System

Abstract

An object of a system according to an embodiment is to recognize an emotion of a user, propose an appropriate management method on the basis of the emotion, and improve communication skill.SOLUTION: A system according to an embodiment includes an emotion recognition unit, a management suggestion unit, a communication support unit, and an advice providing unit. The emotion recognition unit recognizes an emotion of a user. The management proposal unit proposes an appropriate management method on the basis of the emotion recognized by the emotion recognition unit. The communication support unit improves the communication skill of the user on the basis of the management method proposed by the management proposing unit. The advice providing unit provides advice in accordance with individual needs based on the skill improved by the communication support unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to adequately recognize users' emotions and, based on that, provide effective management methods and support for improving communication skills.

[0005] The system according to the embodiment aims to recognize the user's emotions, suggest appropriate management methods based on the emotions, and improve communication skills. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion recognition unit, a management suggestion unit, a communication support unit, and an advice providing unit. The emotion recognition unit recognizes the emotion of a user. The management suggestion unit suggests an appropriate management method based on the emotion recognized by the emotion recognition unit. The communication support unit improves the user's communication skills based on the management method suggested by the management suggestion unit. The advice providing unit provides advice tailored to individual needs based on the skills improved by the communication support unit. [Effects of the Invention]

[0007] The system according to the embodiment can recognize the user's emotions, suggest appropriate management methods based on the emotions, and improve communication skills. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A system for supporting the improvement of mental health and social-emotional skills according to an embodiment of the present invention recognizes and manages a user's emotions, improves communication skills, and provides advice tailored to individual needs, thereby removing barriers to improving a user's mental health and personal success and promoting inclusive growth in educational and work environments.

[0029] A system for supporting the improvement of mental health and social-emotional skills according to an embodiment includes an emotion recognition unit, a management suggestion unit, a communication support unit, and an advice provision unit. The emotion recognition unit recognizes a user's emotion. For example, the emotion recognition unit captures the user's facial expression with a camera and analyzes the emotion using an emotion estimation algorithm. The emotion recognition unit can also record the user's voice and estimate the emotion using voice analysis technology. The emotion recognition unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and analyze the emotion using an emotion estimation algorithm. The management suggestion unit suggests an appropriate management method based on the emotion recognized by the emotion recognition unit. For example, if the user is feeling stressed, the management suggestion unit can suggest a relaxation method or a stress relief method. If the user is feeling anxious, the management suggestion unit can also suggest a counseling method or a self-care method. If the user is feeling angry, the management suggestion unit can also suggest an anger control method. The communication support unit improves the user's communication skills based on the management method suggested by the management suggestion unit. For example, if a user is experiencing difficulties in interpersonal relationships, the communication support unit can suggest appropriate communication methods and conversation tips. Furthermore, if a user wants to improve their presentation skills, the communication support unit can suggest effective presentation methods. Furthermore, if a user wants to improve their leadership skills, the communication support unit can suggest leadership tips. The advice providing unit provides advice tailored to individual needs based on the skills improved by the communication support unit. For example, if a user is facing a specific challenge, the advice providing unit can analyze the challenge and suggest specific solutions and support methods. Furthermore, if a user feels there are barriers to academic or workplace success, the advice providing unit can analyze the barriers and suggest specific measures and advice for growth. Furthermore, if a user is aiming for personal growth, the advice providing unit can suggest a specific plan for growth.As a result, the system for supporting the improvement of mental health and social-emotional skills according to the embodiment can remove barriers to improving users' mental health and personal success, and promote inclusive growth in educational and work environments.

[0030] The emotion recognition unit can refer to the user's past emotional history and propose a management method that takes into account emotional transitions. For example, the emotion recognition unit uses a generation AI to analyze the user's past emotional history and propose a management method that takes into account emotional transitions. For example, based on past emotional data, the unit can identify times and situations in which the user is likely to feel stressed and propose preventive measures. The emotion recognition unit can also analyze emotional transitions based on the user's past emotional history and propose appropriate management methods. For example, the unit can analyze the emotions the user has felt in the past and propose relaxation methods or stress relief methods that take into account those transitions. The emotion recognition unit can also analyze emotional transitions based on the user's past emotional history and propose appropriate counseling methods or self-care methods. This makes it possible to propose more appropriate management methods by taking into account the user's past emotional history.

[0031] The emotion recognition unit acquires the user's physical vital data in real time, allowing for more accurate capture of emotional changes. For example, the generative AI acquires the user's heart rate and electrodermal activity in real time to more accurately capture emotional changes. For example, it determines whether the user is feeling stressed based on an increase in heart rate or changes in electrodermal activity. The emotion recognition unit can also analyze emotional changes based on the user's vital data and suggest appropriate management methods. For example, it can analyze fluctuations in the user's heart rate and electrodermal activity to suggest relaxation and stress relief methods. The emotion recognition unit can also analyze emotional changes based on the user's vital data and suggest appropriate counseling and self-care methods. This allows for more accurate capture of emotional changes by acquiring the user's physical vital data in real time.

