the feedback unit collects user feedback and uses it to improve the content and methods of feedback. This enables it to provide more effective support to users.

[0034] The intervention unit can intervene in critical situations and high-stress situations. For example, the intervention unit can use AI to monitor the user's psychological state in real time and intervene immediately in critical situations and high-stress situations. For instance, the intervention unit can provide emergency support and coping strategies when a user faces a major stress event. The intervention unit can also respond quickly if the user's psychological state deteriorates rapidly. For example, the intervention unit can provide coping methods such as relaxation techniques and breathing exercises when a user is in a panic state. In this way, intervening in critical situations and high-stress situations can enhance the user's psychological support.

[0035] The liaison department can collaborate with family members and professionals with the user's permission. For example, the liaison department can use AI to share information about the user's mental state with family members and professionals. For instance, the liaison department can notify family members and professionals of changes and progress in the user's mental state. Furthermore, with the user's permission, the liaison department can maintain regular contact with family members and professionals. For example, the liaison department can provide family members and professionals with regular reports on the user's mental state. This allows for the creation of a comprehensive support system by collaborating with family members and professionals with the user's permission.

[0036] The analytics department can analyze users' psychological states through daily check-ins and interactions. For example, the analytics department uses AI to analyze users' daily check-in data and conversation content. For instance, the analytics department extracts users' stress levels and emotional states from daily check-in data. The analytics department can also analyze the content of conversations with users to understand changes in their psychological state. For example, the analytics department analyzes the emotional expressions and word choices that users show during conversations to evaluate their psychological state. This allows for more accurate analysis of users' psychological states through daily check-ins and interactions.

[0037] The service provider can offer individualized resilience enhancement programs. For example, the service provider can use AI to create a resilience enhancement program tailored to the user. For instance, the service provider can provide a program that teaches skills such as stress management, emotional control, and building positive thinking patterns, based on the user's psychological state and progress. The service provider can also customize the program content according to the user's needs. For example, the service provider can provide training to help the user cope with specific stressors. By providing individualized resilience enhancement programs, the service provider can enhance the user's mental resilience and adaptability.

[0038] The tracking unit can track user progress. For example, it uses AI to collect and analyze user progress data. For instance, it evaluates user achievement, improvement, and goal attainment rates. Furthermore, the tracking unit can periodically collect user progress data to understand long-term progress. For example, it can graph and visually display user progress data. This allows for tracking user progress, understanding user growth, and providing appropriate support.

[0039] The feedback system can provide real-time feedback and encouragement. For example, it can use AI to analyze user progress data and provide appropriate feedback. For instance, it can adjust the content and timing of feedback according to the user's progress. Furthermore, the feedback system can provide encouraging messages to maintain user motivation. For example, it can send a message of praise when a user achieves a goal. By providing real-time feedback and encouragement, it can maintain user motivation and promote growth.

[0040] The analytics department can analyze changes in users' psychological states over the long term based on daily check-ins and conversation content. For example, the analytics department uses AI to analyze users' daily check-in data and conversation content to understand long-term changes in their psychological state. For example, the analytics department collects users' daily check-in data and analyzes changes in their psychological state by graphing them. The analytics department can also analyze conversation content with users and track changes in their emotions over time. For example, the analytics department statistically analyzes changes in users' psychological states to understand long-term trends. In this way, by analyzing daily check-ins and conversation content over the long term, it is possible to understand changes in users' psychological states.

[0041] The analysis unit can predict a user's current psychological state by referring to their past psychological state data. For example, the analysis unit uses AI to analyze a user's past psychological state data and predict their current state. For instance, the analysis unit estimates a user's current psychological state based on their past psychological state data. Furthermore, the analysis unit can learn a user's past emotional patterns and predict their current psychological state. For example, the analysis unit predicts a user's current psychological state based on their past psychological state data and proposes appropriate coping strategies. This allows the system to predict the current psychological state and provide appropriate coping strategies by referring to past psychological state data.

[0042] The analysis department can analyze a user's psychological state by considering their lifestyle and behavioral patterns. For example, the analysis department can use AI to analyze a user's lifestyle and behavioral patterns and reflect this in the analysis of their psychological state. For example, the analysis department can analyze a user's sleep patterns and reflect this in the analysis of their psychological state. The analysis department can also analyze a user's eating habits and reflect this in the analysis of their psychological state. For example, the analysis department can analyze a user's exercise habits and reflect this in the analysis of their psychological state. By considering the user's lifestyle and behavioral patterns, a more accurate analysis of their psychological state becomes possible.

[0043] The analysis unit can analyze users' social media activity and detect changes in their psychological state. For example, the analysis unit can use AI to analyze the content of users' social media posts and detect changes in their psychological state. For example, the analysis unit can analyze users' social media interaction patterns and detect changes in their psychological state. Furthermore, the analysis unit can analyze users' emotional expressions on social media and detect changes in their psychological state. For example, the analysis unit can analyze users' social media activity and detect changes in their psychological state at an early stage. In this way, by analyzing social media activity, changes in psychological state can be detected at an early stage.

[0044] The service provider can adjust the difficulty level of the resilience enhancement program in stages according to the user's progress. For example, the service provider can use AI to analyze the user's progress data and adjust the program's difficulty level. For example, the service provider can adjust the program's difficulty level in stages according to the user's skill level. The service provider can also adjust the program's difficulty level based on user feedback. For example, the service provider can adjust the program's difficulty level according to the user's progress. This allows for support tailored to the user's growth by adjusting the program's difficulty level according to their progress.

[0045] The service provider can offer customized resilience enhancement programs tailored to the user's specific challenges. For example, the service provider can use AI to create appropriate programs for the user's specific challenges. For instance, the service provider can offer programs specifically focused on stress management. It can also offer programs specifically focused on emotional control. For example, the service provider can offer programs specifically focused on building positive thinking patterns. This allows for support tailored to the user's needs by providing customized programs for specific challenges.

[0046] The service provider can optimize the delivery schedule of the resilience enhancement program to match the user's lifestyle. For example, the service provider can use AI to analyze the user's lifestyle and adjust the program delivery schedule. For example, the service provider can adjust the program delivery schedule to match the user's sleep pattern. The service provider can also adjust the program delivery schedule to match the user's work or study schedule. For example, the service provider can propose an optimal program delivery schedule based on the user's lifestyle. By optimizing the delivery schedule to match the user's lifestyle, the program can be delivered without disrupting the user's life.

[0047] The service provider can personalize program content based on the user's hobbies and interests. For example, the service provider can use AI to analyze the user's hobbies and interests and customize the program content. For instance, the service provider can provide a program that includes content related to the user's hobbies. Furthermore, the service provider can customize program content based on the user's interests. For example, the service provider can provide a program related to the user's favorite activities. By personalizing programs based on hobbies and interests, the service provider can increase the user's motivation.