[0032] The emotion recognition unit can analyze the content of a user's social media posts and grasp emotional trends. For example, the emotion recognition unit uses a generative AI to analyze the content of a user's social media posts and grasp emotional trends. For example, it analyzes keywords and phrases in the post content to identify the user's recent emotions. The emotion recognition unit can also analyze emotional trends based on the content of a user's social media posts and suggest appropriate management methods. For example, if the user is feeling stressed, it can suggest relaxation methods and stress relief methods based on that information. The emotion recognition unit can also analyze emotional trends based on the content of a user's social media posts and suggest appropriate counseling methods and self-care methods. In this way, emotional trends can be grasped by analyzing the content of a user's social media posts.

[0033] The emotion recognition unit can share the results of the emotion recognition with the user's friends and family, thereby building an emotional support network. The emotion recognition unit, for example, shares the results of the emotion recognition with the user's friends and family, thereby building an emotional support network. For example, if the user is feeling stressed, the emotion recognition unit notifies the user's friends and family of this information and requests their support. The emotion recognition unit can also collaborate with the user's friends and family based on the results of the emotion recognition, thereby building an emotional support network. For example, if the user is feeling anxious, the emotion recognition unit can share this information with the user's friends and family and request their support. The emotion recognition unit can also collaborate with the user's friends and family based on the results of the emotion recognition, thereby building a system for building an emotional support network. For example, a system can be built in which the user requests support from friends and family depending on changes in their emotions. In this way, an emotional support network can be built by sharing the results of the emotion recognition.

[0034] The communication support unit can analyze the user's past dialogue history and suggest specific areas for improvement. For example, the communication support unit uses a generation AI to analyze the user's past dialogue history and suggest specific areas for improvement. For example, it identifies failures and successes in past dialogues and suggests improvement measures. The communication support unit can also analyze specific areas for improvement based on the user's past dialogue history and suggest appropriate communication methods. For example, it can analyze what types of dialogues the user has had in the past and suggest areas for improvement. The communication support unit can also analyze specific areas for improvement based on the user's past dialogue history and suggest tips for appropriate dialogue. In this way, it is possible to suggest specific areas for improvement by analyzing the user's past dialogue history.

[0035] The communication support unit can analyze a user's non-verbal communication and provide comprehensive advice. For example, the communication support unit uses a generation AI to analyze a user's non-verbal communication (gestures, facial expressions) and provide comprehensive advice. For example, it analyzes how gestures and facial expressions are used during a conversation and suggests improvements. The communication support unit can also provide comprehensive advice based on a user's non-verbal communication. For example, it can analyze what gestures and facial expressions a user uses during a conversation and suggest areas for improvement. The communication support unit can also build a system that provides comprehensive advice based on a user's non-verbal communication. For example, it can build a system that analyzes what gestures and facial expressions a user uses during a conversation and suggests areas for improvement. In this way, comprehensive advice can be provided by analyzing a user's non-verbal communication.

[0036] The communication support unit can learn intercultural communication skills and provide advice from a global perspective. For example, the communication support unit uses a generative AI to learn intercultural communication skills and provide advice from a global perspective. For example, it suggests ways to communicate with people of different cultural backgrounds. The communication support unit can also provide advice from a global perspective based on intercultural communication skills. For example, if a user is having difficulty in intercultural dialogue, it can suggest ways to communicate. The communication support unit can also build a system that provides advice from a global perspective based on intercultural communication skills. For example, if a user is having difficulty in intercultural dialogue, it can build a system that suggests ways to communicate. In this way, by learning intercultural communication skills, it can provide advice from a global perspective.

[0037] The communication support unit can provide a simulation environment using virtual reality (VR) and conduct practical training. For example, the communication support unit improves a user's communication skills by using a generation AI to provide a simulation environment using virtual reality (VR). For example, a dialogue scenario is reproduced in VR and practical training is conducted. The communication support unit can also conduct practical training based on a simulation environment using virtual reality (VR). For example, a user experiences a dialogue scenario in VR and proposes improvements thereto. The communication support unit can also build a system for conducting practical training based on a simulation environment using virtual reality (VR). For example, a system is built in which a user experiences a dialogue scenario in VR and proposes improvements thereto. In this way, practical training can be conducted by providing a simulation environment using virtual reality (VR).

[0038] The advice providing unit can analyze the user's lifestyle and daily routine and suggest specific improvement measures. For example, the advice providing unit uses a generation AI to analyze the user's lifestyle and daily routine and suggest specific improvement measures. For example, the advice providing unit analyzes the user's diet and exercise habits and suggests healthy lifestyle habits. The advice providing unit can also suggest specific improvement measures based on the user's lifestyle and daily routine. For example, it can suggest specific methods for the user to improve their daily routine. The advice providing unit can also build a system that suggests specific improvement measures based on the user's lifestyle and daily routine. For example, it can build a system that suggests specific methods for the user to improve their daily routine. In this way, it is possible to suggest specific improvement measures by analyzing the user's lifestyle and daily routine.

[0039] The advice providing unit can provide optimal advice by referring to the user's past experiences of success and failure. The advice providing unit, for example, uses a generation AI to refer to the user's past experiences of success and failure and provide optimal advice. For example, based on past experiences of success, it may suggest ways to succeed in similar situations. The advice providing unit can also provide optimal advice based on the user's past experiences of success and failure. For example, based on past experiences of failure, it may suggest ways to avoid failure in similar situations. The advice providing unit can also build a system that provides optimal advice based on the user's past experiences of success and failure. For example, a system can be built that provides optimal advice based on the user's past experiences of success and failure. In this way, optimal advice can be provided by referring to the user's past experiences of success and failure.

[0040] The advice providing unit can provide personalized advice by taking into account the user's hobbies and interests. For example, the generation AI of the advice providing unit provides personalized advice by taking into account the user's hobbies and interests. For example, if the user is interested in music, it will suggest a relaxation method that uses music. The advice providing unit can also provide personalized advice based on the user's hobbies and interests. For example, if the user is interested in sports, it will suggest a stress relief method that uses sports. The advice providing unit can also build a system that provides personalized advice based on the user's hobbies and interests. For example, a system can be built that provides appropriate advice according to the user's hobbies and interests. This makes it possible to provide personalized advice by taking into account the user's hobbies and interests.

[0041] The advice providing unit can refer to the user's geographical location information and suggest resources and services specific to the region. For example, the generation AI refers to the user's geographical location information and suggests resources and services specific to the region. For example, it can introduce counseling services in the region where the user lives. The advice providing unit can also suggest resources and services specific to the region based on the user's geographical location information. For example, it can introduce medical services in the region where the user lives. The advice providing unit can also build a system that suggests resources and services specific to the region based on the user's geographical location information. For example, it can build a system that provides event information in the region where the user lives. In this way, it is possible to suggest resources and services specific to the region by referring to the user's geographical location information.

[0042] The advice providing unit can analyze the user's sleep patterns and dietary habits and suggest comprehensive health management. For example, the advice providing unit uses a generation AI to analyze the user's sleep patterns and suggest comprehensive health management. For example, the advice providing unit provides advice for better sleep based on the user's sleep data. The advice providing unit can also analyze the user's dietary habits and suggest comprehensive health management. For example, the advice providing unit can evaluate nutritional balance based on the user's food records and suggest a healthy diet. The advice providing unit can also build a system that suggests comprehensive health management based on the user's sleep patterns and dietary habits. For example, a system can be built that provides specific advice for health management based on the user's sleep data and dietary records. In this way, comprehensive health management can be suggested by analyzing the user's sleep patterns and dietary habits.

[0043] The advice providing unit can monitor the user's stress level in real time and suggest appropriate relaxation methods. For example, the generation AI of the advice providing unit monitors the user's stress level in real time and suggests appropriate relaxation methods. For example, it suggests deep breathing or meditation when the user feels stressed. The advice providing unit can also monitor the user's stress level in real time and suggest appropriate relaxation methods. For example, it suggests relaxation music when the user feels stressed. The advice providing unit can also build a system that monitors the user's stress level in real time and suggests appropriate relaxation methods. For example, it builds a system that suggests appropriate relaxation methods when the user feels stressed. In this way, the user's stress level can be monitored in real time and appropriate relaxation methods can be suggested.

[0044] The advice providing unit can propose personalized relaxation methods by taking into account the user's hobbies and interests. For example, the generation AI of the advice providing unit proposes personalized relaxation methods by taking into account the user's hobbies and interests. For example, if the user is interested in music, it proposes relaxation methods that utilize music. The advice providing unit can also propose personalized relaxation methods based on the user's hobbies and interests. For example, if the user is interested in sports, it proposes stress relief methods that utilize sports. The advice providing unit can also build a system that proposes personalized relaxation methods based on the user's hobbies and interests. For example, a system is built that proposes appropriate relaxation methods according to the user's hobbies and interests. In this way, personalized relaxation methods can be proposed by taking the user's hobbies and interests into consideration.

[0045] The advice providing unit can refer to the user's geographical location information and suggest resources and services specific to the region. For example, the generation AI refers to the user's geographical location information and suggests resources and services specific to the region. For example, it can introduce counseling services in the region where the user lives. The advice providing unit can also suggest resources and services specific to the region based on the user's geographical location information. For example, it can introduce medical services in the region where the user lives. The advice providing unit can also build a system that suggests resources and services specific to the region based on the user's geographical location information. For example, it can build a system that provides event information in the region where the user lives. In this way, it is possible to suggest resources and services specific to the region by referring to the user's geographical location information.

[0046] The advice providing unit can analyze the user's learning style and work history and propose an optimal growth plan. For example, the advice providing unit uses a generation AI to analyze the user's learning style and propose an optimal growth plan. For example, if the user is a visual learner, the advice providing unit proposes a learning plan that utilizes visual learning materials. The advice providing unit can also analyze the user's work history and propose an optimal growth plan. For example, it proposes a career path based on the user's past work experience. The advice providing unit can also build a system that proposes an optimal growth plan based on the user's learning style and work history. For example, a system that proposes a growth plan based on the user's learning style and work history is built. In this way, the optimal growth plan can be proposed by analyzing the user's learning style and work history.