[0048] The service provider can personalize program content based on the user's hobbies and interests. For example, the service provider can use AI to analyze the user's hobbies and interests and customize the program content. For instance, the service provider can provide a program that includes content related to the user's hobbies. Furthermore, the service provider can customize program content based on the user's interests. For example, the service provider can provide a program related to the user's favorite activities. By personalizing programs based on hobbies and interests, the service provider can increase the user's motivation.

[0049] The tracking unit can periodically collect user progress data and track long-term progress. For example, the tracking unit can use AI to periodically collect user progress data and understand long-term progress. For example, the tracking unit can collect user progress data daily and track long-term progress. Furthermore, the tracking unit can collect user progress data weekly and track long-term progress. For example, the tracking unit can collect user progress data monthly and track long-term progress. This allows for understanding long-term progress by regularly collecting progress data.

[0050] The tracking unit can analyze user progress data and detect specific patterns and trends. For example, the tracking unit can use AI to analyze user progress data and detect specific patterns and trends. For instance, the tracking unit can analyze user progress data and detect specific patterns. Furthermore, the tracking unit can analyze user progress data and detect specific trends. For example, the tracking unit can analyze user progress data and detect trends of stagnation or improvement in progress. This allows for the identification of specific patterns and trends by analyzing progress data.

[0051] The tracking unit can adjust the frequency of progress data collection according to the user's living environment and circumstances. For example, the tracking unit can use AI to analyze the user's living environment and circumstances and adjust the frequency of progress data collection. For example, the tracking unit can adjust the frequency of progress data collection to match the user's work or study schedule. The tracking unit can also adjust the frequency of progress data collection based on the user's daily rhythm. For example, the tracking unit can adjust the frequency of progress data collection according to the user's specific circumstances. By adjusting the collection frequency according to the living environment and circumstances, more appropriate data collection becomes possible.

[0052] The tracking unit can improve its method of collecting progress data based on user feedback. For example, the tracking unit can use AI to analyze user feedback and improve its method of collecting progress data. For example, the tracking unit can adjust its method of collecting progress data based on user opinions. Furthermore, the tracking unit can optimize its method of collecting progress data according to user requests. For example, the tracking unit improves its method of collecting progress data based on user feedback. This allows for more appropriate data collection by improving the collection method based on feedback.

[0053] The feedback unit can adjust the frequency and timing of feedback according to the user's progress. For example, the feedback unit can use AI to analyze the user's progress data and adjust the frequency and timing of feedback. For example, the feedback unit can adjust the timing of feedback according to the user's skill level. Furthermore, the feedback unit can adjust the frequency and timing of feedback based on the user's feedback. For example, the feedback unit can adjust the frequency of feedback according to the user's progress. This allows for more appropriate support by adjusting the frequency and timing of feedback according to progress.

[0054] The feedback unit can provide specific feedback on a user's particular actions and achievements. For example, the feedback unit can use AI to analyze a user's specific actions and achievements and provide specific feedback. For instance, if a user improves their stress management skills, the feedback unit can provide specific feedback. Similarly, if a user improves their emotional control skills, the feedback unit can provide specific feedback. For example, if a user develops positive thought patterns, the feedback unit can provide specific feedback. This allows for user growth by providing specific feedback on particular actions and achievements.

[0055] The feedback unit can optimize the feedback delivery schedule to match the user's lifestyle. For example, the feedback unit can use AI to analyze the user's lifestyle and adjust the feedback delivery schedule. For instance, the feedback unit can adjust the feedback delivery schedule to match the user's sleep patterns. Furthermore, the feedback unit can adjust the feedback delivery schedule to match the user's work or study schedule. For example, the feedback unit can propose an optimal feedback delivery schedule based on the user's lifestyle. By optimizing the delivery schedule to match the user's lifestyle, feedback can be provided without disrupting their daily life.

[0056] The feedback system can personalize the content of feedback based on the user's hobbies and interests. For example, the feedback system can use AI to analyze the user's hobbies and interests and customize the feedback content. For instance, the feedback system can provide feedback that includes content related to the user's hobbies. Furthermore, the feedback system can customize the content of feedback based on the user's interests. For example, the feedback system can provide feedback related to the user's favorite activities. By personalizing feedback based on hobbies and interests, user motivation can be increased.

[0057] The intervention unit can select the optimal intervention method by referring to the user's past data during critical situations or periods of high stress. For example, the intervention unit can use AI to analyze the user's past data and select the optimal intervention method. For instance, it can analyze the user's past stress data and select the optimal intervention method. Furthermore, the intervention unit can analyze the user's past emotional data and select the optimal intervention method. For example, it can analyze the user's past psychological state data and select the optimal intervention method. This allows the system to select the optimal intervention method during critical situations or periods of high stress by referring to past data.

[0058] The intervention unit can provide customized intervention plans tailored to the user's specific situation. For example, the intervention unit can use AI to create intervention plans based on the user's specific circumstances. For instance, the intervention unit can provide intervention plans specifically focused on stress management. It can also provide intervention plans specifically focused on emotional regulation. For example, the intervention unit can provide intervention plans specifically focused on building positive thinking patterns. This allows for support tailored to the user's needs by providing customized intervention plans based on specific situations.

[0059] The intervention unit can adjust the frequency and method of intervention according to the user's living environment and circumstances. For example, the intervention unit can use AI to analyze the user's living environment and circumstances and adjust the frequency and method of intervention. For example, the intervention unit can adjust the frequency and method of intervention to match the user's work or study schedule. Furthermore, the intervention unit can adjust the frequency and method of intervention based on the user's daily rhythm. For example, the intervention unit can adjust the frequency and method of intervention according to the user's specific circumstances. By adjusting the frequency and method of intervention according to the living environment and circumstances, more appropriate intervention becomes possible.

[0060] The intervention unit can improve its intervention methods based on user feedback. For example, the intervention unit can use AI to analyze user feedback and improve its intervention methods. For example, the intervention unit can adjust its intervention methods based on user opinions. Furthermore, the intervention unit can optimize its intervention methods according to user requests. For example, the intervention unit improves its intervention methods based on user feedback. This allows for more appropriate interventions by improving intervention methods based on feedback.

[0061] The liaison unit can provide appropriate information to family members and professionals with the user's permission. For example, the liaison unit can use AI to provide information about the user's psychological state to family members and professionals. For example, with the user's permission, the liaison unit can provide information about the user's psychological state to family members. The liaison unit can also provide information about the user's psychological state to professionals with the user's permission. For example, with the user's permission, the liaison unit can provide information about the user's progress to family members and professionals. This allows for the creation of a comprehensive support system by providing appropriate information with the user's permission.