[0047] The advice providing unit can monitor the user's goal setting and progress in real time and provide appropriate feedback. For example, the generation AI of the advice providing unit monitors the user's goal setting and progress in real time and provides appropriate feedback. For example, the generation AI analyzes the progress toward the goals set by the user and provides advice according to the degree of achievement. The advice providing unit can also monitor the user's goal setting and progress in real time and provide appropriate feedback. For example, the advice providing unit can monitor whether the user is making progress toward the goals and provide corrective advice as necessary. The advice providing unit can also build a system that monitors the user's goal setting and progress in real time and provides appropriate feedback. For example, a system can be built that monitors whether the user is making progress toward the goals and provides feedback according to the progress. In this way, the user's goal setting and progress can be monitored in real time and appropriate feedback can be provided.

[0048] The advice providing unit can propose a personalized growth plan by taking into account the user's hobbies and interests. For example, the generation AI of the advice providing unit proposes a personalized growth plan by taking into account the user's hobbies and interests. For example, if the user is interested in music, it proposes a study plan that utilizes music. The advice providing unit can also propose a personalized growth plan based on the user's hobbies and interests. For example, if the user is interested in sports, it proposes a growth plan that utilizes sports. The advice providing unit can also build a system that proposes a personalized growth plan based on the user's hobbies and interests. For example, a system is built that proposes an appropriate growth plan according to the user's hobbies and interests. In this way, a personalized growth plan can be proposed by taking into account the user's hobbies and interests.

[0049] The advice providing unit can refer to the user's geographical location information and suggest resources and services specific to the region. For example, the generation AI refers to the user's geographical location information and suggests resources and services specific to the region. For example, it introduces educational institutions and vocational training facilities in the area where the user lives. The advice providing unit can also suggest resources and services specific to the region based on the user's geographical location information. For example, it can introduce medical services in the area where the user lives. The advice providing unit can also build a system that suggests resources and services specific to the region based on the user's geographical location information. For example, it builds a system that provides event information in the area where the user lives. In this way, it is possible to suggest resources and services specific to the region by referring to the user's geographical location information.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The communication support unit can analyze the user's past dialogue history and suggest specific areas for improvement. For example, the generation AI can analyze the user's past dialogue history and suggest specific areas for improvement. For example, it can identify failures and successes in past dialogues and suggest improvement measures. The communication support unit can also analyze specific areas for improvement based on the user's past dialogue history and suggest appropriate communication methods. For example, it can analyze what types of dialogues the user has had in the past and suggest areas for improvement. The communication support unit can also analyze specific areas for improvement based on the user's past dialogue history and suggest tips for appropriate dialogue. In this way, specific areas for improvement can be suggested by analyzing the user's past dialogue history.

[0052] The communication support unit can learn intercultural communication skills and provide advice from a global perspective. For example, a generative AI can learn intercultural communication skills and provide advice from a global perspective. For example, it can suggest ways to communicate with people from different cultural backgrounds. The communication support unit can also provide advice from a global perspective based on intercultural communication skills. For example, if a user is having difficulty in intercultural dialogue, it can suggest ways to communicate with them. The communication support unit can also build a system that provides advice from a global perspective based on intercultural communication skills. For example, if a user is having difficulty in intercultural dialogue, it can build a system that suggests ways to communicate with them. In this way, by learning intercultural communication skills, it can provide advice from a global perspective.

[0053] The communication support unit can provide a simulation environment using virtual reality (VR) and conduct practical training. For example, a generation AI can provide a simulation environment using virtual reality (VR) to improve a user's communication skills. For example, a dialogue scenario can be reproduced in VR and practical training can be conducted. The communication support unit can also conduct practical training based on a simulation environment using virtual reality (VR). For example, a user can experience a dialogue scenario in VR and suggest improvements. The communication support unit can also build a system for conducting practical training based on a simulation environment using virtual reality (VR). For example, a system can be built in which a user can experience a dialogue scenario in VR and suggest improvements. In this way, practical training can be conducted by providing a simulation environment using virtual reality (VR).

[0054] The advice providing unit can analyze the user's lifestyle and daily routine and suggest specific improvement measures. For example, the generation AI analyzes the user's lifestyle and daily routine and suggests specific improvement measures. For example, it analyzes the user's diet and exercise habits and suggests healthy lifestyle habits. The advice providing unit can also suggest specific improvement measures based on the user's lifestyle and daily routine. For example, it can suggest specific methods for the user to improve their daily routine. The advice providing unit can also build a system that suggests specific improvement measures based on the user's lifestyle and daily routine. For example, it can build a system that suggests specific methods for the user to improve their daily routine. In this way, it is possible to suggest specific improvement measures by analyzing the user's lifestyle and daily routine.

[0055] The advice providing unit can provide optimal advice by referring to the user's past experiences of success and failure. For example, the generation AI can provide optimal advice by referring to the user's past experiences of success and failure. For example, it can suggest ways to succeed in similar situations based on past experiences of success. The advice providing unit can also provide optimal advice based on the user's past experiences of success and failure. For example, it can suggest ways to avoid failure in similar situations based on past experiences of failure. The advice providing unit can also build a system that provides optimal advice based on the user's past experiences of success and failure. For example, it can build a system that provides optimal advice based on the user's past experiences of success and failure. In this way, it is possible to provide optimal advice by referring to the user's past experiences of success and failure.

[0056] The advice providing unit can analyze the user's learning style and work history and propose an optimal growth plan. For example, the generation AI analyzes the user's learning style and proposes an optimal growth plan. For example, if the user is a visual learner, it proposes a learning plan that utilizes visual learning materials. The advice providing unit can also analyze the user's work history and propose an optimal growth plan. For example, it proposes a career path based on the user's past work experience. The advice providing unit can also build a system that proposes an optimal growth plan based on the user's learning style and work history. For example, it builds a system that proposes a growth plan based on the user's learning style and work history. In this way, it is possible to propose an optimal growth plan by analyzing the user's learning style and work history.

[0057] The advice providing unit can monitor the user's goal setting and progress in real time and provide appropriate feedback. For example, the generation AI monitors the user's goal setting and progress in real time and provides appropriate feedback. For example, it analyzes the progress toward the goals set by the user and provides advice according to the level of achievement. The advice providing unit can also monitor the user's goal setting and progress in real time and provide appropriate feedback. For example, it monitors whether the user is making progress toward the goals and provides corrective advice as necessary. The advice providing unit can also build a system that monitors the user's goal setting and progress in real time and provides appropriate feedback. For example, it builds a system that monitors whether the user is making progress toward the goals and provides feedback according to the progress. In this way, the user's goal setting and progress can be monitored in real time and appropriate feedback can be provided.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The emotion recognition unit recognizes the user's emotions. For example, the emotion recognition unit captures the user's facial expressions with a camera and analyzes the emotions using an emotion estimation algorithm. The emotion recognition unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the emotion recognition unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and analyze the emotions using an emotion estimation algorithm. Step 2: The management suggestion unit suggests an appropriate management method based on the emotion recognized by the emotion recognition unit. For example, if the user is feeling stressed, the management suggestion unit suggests relaxation methods or stress relief methods. Also, if the user is feeling anxious, the management suggestion unit can suggest counseling methods or self-care methods. Furthermore, if the user is feeling angry, the management suggestion unit can suggest anger control methods. Step 3: The communication support unit improves the user's communication skills based on the management method suggested by the management suggestion unit. For example, if the user is experiencing difficulties in interpersonal relationships, the communication support unit can suggest appropriate communication methods and conversation tips. Also, if the user wants to improve their presentation skills, the communication support unit can suggest effective presentation methods. Furthermore, if the user wants to improve their leadership skills, the communication support unit can suggest leadership tips. Step 4: The advice provider provides advice tailored to individual needs based on the skills improved by the communication support unit. For example, if the user is facing a specific challenge, the advice provider analyzes the challenge and proposes specific solutions and support methods. If the user feels there are barriers to success in school or at work, the advice provider can analyze those barriers and propose specific measures and advice for growth. Furthermore, if the user is aiming for personal growth, the advice provider can propose a specific plan for growth.

[0060] (Example 2) A system for supporting the improvement of mental health and social-emotional skills according to an embodiment of the present invention recognizes and manages a user's emotions, improves communication skills, and provides advice tailored to individual needs, thereby removing barriers to improving a user's mental health and personal success and promoting inclusive growth in educational and work environments.

[0061] A system for supporting the improvement of mental health and social-emotional skills according to an embodiment includes an emotion recognition unit, a management suggestion unit, a communication support unit, and an advice provision unit. The emotion recognition unit recognizes a user's emotion. For example, the emotion recognition unit captures the user's facial expression with a camera and analyzes the emotion using an emotion estimation algorithm. The emotion recognition unit can also record the user's voice and estimate the emotion using voice analysis technology. The emotion recognition unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and analyze the emotion using an emotion estimation algorithm. The management suggestion unit suggests an appropriate management method based on the emotion recognized by the emotion recognition unit. For example, if the user is feeling stressed, the management suggestion unit can suggest a relaxation method or a stress relief method. If the user is feeling anxious, the management suggestion unit can also suggest a counseling method or a self-care method. If the user is feeling angry, the management suggestion unit can also suggest an anger control method. The communication support unit improves the user's communication skills based on the management method suggested by the management suggestion unit. For example, if a user is experiencing difficulties in interpersonal relationships, the communication support unit can suggest appropriate communication methods and conversation tips. Furthermore, if a user wants to improve their presentation skills, the communication support unit can suggest effective presentation methods. Furthermore, if a user wants to improve their leadership skills, the communication support unit can suggest leadership tips. The advice providing unit provides advice tailored to individual needs based on the skills improved by the communication support unit. For example, if a user is facing a specific challenge, the advice providing unit can analyze the challenge and suggest specific solutions and support methods. Furthermore, if a user feels there are barriers to academic or workplace success, the advice providing unit can analyze the barriers and suggest specific measures and advice for growth. Furthermore, if a user is aiming for personal growth, the advice providing unit can suggest a specific plan for growth.As a result, the system for supporting the improvement of mental health and social-emotional skills according to the embodiment can remove barriers to improving users' mental health and personal success, and promote inclusive growth in educational and work environments.