[0062] The collaboration unit can optimize the method of collaboration with family and professionals according to the user's living environment and circumstances. For example, the collaboration unit can use AI to analyze the user's living environment and circumstances and optimize the collaboration method. For example, the collaboration unit can adjust the method of collaboration with family and professionals to match the user's work or study schedule. Furthermore, the collaboration unit can adjust the method of collaboration with family and professionals based on the user's daily rhythm. For example, the collaboration unit can adjust the method of collaboration with family and professionals according to the user's specific circumstances. By optimizing the collaboration method according to the living environment and circumstances, more appropriate support becomes possible.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The AI ​​resilience coach can refer to the user's past psychological state data when analyzing their current state. For example, the analysis unit can more accurately assess the user's current psychological state based on data on their past stress levels and emotional states. Furthermore, the analysis unit can use past data to understand patterns of changes in the user's psychological state and predict their future state. In addition, based on past data, the analysis unit can identify situations in which the user is prone to stress and suggest preventative measures. This allows for more accurate analysis and prediction of psychological states by utilizing past data.

[0065] The AI ​​Resilience Coach can consider a user's social media activity when analyzing their psychological state. For example, the analysis unit can analyze the content of a user's social media posts to detect changes in their emotions. It can also analyze a user's social media interaction patterns to understand changes in their psychological state. Furthermore, the analysis unit can analyze a user's emotional expressions on social media to detect changes in their psychological state at an early stage. This allows for earlier detection of changes in a user's psychological state and the implementation of appropriate countermeasures by analyzing their social media activity.

[0066] The AI ​​Resilience Coach can consider the user's lifestyle and behavioral patterns when analyzing their psychological state. For example, the analysis unit can analyze the user's sleep patterns and incorporate them into the analysis of their psychological state. It can also analyze the user's eating habits and incorporate them into the analysis of their psychological state. Furthermore, it can analyze the user's exercise habits and incorporate them into the analysis of their psychological state. This allows for a more accurate analysis of the user's psychological state by considering their lifestyle and behavioral patterns.

[0067] The AI ​​resilience coach can predict a user's current psychological state by referencing their past psychological state data when analyzing their current state. For example, the analysis unit estimates the current psychological state based on the user's past psychological state data. The analysis unit can also learn the user's past emotional patterns and predict their current psychological state. Furthermore, the analysis unit can predict the current psychological state based on the user's past psychological state data and suggest appropriate coping strategies. In this way, by referencing past psychological state data, it can predict the current psychological state and provide appropriate coping strategies.

[0068] The AI ​​Resilience Coach can analyze changes in a user's psychological state over the long term, based on daily check-ins and conversation content. For example, the analysis department analyzes the user's daily check-in data and conversation content to understand long-term changes in their psychological state. The analysis department can also collect the user's daily check-in data and analyze changes in their psychological state by graphing them. Furthermore, the analysis department can analyze the content of conversations with the user and track changes in their emotions over time. This allows for a long-term analysis based on daily check-ins and conversation content to understand changes in the user's psychological state.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The analysis unit analyzes the user's psychological state. For example, AI can be used to measure the user's stress level, emotional state, and psychological health. Step 2: The service provider offers individualized resilience enhancement programs based on the results analyzed by the analysis department. For example, they might use AI to determine the appropriate training content and session frequency for each user, and provide programs to help them learn skills such as stress management, emotional control, and building positive thinking patterns. Step 3: The tracking unit tracks the user's progress. For example, it uses AI to evaluate the user's achievement level, improvement level, and goal achievement rate, and regularly collects progress data to understand long-term progress. Step 4: The feedback unit provides real-time feedback and encouragement based on the progress data obtained by the tracking unit. For example, it uses AI to provide appropriate feedback to the user and adjusts the frequency and timing of feedback according to the progress.

[0071] (Example of form 2) An AI resilience coach according to an embodiment of the present invention is a personalized AI assistant service for enhancing a user's mental resilience and adaptability. This AI resilience coach analyzes the user's psychological state through daily check-ins and interactions, and provides an individualized resilience enhancement program. Through short daily sessions, the user can learn skills such as stress management, emotional control, and building positive thought patterns. The AI ​​coach tracks the user's progress and provides real-time feedback and encouragement. Furthermore, the AI ​​resilience coach intervenes immediately in times of crisis or high stress, providing urgent support and coping strategies. In addition, with the user's permission, it collaborates with family and professionals to build a comprehensive support system. For example, the AI ​​resilience coach helps the user continuously cultivate resilience in their daily life, enabling them to effectively cope with various life challenges. For instance, the user can deepen their self-understanding and develop the ability to recover positively even in difficult situations. In this way, the AI ​​resilience coach can enhance the user's mental resilience and adaptability.

[0072] The AI ​​resilience coach according to this embodiment comprises an analysis unit, a provision unit, a tracking unit, and a feedback unit. The analysis unit analyzes the user's psychological state. The analysis unit analyzes the user's psychological state using, for example, AI. For example, the analysis unit can measure the user's stress level, emotional state, psychological health, etc. The provision unit provides an individualized resilience enhancement program based on the results analyzed by the analysis unit. The provision unit determines, for example, the training content and session frequency appropriate for the user using AI. For example, the provision unit provides a program that teaches skills such as stress management, emotional control, and building positive thinking patterns according to the user's psychological state. The tracking unit tracks the user's progress. The tracking unit evaluates the user's achievement level, improvement level, goal achievement rate, etc., using, for example, AI. For example, the tracking unit periodically collects user progress data to grasp long-term progress. The feedback unit provides real-time feedback and encouragement based on the progress data obtained by the tracking unit. The feedback unit provides appropriate feedback to the user using, for example, AI. For example, the feedback unit adjusts the frequency and timing of feedback according to the user's progress. This allows the AI ​​resilience coach according to the embodiment to analyze the user's psychological state, provide a personalized resilience enhancement program, track progress, and provide real-time feedback and encouragement.

[0073] The analysis department analyzes the user's psychological state. Specifically, it uses AI to analyze the user's psychological state. The AI ​​collects text data, voice data, and biosensor data entered by the user, and uses this data to measure the user's stress level, emotional state, and psychological health. For example, it analyzes text data entered by the user in a diary format using natural language processing technology to perform emotional analysis. This makes it possible to detect changes in the user's emotions and signs of stress. In addition, by analyzing voice data, it is possible to infer the emotional state from changes in voice tone and speaking style. Furthermore, it analyzes biosensor data such as heart rate and skin electrical activity obtained from wearable devices worn by the user to quantitatively evaluate the stress level. In this way, the analysis department can integrate multifaceted data to gain a detailed understanding of the user's psychological state.

[0074] The service provider offers personalized resilience enhancement programs based on the results analyzed by the analysis department. Specifically, AI is used to determine the appropriate training content and session frequency for each user. The AI ​​considers the user's psychological state and past training history to generate the optimal program. For example, it may offer breathing exercises and meditation sessions for stress management, cognitive behavioral therapy exercises for emotional control, and positive psychology workshops for building positive thinking patterns. These programs are customized to the user's needs and can effectively enhance resilience. The service provider also collects user feedback in real time and adjusts the program content and progress as needed. This ensures that users always receive optimal training and can experience improved resilience.

[0075] The tracking unit monitors the user's progress. Specifically, it uses AI to evaluate the user's achievement level, improvement level, and goal attainment rate. The AI ​​collects data from the user's training and uses this data to quantitatively evaluate their progress. For example, it records the number of exercises performed, the duration, and the number of goals achieved, and analyzes this data to evaluate progress. It also tracks changes in the user's psychological state to evaluate the effectiveness of the training. For example, it analyzes the results of regularly conducted psychological state assessment tests to understand changes in the user's stress level and emotional state. This allows the tracking unit to grasp the user's long-term progress in detail and continuously evaluate the effectiveness of the training.

[0076] The feedback unit provides real-time feedback and encouragement based on progress data obtained by the tracking unit. Specifically, it uses AI to provide appropriate feedback to users. The AI ​​analyzes the user's progress and adjusts the content and timing of feedback according to the degree of achievement and improvement. For example, if a user achieves a goal, it sends a message of praise to further motivate them. If progress has stalled, it provides encouraging messages and advice for improvement. This ensures that users always receive appropriate support and can maintain their motivation to continue training. Furthermore, the feedback unit collects user feedback and uses it to improve the content and methods of feedback. This enables it to provide more effective support to users.

[0077] The intervention unit can intervene in critical situations and high-stress situations. For example, the intervention unit can use AI to monitor the user's psychological state in real time and intervene immediately in critical situations and high-stress situations. For instance, the intervention unit can provide emergency support and coping strategies when a user faces a major stress event. The intervention unit can also respond quickly if the user's psychological state deteriorates rapidly. For example, the intervention unit can provide coping methods such as relaxation techniques and breathing exercises when a user is in a panic state. In this way, intervening in critical situations and high-stress situations can enhance the user's psychological support.

[0078] The liaison department can collaborate with family members and professionals with the user's permission. For example, the liaison department can use AI to share information about the user's mental state with family members and professionals. For instance, the liaison department can notify family members and professionals of changes and progress in the user's mental state. Furthermore, with the user's permission, the liaison department can maintain regular contact with family members and professionals. For example, the liaison department can provide family members and professionals with regular reports on the user's mental state. This allows for the creation of a comprehensive support system by collaborating with family members and professionals with the user's permission.

[0079] The analytics department can analyze users' psychological states through daily check-ins and interactions. For example, the analytics department uses AI to analyze users' daily check-in data and conversation content. For instance, the analytics department extracts users' stress levels and emotional states from daily check-in data. The analytics department can also analyze the content of conversations with users to understand changes in their psychological state. For example, the analytics department analyzes the emotional expressions and word choices that users show during conversations to evaluate their psychological state. This allows for more accurate analysis of users' psychological states through daily check-ins and interactions.

[0080] The service provider can offer individualized resilience enhancement programs. For example, the service provider can use AI to create a resilience enhancement program tailored to the user. For instance, the service provider can provide a program that teaches skills such as stress management, emotional control, and building positive thinking patterns, based on the user's psychological state and progress. The service provider can also customize the program content according to the user's needs. For example, the service provider can provide training to help the user cope with specific stressors. By providing individualized resilience enhancement programs, the service provider can enhance the user's mental resilience and adaptability.

[0081] The tracking unit can track user progress. For example, it uses AI to collect and analyze user progress data. For instance, it evaluates user achievement, improvement, and goal attainment rates. Furthermore, the tracking unit can periodically collect user progress data to understand long-term progress. For example, it can graph and visually display user progress data. This allows for tracking user progress, understanding user growth, and providing appropriate support.

[0082] The feedback system can provide real-time feedback and encouragement. For example, it can use AI to analyze user progress data and provide appropriate feedback. For instance, it can adjust the content and timing of feedback according to the user's progress. Furthermore, the feedback system can provide encouraging messages to maintain user motivation. For example, it can send a message of praise when a user achieves a goal. By providing real-time feedback and encouragement, it can maintain user motivation and promote growth.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis method of their psychological state based on those estimated emotions. For example, the analysis unit can use AI to analyze the user's emotions and adjust the analysis method of their psychological state. For instance, if the user is stressed, the analysis unit will adopt an analysis method that focuses on stress reduction. If the user is relaxed, the analysis unit can adopt an analysis method that evaluates the long-term stability of their psychological state. For example, if the user is agitated, the analysis unit will adopt an analysis method that analyzes emotional fluctuations and suggests appropriate coping strategies. By adjusting the analysis method based on the user's emotions, a more accurate analysis of their psychological state becomes possible.

[0084] The analytics department can analyze changes in users' psychological states over the long term based on daily check-ins and conversation content. For example, the analytics department uses AI to analyze users' daily check-in data and conversation content to understand long-term changes in their psychological state. For example, the analytics department collects users' daily check-in data and analyzes changes in their psychological state by graphing them. The analytics department can also analyze conversation content with users and track changes in their emotions over time. For example, the analytics department statistically analyzes changes in users' psychological states to understand long-term trends. In this way, by analyzing daily check-ins and conversation content over the long term, it is possible to understand changes in users' psychological states.

[0085] The analysis unit can predict a user's current psychological state by referring to their past psychological state data. For example, the analysis unit uses AI to analyze a user's past psychological state data and predict their current state. For instance, the analysis unit estimates a user's current psychological state based on their past psychological state data. Furthermore, the analysis unit can learn a user's past emotional patterns and predict their current psychological state. For example, the analysis unit predicts a user's current psychological state based on their past psychological state data and proposes appropriate coping strategies. This allows the system to predict the current psychological state and provide appropriate coping strategies by referring to past psychological state data.