[0062] The emotion recognition unit can refer to the user's past emotional history and propose a management method that takes into account emotional transitions. For example, the emotion recognition unit uses a generation AI to analyze the user's past emotional history and propose a management method that takes into account emotional transitions. For example, based on past emotional data, the unit can identify times and situations in which the user is likely to feel stressed and propose preventive measures. The emotion recognition unit can also analyze emotional transitions based on the user's past emotional history and propose appropriate management methods. For example, the unit can analyze the emotions the user has felt in the past and propose relaxation methods or stress relief methods that take into account those transitions. The emotion recognition unit can also analyze emotional transitions based on the user's past emotional history and propose appropriate counseling methods or self-care methods. This makes it possible to propose more appropriate management methods by taking into account the user's past emotional history.

[0063] The emotion recognition unit acquires the user's physical vital data in real time, allowing for more accurate capture of emotional changes. For example, the generative AI acquires the user's heart rate and electrodermal activity in real time to more accurately capture emotional changes. For example, it determines whether the user is feeling stressed based on an increase in heart rate or changes in electrodermal activity. The emotion recognition unit can also analyze emotional changes based on the user's vital data and suggest appropriate management methods. For example, it can analyze fluctuations in the user's heart rate and electrodermal activity to suggest relaxation and stress relief methods. The emotion recognition unit can also analyze emotional changes based on the user's vital data and suggest appropriate counseling and self-care methods. This allows for more accurate capture of emotional changes by acquiring the user's physical vital data in real time.

[0064] The emotion recognition unit can use the emotion estimation function to estimate the user's emotion in real time and automatically provide music or video content corresponding to the emotion. The emotion recognition unit, for example, uses the emotion estimation function to estimate the user's emotion in real time and automatically provide music corresponding to the emotion. For example, if the user feels like relaxing, music with a relaxing effect is played. The emotion recognition unit can also use the emotion estimation function to estimate the user's emotion in real time and automatically provide video content corresponding to the emotion. For example, if the user feels like cheering up, an encouraging video is played. The emotion recognition unit can also use the emotion estimation function to build a system that estimates the user's emotion in real time and automatically provides music or video content corresponding to the emotion. For example, a system can be built that provides appropriate music or video content according to changes in the user's emotion. This makes it possible to support emotion management by automatically providing music or video content corresponding to the user's emotion.

[0065] The emotion recognition unit can analyze the content of a user's social media posts and grasp emotional trends. For example, the emotion recognition unit uses a generative AI to analyze the content of a user's social media posts and grasp emotional trends. For example, it analyzes keywords and phrases in the post content to identify the user's recent emotions. The emotion recognition unit can also analyze emotional trends based on the content of a user's social media posts and suggest appropriate management methods. For example, if the user is feeling stressed, it can suggest relaxation methods and stress relief methods based on that information. The emotion recognition unit can also analyze emotional trends based on the content of a user's social media posts and suggest appropriate counseling methods and self-care methods. In this way, emotional trends can be grasped by analyzing the content of a user's social media posts.

[0066] The emotion recognition unit can share the results of the emotion recognition with the user's friends and family, thereby building an emotional support network. The emotion recognition unit, for example, shares the results of the emotion recognition with the user's friends and family, thereby building an emotional support network. For example, if the user is feeling stressed, the emotion recognition unit notifies the user's friends and family of this information and requests their support. The emotion recognition unit can also collaborate with the user's friends and family based on the results of the emotion recognition, thereby building an emotional support network. For example, if the user is feeling anxious, the emotion recognition unit can share this information with the user's friends and family and request their support. The emotion recognition unit can also collaborate with the user's friends and family based on the results of the emotion recognition, thereby building a system for building an emotional support network. For example, a system can be built in which the user requests support from friends and family depending on changes in their emotions. In this way, an emotional support network can be built by sharing the results of the emotion recognition.

[0067] The emotion recognition unit can use the emotion estimation function to analyze facial expressions and tone of voice when a user inputs an emotion, thereby improving the accuracy of emotion recognition. The emotion recognition unit, for example, uses the emotion estimation function to analyze facial expressions when a user inputs an emotion, thereby improving the accuracy of emotion recognition. For example, a camera is used to analyze the user's facial expressions in real time to accurately recognize emotions. The emotion recognition unit can also use the emotion estimation function to analyze the tone of voice when a user inputs an emotion, thereby improving the accuracy of emotion recognition. For example, a microphone is used to analyze the user's tone of voice in real time to accurately recognize emotions. The emotion recognition unit can also use the emotion estimation function to analyze facial expressions and tone of voice when a user inputs an emotion, thereby building a system that improves the accuracy of emotion recognition. For example, a system is built that analyzes facial expressions and tone of voice when a user inputs an emotion and accurately recognizes emotions. In this way, the accuracy of emotion recognition can be improved by analyzing the user's facial expressions and tone of voice.