[0086] The analysis unit can estimate the user's emotions and prioritize analysis results based on those estimated emotions. For example, the analysis unit can use AI to analyze the user's emotions and prioritize the analysis results. For instance, if the user is feeling stressed, the analysis unit will prioritize displaying stress-related analysis results. Similarly, if the user is relaxed, the analysis unit can prioritize displaying analysis results related to long-term psychological stability. For example, if the user is excited, the analysis unit will prioritize displaying analysis results related to emotional fluctuations. By prioritizing analysis results based on the user's emotions, the system can prioritize the provision of important information.

[0087] The analysis department can analyze a user's psychological state by considering their lifestyle and behavioral patterns. For example, the analysis department can use AI to analyze a user's lifestyle and behavioral patterns and reflect this in the analysis of their psychological state. For example, the analysis department can analyze a user's sleep patterns and reflect this in the analysis of their psychological state. The analysis department can also analyze a user's eating habits and reflect this in the analysis of their psychological state. For example, the analysis department can analyze a user's exercise habits and reflect this in the analysis of their psychological state. By considering the user's lifestyle and behavioral patterns, a more accurate analysis of their psychological state becomes possible.

[0088] The analysis unit can analyze users' social media activity and detect changes in their psychological state. For example, the analysis unit can use AI to analyze the content of users' social media posts and detect changes in their psychological state. For example, the analysis unit can analyze users' social media interaction patterns and detect changes in their psychological state. Furthermore, the analysis unit can analyze users' emotional expressions on social media and detect changes in their psychological state. For example, the analysis unit can analyze users' social media activity and detect changes in their psychological state at an early stage. In this way, by analyzing social media activity, changes in psychological state can be detected at an early stage.

[0089] The service provider can estimate the user's emotions and adjust the content of the resilience enhancement program based on those emotions. For example, the service provider can use AI to analyze the user's emotions and adjust the content of the resilience enhancement program. For instance, if the user is feeling stressed, the service provider can provide a program focused on stress reduction. If the user is relaxed, the service provider can provide a program aimed at long-term psychological stability. For example, if the user is excited, the service provider can provide a program to control emotional fluctuations. By adjusting the program content based on the user's emotions, more effective resilience enhancement becomes possible.

[0090] The service provider can adjust the difficulty level of the resilience enhancement program in stages according to the user's progress. For example, the service provider can use AI to analyze the user's progress data and adjust the program's difficulty level. For example, the service provider can adjust the program's difficulty level in stages according to the user's skill level. The service provider can also adjust the program's difficulty level based on user feedback. For example, the service provider can adjust the program's difficulty level according to the user's progress. This allows for support tailored to the user's growth by adjusting the program's difficulty level according to their progress.

[0091] The service provider can offer customized resilience enhancement programs tailored to the user's specific challenges. For example, the service provider can use AI to create appropriate programs for the user's specific challenges. For instance, the service provider can offer programs specifically focused on stress management. It can also offer programs specifically focused on emotional control. For example, the service provider can offer programs specifically focused on building positive thinking patterns. This allows for support tailored to the user's needs by providing customized programs for specific challenges.

[0092] The service provider can estimate the user's emotions and adjust the timing of program delivery based on those emotions. For example, the service provider can use AI to analyze the user's emotions and adjust the timing of program delivery. For instance, if the user is feeling stressed, the service provider can deliver the program immediately. Conversely, if the user is relaxed, the service provider can deliver the program at an appropriate time. For example, if the user is excited, the service provider can deliver the program at a time that controls the fluctuations in their emotions. By adjusting the delivery timing based on the user's emotions, more effective support becomes possible.

[0093] The service provider can optimize the delivery schedule of the resilience enhancement program to match the user's lifestyle. For example, the service provider can use AI to analyze the user's lifestyle and adjust the program delivery schedule. For example, the service provider can adjust the program delivery schedule to match the user's sleep pattern. The service provider can also adjust the program delivery schedule to match the user's work or study schedule. For example, the service provider can propose an optimal program delivery schedule based on the user's lifestyle. By optimizing the delivery schedule to match the user's lifestyle, the program can be delivered without disrupting the user's life.

[0094] The service provider can personalize program content based on the user's hobbies and interests. For example, the service provider can use AI to analyze the user's hobbies and interests and customize the program content. For instance, the service provider can provide a program that includes content related to the user's hobbies. Furthermore, the service provider can customize program content based on the user's interests. For example, the service provider can provide a program related to the user's favorite activities. By personalizing programs based on hobbies and interests, the service provider can increase the user's motivation.

[0095] The service provider can personalize program content based on the user's hobbies and interests. For example, the service provider can use AI to analyze the user's hobbies and interests and customize the program content. For instance, the service provider can provide a program that includes content related to the user's hobbies. Furthermore, the service provider can customize program content based on the user's interests. For example, the service provider can provide a program related to the user's favorite activities. By personalizing programs based on hobbies and interests, the service provider can increase the user's motivation.

[0096] The tracking unit can estimate the user's emotions and adjust the method of collecting progress data based on the estimated emotions. For example, the tracking unit can use AI to analyze the user's emotions and adjust the method of collecting progress data. For instance, if the user is stressed, the tracking unit can adopt a simplified method of collecting progress data. Conversely, if the user is relaxed, the tracking unit can adopt a more detailed method of collecting progress data. For example, if the user is excited, the tracking unit can adopt a method of collecting progress data that reflects emotional fluctuations. By adjusting the method of collecting progress data based on the user's emotions, more appropriate data collection becomes possible.

[0097] The tracking unit can periodically collect user progress data and track long-term progress. For example, the tracking unit can use AI to periodically collect user progress data and understand long-term progress. For example, the tracking unit can collect user progress data daily and track long-term progress. Furthermore, the tracking unit can collect user progress data weekly and track long-term progress. For example, the tracking unit can collect user progress data monthly and track long-term progress. This allows for understanding long-term progress by regularly collecting progress data.

[0098] The tracking unit can analyze user progress data and detect specific patterns and trends. For example, the tracking unit can use AI to analyze user progress data and detect specific patterns and trends. For instance, the tracking unit can analyze user progress data and detect specific patterns. Furthermore, the tracking unit can analyze user progress data and detect specific trends. For example, the tracking unit can analyze user progress data and detect trends of stagnation or improvement in progress. This allows for the identification of specific patterns and trends by analyzing progress data.

[0099] The tracking unit can estimate the user's emotions and adjust the display method of progress data based on the estimated emotions. For example, the tracking unit can use AI to analyze the user's emotions and adjust the display method of progress data. For instance, if the user is stressed, the tracking unit can provide a simple and highly visible display method. If the user is relaxed, the tracking unit can provide a display method that includes detailed information. For example, if the user is excited, the tracking unit can provide a visually stimulating display method. By adjusting the display method based on the user's emotions, more appropriate data display becomes possible.

[0100] The tracking unit can adjust the frequency of progress data collection according to the user's living environment and circumstances. For example, the tracking unit can use AI to analyze the user's living environment and circumstances and adjust the frequency of progress data collection. For example, the tracking unit can adjust the frequency of progress data collection to match the user's work or study schedule. The tracking unit can also adjust the frequency of progress data collection based on the user's daily rhythm. For example, the tracking unit can adjust the frequency of progress data collection according to the user's specific circumstances. By adjusting the collection frequency according to the living environment and circumstances, more appropriate data collection becomes possible.

[0101] The tracking unit can improve its method of collecting progress data based on user feedback. For example, the tracking unit can use AI to analyze user feedback and improve its method of collecting progress data. For example, the tracking unit can adjust its method of collecting progress data based on user opinions. Furthermore, the tracking unit can optimize its method of collecting progress data according to user requests. For example, the tracking unit improves its method of collecting progress data based on user feedback. This allows for more appropriate data collection by improving the collection method based on feedback.

[0102] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, the feedback unit can use AI to analyze the user's emotions and adjust the content of the feedback. For instance, if the user is stressed, the feedback unit can provide feedback focused on stress reduction. If the user is relaxed, the feedback unit can provide feedback regarding the long-term stability of their psychological state. For example, if the user is excited, the feedback unit can provide feedback to help control emotional fluctuations. By adjusting the content of the feedback based on the user's emotions, more appropriate feedback becomes possible.

[0103] The feedback unit can adjust the frequency and timing of feedback according to the user's progress. For example, the feedback unit can use AI to analyze the user's progress data and adjust the frequency and timing of feedback. For example, the feedback unit can adjust the timing of feedback according to the user's skill level. Furthermore, the feedback unit can adjust the frequency and timing of feedback based on the user's feedback. For example, the feedback unit can adjust the frequency of feedback according to the user's progress. This allows for more appropriate support by adjusting the frequency and timing of feedback according to progress.

[0104] The feedback unit can provide specific feedback on a user's particular actions and achievements. For example, the feedback unit can use AI to analyze a user's specific actions and achievements and provide specific feedback. For instance, if a user improves their stress management skills, the feedback unit can provide specific feedback. Similarly, if a user improves their emotional control skills, the feedback unit can provide specific feedback. For example, if a user develops positive thought patterns, the feedback unit can provide specific feedback. This allows for user growth by providing specific feedback on particular actions and achievements.

[0105] The feedback unit can estimate the user's emotions and adjust encouraging messages based on those emotions. For example, the feedback unit can use AI to analyze the user's emotions and adjust encouraging messages accordingly. For instance, if the user is feeling stressed, the feedback unit can provide encouraging messages. Conversely, if the user is relaxed, it can provide positive messages. For example, if the user is excited, the feedback unit can provide messages to help manage emotional fluctuations. This allows for more appropriate support by adjusting encouraging messages based on the user's emotions.

[0106] The feedback unit can optimize the feedback delivery schedule to match the user's lifestyle. For example, the feedback unit can use AI to analyze the user's lifestyle and adjust the feedback delivery schedule. For instance, the feedback unit can adjust the feedback delivery schedule to match the user's sleep patterns. Furthermore, the feedback unit can adjust the feedback delivery schedule to match the user's work or study schedule. For example, the feedback unit can propose an optimal feedback delivery schedule based on the user's lifestyle. By optimizing the delivery schedule to match the user's lifestyle, feedback can be provided without disrupting their daily life.

[0107] The feedback system can personalize the content of feedback based on the user's hobbies and interests. For example, the feedback system can use AI to analyze the user's hobbies and interests and customize the feedback content. For instance, the feedback system can provide feedback that includes content related to the user's hobbies. Furthermore, the feedback system can customize the content of feedback based on the user's interests. For example, the feedback system can provide feedback related to the user's favorite activities. By personalizing feedback based on hobbies and interests, user motivation can be increased.

[0108] The intervention unit can estimate the user's emotions and adjust the intervention method based on the estimated emotions. For example, the intervention unit can use AI to analyze the user's emotions and adjust the intervention method. For instance, if the user is stressed, the intervention unit can provide an intervention method focused on stress reduction. If the user is relaxed, the intervention unit can provide an intervention method aimed at long-term psychological stability. For example, if the user is agitated, the intervention unit can provide an intervention method to control emotional fluctuations. By adjusting the intervention method based on the user's emotions, more appropriate intervention becomes possible.

[0109] The intervention unit can select the optimal intervention method by referring to the user's past data during critical situations or periods of high stress. For example, the intervention unit can use AI to analyze the user's past data and select the optimal intervention method. For instance, it can analyze the user's past stress data and select the optimal intervention method. Furthermore, the intervention unit can analyze the user's past emotional data and select the optimal intervention method. For example, it can analyze the user's past psychological state data and select the optimal intervention method. This allows the system to select the optimal intervention method during critical situations or periods of high stress by referring to past data.

[0110] The intervention unit can provide customized intervention plans tailored to the user's specific situation. For example, the intervention unit can use AI to create intervention plans based on the user's specific circumstances. For instance, the intervention unit can provide intervention plans specifically focused on stress management. It can also provide intervention plans specifically focused on emotional regulation. For example, the intervention unit can provide intervention plans specifically focused on building positive thinking patterns. This allows for support tailored to the user's needs by providing customized intervention plans based on specific situations.

[0111] The intervention unit can estimate the user's emotions and adjust the timing of interventions based on those emotions. For example, the intervention unit can use AI to analyze the user's emotions and adjust the timing of interventions. For instance, if the user is feeling stressed, the intervention unit will intervene immediately. Conversely, if the user is relaxed, the intervention unit can intervene at an appropriate time. For example, if the user is excited, the intervention unit will intervene at a time that controls the fluctuations in their emotions. By adjusting the timing of interventions based on the user's emotions, more appropriate interventions become possible.

[0112] The intervention unit can adjust the frequency and method of intervention according to the user's living environment and circumstances. For example, the intervention unit can use AI to analyze the user's living environment and circumstances and adjust the frequency and method of intervention. For example, the intervention unit can adjust the frequency and method of intervention to match the user's work or study schedule. Furthermore, the intervention unit can adjust the frequency and method of intervention based on the user's daily rhythm. For example, the intervention unit can adjust the frequency and method of intervention according to the user's specific circumstances. By adjusting the frequency and method of intervention according to the living environment and circumstances, more appropriate intervention becomes possible.