[0068] The communication support unit can analyze the user's past dialogue history and suggest specific areas for improvement. For example, the communication support unit uses a generation AI to analyze the user's past dialogue history and suggest specific areas for improvement. For example, it identifies failures and successes in past dialogues and suggests improvement measures. The communication support unit can also analyze specific areas for improvement based on the user's past dialogue history and suggest appropriate communication methods. For example, it can analyze what types of dialogues the user has had in the past and suggest areas for improvement. The communication support unit can also analyze specific areas for improvement based on the user's past dialogue history and suggest tips for appropriate dialogue. In this way, it is possible to suggest specific areas for improvement by analyzing the user's past dialogue history.

[0069] The communication support unit can analyze a user's non-verbal communication and provide comprehensive advice. For example, the communication support unit uses a generation AI to analyze a user's non-verbal communication (gestures, facial expressions) and provide comprehensive advice. For example, it analyzes how gestures and facial expressions are used during a conversation and suggests improvements. The communication support unit can also provide comprehensive advice based on a user's non-verbal communication. For example, it can analyze what gestures and facial expressions a user uses during a conversation and suggest areas for improvement. The communication support unit can also build a system that provides comprehensive advice based on a user's non-verbal communication. For example, it can build a system that analyzes what gestures and facial expressions a user uses during a conversation and suggests areas for improvement. In this way, comprehensive advice can be provided by analyzing a user's non-verbal communication.

[0070] The communication support unit can use the emotion estimation function to suggest a communication method according to the user's emotional state and promote emotional empathy. The communication support unit, for example, uses the emotion estimation function to suggest a communication method according to the user's emotional state. For example, if the user is feeling anxious, a communication method that gives a sense of security is suggested. The communication support unit can also use the emotion estimation function to suggest a communication method according to the user's emotional state and promote emotional empathy. For example, if the user is feeling sad, a communication method that shows empathy is suggested. The communication support unit can also use the emotion estimation function to suggest a communication method according to the user's emotional state and build a system that promotes emotional empathy. For example, a system is built that suggests an appropriate communication method according to changes in the user's emotions. In this way, emotional empathy can be promoted by suggesting a communication method according to the user's emotional state.

[0071] The communication support unit can learn intercultural communication skills and provide advice from a global perspective. For example, the communication support unit uses a generative AI to learn intercultural communication skills and provide advice from a global perspective. For example, it suggests ways to communicate with people of different cultural backgrounds. The communication support unit can also provide advice from a global perspective based on intercultural communication skills. For example, if a user is having difficulty in intercultural dialogue, it can suggest ways to communicate. The communication support unit can also build a system that provides advice from a global perspective based on intercultural communication skills. For example, if a user is having difficulty in intercultural dialogue, it can build a system that suggests ways to communicate. In this way, by learning intercultural communication skills, it can provide advice from a global perspective.

[0072] The communication support unit can provide a simulation environment using virtual reality (VR) and conduct practical training. For example, the communication support unit improves a user's communication skills by using a generation AI to provide a simulation environment using virtual reality (VR). For example, a dialogue scenario is reproduced in VR and practical training is conducted. The communication support unit can also conduct practical training based on a simulation environment using virtual reality (VR). For example, a user experiences a dialogue scenario in VR and proposes improvements thereto. The communication support unit can also build a system for conducting practical training based on a simulation environment using virtual reality (VR). For example, a system is built in which a user experiences a dialogue scenario in VR and proposes improvements thereto. In this way, practical training can be conducted by providing a simulation environment using virtual reality (VR).

[0073] The communication support unit can use the emotion estimation function to monitor the stress and anxiety felt by the user during a conversation in real time and propose appropriate countermeasures. The communication support unit can, for example, use the emotion estimation function to monitor the stress and anxiety felt by the user during a conversation in real time and propose appropriate countermeasures. For example, the communication support unit can propose a relaxation method when the user feels stressed. The communication support unit can also use the emotion estimation function to monitor the stress and anxiety felt by the user during a conversation in real time and propose appropriate countermeasures. For example, the communication support unit can propose a counseling method when the user feels anxious. The communication support unit can also use the emotion estimation function to build a system that monitors the stress and anxiety felt by the user during a conversation in real time and proposes appropriate countermeasures. For example, a system can be built that monitors the stress and anxiety felt by the user during a conversation and proposes appropriate countermeasures. In this way, the stress and anxiety felt by the user during a conversation can be monitored in real time and proposed appropriate countermeasures, thereby reducing stress and anxiety during a conversation.

[0074] The advice providing unit can analyze the user's lifestyle and daily routine and suggest specific improvement measures. For example, the advice providing unit uses a generation AI to analyze the user's lifestyle and daily routine and suggest specific improvement measures. For example, the advice providing unit analyzes the user's diet and exercise habits and suggests healthy lifestyle habits. The advice providing unit can also suggest specific improvement measures based on the user's lifestyle and daily routine. For example, it can suggest specific methods for the user to improve their daily routine. The advice providing unit can also build a system that suggests specific improvement measures based on the user's lifestyle and daily routine. For example, it can build a system that suggests specific methods for the user to improve their daily routine. In this way, it is possible to suggest specific improvement measures by analyzing the user's lifestyle and daily routine.