[0113] The intervention unit can improve its intervention methods based on user feedback. For example, the intervention unit can use AI to analyze user feedback and improve its intervention methods. For example, the intervention unit can adjust its intervention methods based on user opinions. Furthermore, the intervention unit can optimize its intervention methods according to user requests. For example, the intervention unit improves its intervention methods based on user feedback. This allows for more appropriate interventions by improving intervention methods based on feedback.

[0114] The collaboration unit can estimate the user's emotions and adjust the method of collaboration with family and professionals based on the estimated emotions. For example, the collaboration unit can use AI to analyze the user's emotions and adjust the collaboration method. For instance, if the user is feeling stressed, the collaboration unit can provide information on stress reduction to family and professionals. If the user is relaxed, the collaboration unit can provide information on long-term psychological stability. For example, if the user is agitated, the collaboration unit can provide information to help control emotional fluctuations. By adjusting the collaboration method based on the user's emotions, more appropriate support becomes possible.

[0115] The liaison unit can provide appropriate information to family members and professionals with the user's permission. For example, the liaison unit can use AI to provide information about the user's psychological state to family members and professionals. For example, with the user's permission, the liaison unit can provide information about the user's psychological state to family members. The liaison unit can also provide information about the user's psychological state to professionals with the user's permission. For example, with the user's permission, the liaison unit can provide information about the user's progress to family members and professionals. This allows for the creation of a comprehensive support system by providing appropriate information with the user's permission.

[0116] The collaboration unit can estimate the user's emotions and adjust the timing of collaboration based on those emotions. For example, the collaboration unit can use AI to analyze the user's emotions and adjust the timing of collaboration. For instance, if the user is feeling stressed, the collaboration unit will immediately collaborate with family or professionals. Conversely, if the user is relaxed, the collaboration unit can collaborate with family or professionals at the appropriate time. For example, if the user is agitated, the collaboration unit will collaborate with family or professionals at a time that helps control emotional fluctuations. By adjusting the timing of collaboration based on the user's emotions, more appropriate support becomes possible.

[0117] The collaboration unit can optimize the method of collaboration with family and professionals according to the user's living environment and circumstances. For example, the collaboration unit can use AI to analyze the user's living environment and circumstances and optimize the collaboration method. For example, the collaboration unit can adjust the method of collaboration with family and professionals to match the user's work or study schedule. Furthermore, the collaboration unit can adjust the method of collaboration with family and professionals based on the user's daily rhythm. For example, the collaboration unit can adjust the method of collaboration with family and professionals according to the user's specific circumstances. By optimizing the collaboration method according to the living environment and circumstances, more appropriate support becomes possible.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] The AI ​​resilience coach can refer to the user's past psychological state data when analyzing their current state. For example, the analysis unit can more accurately assess the user's current psychological state based on data on their past stress levels and emotional states. Furthermore, the analysis unit can use past data to understand patterns of changes in the user's psychological state and predict their future state. In addition, based on past data, the analysis unit can identify situations in which the user is prone to stress and suggest preventative measures. This allows for more accurate analysis and prediction of psychological states by utilizing past data.

[0120] The AI ​​Resilience Coach can consider a user's social media activity when analyzing their psychological state. For example, the analysis unit can analyze the content of a user's social media posts to detect changes in their emotions. It can also analyze a user's social media interaction patterns to understand changes in their psychological state. Furthermore, the analysis unit can analyze a user's emotional expressions on social media to detect changes in their psychological state at an early stage. This allows for earlier detection of changes in a user's psychological state and the implementation of appropriate countermeasures by analyzing their social media activity.

[0121] The AI ​​Resilience Coach can consider the user's lifestyle and behavioral patterns when analyzing their psychological state. For example, the analysis unit can analyze the user's sleep patterns and incorporate them into the analysis of their psychological state. It can also analyze the user's eating habits and incorporate them into the analysis of their psychological state. Furthermore, it can analyze the user's exercise habits and incorporate them into the analysis of their psychological state. This allows for a more accurate analysis of the user's psychological state by considering their lifestyle and behavioral patterns.

[0122] The AI ​​resilience coach can estimate the user's emotions when analyzing their psychological state and adjust the analysis method based on those emotions. For example, if the user is stressed, the analysis unit will adopt an analysis method focused on stress reduction. If the user is relaxed, the analysis unit can adopt an analysis method that evaluates the long-term stability of their psychological state. Furthermore, if the user is agitated, the analysis unit can adopt an analysis method that analyzes emotional fluctuations and suggests appropriate coping strategies. By adjusting the analysis method based on the user's emotions, a more accurate analysis of their psychological state becomes possible.

[0123] The AI ​​Resilience Coach can estimate the user's emotions when analyzing their psychological state and prioritize the analysis results based on those emotions. For example, if the user is feeling stressed, the analysis unit will prioritize displaying stress-related results. If the user is relaxed, the analysis unit can prioritize displaying results related to long-term psychological stability. Furthermore, if the user is agitated, the analysis unit can prioritize displaying results related to emotional fluctuations. By prioritizing analysis results based on the user's emotions, the AI ​​Resilience Coach can provide important information in a timely manner.

[0124] The AI ​​resilience coach can predict a user's current psychological state by referencing their past psychological state data when analyzing their current state. For example, the analysis unit estimates the current psychological state based on the user's past psychological state data. The analysis unit can also learn the user's past emotional patterns and predict their current psychological state. Furthermore, the analysis unit can predict the current psychological state based on the user's past psychological state data and suggest appropriate coping strategies. In this way, by referencing past psychological state data, it can predict the current psychological state and provide appropriate coping strategies.

[0125] The AI ​​Resilience Coach can analyze changes in a user's psychological state over the long term, based on daily check-ins and conversation content. For example, the analysis department analyzes the user's daily check-in data and conversation content to understand long-term changes in their psychological state. The analysis department can also collect the user's daily check-in data and analyze changes in their psychological state by graphing them. Furthermore, the analysis department can analyze the content of conversations with the user and track changes in their emotions over time. This allows for a long-term analysis based on daily check-ins and conversation content to understand changes in the user's psychological state.

[0126] The AI ​​resilience coach can estimate the user's emotions when analyzing their psychological state and adjust the analysis method based on those emotions. For example, if the user is stressed, the analysis unit will adopt an analysis method focused on stress reduction. If the user is relaxed, the analysis unit can adopt an analysis method that evaluates the long-term stability of their psychological state. Furthermore, if the user is agitated, the analysis unit can adopt an analysis method that analyzes emotional fluctuations and suggests appropriate coping strategies. By adjusting the analysis method based on the user's emotions, a more accurate analysis of their psychological state becomes possible.

[0127] The AI ​​Resilience Coach can estimate the user's emotions when analyzing their psychological state and prioritize the analysis results based on those emotions. For example, if the user is feeling stressed, the analysis unit will prioritize displaying stress-related results. If the user is relaxed, the analysis unit can prioritize displaying results related to long-term psychological stability. Furthermore, if the user is agitated, the analysis unit can prioritize displaying results related to emotional fluctuations. By prioritizing analysis results based on the user's emotions, the AI ​​Resilience Coach can provide important information in a timely manner.

[0128] The AI ​​Resilience Coach can estimate the user's emotions when analyzing their psychological state and prioritize the analysis results based on those emotions. For example, if the user is feeling stressed, the analysis unit will prioritize displaying stress-related results. If the user is relaxed, the analysis unit can prioritize displaying results related to long-term psychological stability. Furthermore, if the user is agitated, the analysis unit can prioritize displaying results related to emotional fluctuations. By prioritizing analysis results based on the user's emotions, the AI ​​Resilience Coach can provide important information in a timely manner.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The analysis unit analyzes the user's psychological state. For example, AI can be used to measure the user's stress level, emotional state, and psychological health. Step 2: The service provider offers individualized resilience enhancement programs based on the results analyzed by the analysis department. For example, they might use AI to determine the appropriate training content and session frequency for each user, and provide programs to help them learn skills such as stress management, emotional control, and building positive thinking patterns. Step 3: The tracking unit tracks the user's progress. For example, it uses AI to evaluate the user's achievement level, improvement level, and goal achievement rate, and regularly collects progress data to understand long-term progress. Step 4: The feedback unit provides real-time feedback and encouragement based on the progress data obtained by the tracking unit. For example, it uses AI to provide appropriate feedback to the user and adjusts the frequency and timing of feedback according to the progress.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the analysis unit, provision unit, tracking unit, feedback unit, intervention unit, and collaboration unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The tracking unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The feedback unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The intervention unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The collaboration unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the analysis unit, provision unit, tracking unit, feedback unit, intervention unit, and coordination unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The tracking unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The feedback unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The intervention unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The coordination unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the analysis unit, provision unit, tracking unit, feedback unit, intervention unit, and collaboration unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The tracking unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The feedback unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The intervention unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The collaboration unit is implemented, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.).

[0180] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0181] 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.

[0182] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0183] Each of the multiple elements described above, including the analysis unit, supply unit, tracking unit, feedback unit, intervention unit, and coordination unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The supply unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The tracking unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The feedback unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The intervention unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The coordination unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] (Note 1) The analysis department analyzes the psychological state of users, A provisioning unit provides individual resilience enhancement programs based on the results analyzed by the aforementioned analysis unit. A tracking unit that tracks the user's progress, A feedback unit provides real-time feedback and encouragement based on the progress data obtained by the tracking unit, Equipped with A system characterized by the following features. (Note 2) We have an intervention unit to intervene in critical situations and high-stress situations. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a collaboration unit that allows users to communicate with family members and professionals with their permission. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is We analyze the user's psychological state through daily check-ins and interactions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We offer individualized resilience enhancement programs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned tracking unit is Track user progress The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned feedback unit is Providing real-time feedback and encouragement The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is We estimate the user's emotions and adjust the method of analyzing their psychological state based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is We analyze changes in the user's psychological state over the long term based on daily check-ins and conversation content. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is By referring to the user's past psychological state data, we predict their current psychological state. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is Analyze the user's psychological state by considering their lifestyle and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is Analyze users' social media activity and detect changes in their psychological state. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, The program estimates the user's emotions and adjusts the content of the resilience enhancement program based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, The difficulty level of the resilience enhancement program will be adjusted in stages according to the user's progress. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, We provide customized resilience enhancement programs to address the specific challenges of our users. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the timing of program delivery based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, We optimize the delivery schedule of the resilience enhancement program to match the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, Personalize the program content based on the user's hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, Personalize the program content based on the user's hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned tracking unit is We estimate the user's emotions and adjust the method of collecting progress data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned tracking unit is Regularly collect user progress data and track long-term progress. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned tracking unit is Analyze user progress data to detect specific patterns and trends. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned tracking unit is It estimates the user's emotions and adjusts how progress data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned tracking unit is Adjust the frequency of progress data collection according to the user's living environment and circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned tracking unit is Based on user feedback, we will improve how we collect progress data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is Adjust the frequency and timing of feedback based on the user's progress. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is Provide specific feedback on the user's particular actions and results. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is It estimates the user's emotions and adjusts encouraging messages based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is Optimize the feedback delivery schedule to match the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is Personalize feedback based on the user's hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned intervention unit is It estimates the user's emotions and adjusts the intervention method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned intervention unit is In critical situations or times of high stress, the system selects the optimal intervention method by referring to the user's past data. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned intervention unit is We provide customized intervention plans tailored to the user's specific circumstances. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned intervention unit is It estimates the user's emotions and adjusts the timing of interventions based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned intervention unit is The frequency and method of intervention will be adjusted according to the user's living environment and circumstances. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned intervention unit is We will improve our intervention methods based on user feedback. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned linkage unit is, It estimates the user's emotions and adjusts how it interacts with family and professionals based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned linkage unit is, With the user's permission, we provide appropriate information to family members and professionals. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the timing of collaboration based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned linkage unit is, Optimize the method of collaboration with family and professionals according to the user's living environment and circumstances. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0203] 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. The analysis department analyzes the psychological state of users, A provisioning unit provides individual resilience enhancement programs based on the results analyzed by the aforementioned analysis unit. A tracking unit that tracks the user's progress, A feedback unit provides real-time feedback and encouragement based on the progress data obtained by the tracking unit, Equipped with A system characterized by the following features.

2. We have an intervention unit to intervene in critical situations and high-stress situations. The system according to feature 1.

3. It includes a collaboration unit that allows users to communicate with family members and professionals with their permission. The system according to feature 1.

4. The aforementioned analysis unit is We analyze the user's psychological state through daily check-ins and interactions. The system according to feature 1.

5. The aforementioned supply unit is, We offer individualized resilience enhancement programs. The system according to feature 1.

6. The aforementioned tracking unit is Track user progress The system according to feature 1.

7. The aforementioned feedback unit is Providing real-time feedback and encouragement The system according to feature 1.

8. The aforementioned analysis unit is We estimate the user's emotions and adjust the method of analyzing their psychological state based on the estimated emotions. The system according to feature 1.

9. The aforementioned analysis unit is We analyze changes in the user's psychological state over the long term based on daily check-ins and conversation content. The system according to feature 1.

10. The aforementioned analysis unit is By referring to the user's past psychological state data, we predict their current psychological state. The system according to feature 1.

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