[0075] The advice providing unit can provide optimal advice by referring to the user's past experiences of success and failure. The advice providing unit, for example, uses a generation AI to refer to the user's past experiences of success and failure and provide optimal advice. For example, based on past experiences of success, it may suggest ways to succeed in similar situations. The advice providing unit can also provide optimal advice based on the user's past experiences of success and failure. For example, based on past experiences of failure, it may suggest ways to avoid failure in similar situations. The advice providing unit can also build a system that provides optimal advice based on the user's past experiences of success and failure. For example, a system can be built that provides optimal advice based on the user's past experiences of success and failure. In this way, optimal advice can be provided by referring to the user's past experiences of success and failure.

[0076] The advice providing unit can use the emotion estimation function to provide advice according to the user's emotional state and enhance emotional support. The advice providing unit, for example, uses the emotion estimation function to provide advice according to the user's emotional state. For example, if the user is feeling stressed, the advice providing unit can suggest a relaxation method. The advice providing unit can also use the emotion estimation function to provide advice according to the user's emotional state and enhance emotional support. For example, if the user is feeling anxious, the advice providing unit can provide advice that gives a sense of security. The advice providing unit can also use the emotion estimation function to build a system that provides advice according to the user's emotional state and enhances emotional support. For example, a system is built that provides appropriate advice according to changes in the user's emotions. In this way, emotional support can be enhanced by providing advice according to the user's emotional state.

[0077] The advice providing unit can provide personalized advice by taking into account the user's hobbies and interests. For example, the generation AI of the advice providing unit provides personalized advice by taking into account the user's hobbies and interests. For example, if the user is interested in music, it will suggest a relaxation method that uses music. The advice providing unit can also provide personalized advice based on the user's hobbies and interests. For example, if the user is interested in sports, it will suggest a stress relief method that uses sports. The advice providing unit can also build a system that provides personalized advice based on the user's hobbies and interests. For example, a system can be built that provides appropriate advice according to the user's hobbies and interests. This makes it possible to provide personalized advice by taking into account the user's hobbies and interests.

[0078] The advice providing unit can refer to the user's geographical location information and suggest resources and services specific to the region. For example, the generation AI refers to the user's geographical location information and suggests resources and services specific to the region. For example, it can introduce counseling services in the region where the user lives. The advice providing unit can also suggest resources and services specific to the region based on the user's geographical location information. For example, it can introduce medical services in the region where the user lives. The advice providing unit can also build a system that suggests resources and services specific to the region based on the user's geographical location information. For example, it can build a system that provides event information in the region where the user lives. In this way, it is possible to suggest resources and services specific to the region by referring to the user's geographical location information.

[0079] The advice providing unit can use the emotion estimation function to monitor the emotional reaction of the user when accepting advice and evaluate the effectiveness of the advice in real time. The advice providing unit, for example, uses the emotion estimation function to monitor the emotional reaction of the user when accepting advice and evaluate the effectiveness of the advice in real time. For example, the advice providing unit monitors emotional changes when the user tries a relaxation method. The advice providing unit can also use the emotion estimation function to monitor the emotional reaction of the user when accepting advice and evaluate the effectiveness of the advice in real time. For example, the advice providing unit monitors emotional changes when the user tries a stress relief method. The advice providing unit can also use the emotion estimation function to monitor the emotional reaction of the user when accepting advice and build a system that evaluates the effectiveness of the advice in real time. For example, a system is built that monitors the emotional reaction of the user when accepting advice and evaluates the effectiveness. In this way, the effectiveness of the advice can be evaluated in real time by monitoring the emotional reaction of the user when accepting advice.

[0080] The advice providing unit can analyze the user's sleep patterns and dietary habits and suggest comprehensive health management. For example, the advice providing unit uses a generation AI to analyze the user's sleep patterns and suggest comprehensive health management. For example, the advice providing unit provides advice for better sleep based on the user's sleep data. The advice providing unit can also analyze the user's dietary habits and suggest comprehensive health management. For example, the advice providing unit can evaluate nutritional balance based on the user's food records and suggest a healthy diet. The advice providing unit can also build a system that suggests comprehensive health management based on the user's sleep patterns and dietary habits. For example, a system can be built that provides specific advice for health management based on the user's sleep data and dietary records. In this way, comprehensive health management can be suggested by analyzing the user's sleep patterns and dietary habits.

[0081] The advice providing unit can monitor the user's stress level in real time and suggest appropriate relaxation methods. For example, the generation AI of the advice providing unit monitors the user's stress level in real time and suggests appropriate relaxation methods. For example, it suggests deep breathing or meditation when the user feels stressed. The advice providing unit can also monitor the user's stress level in real time and suggest appropriate relaxation methods. For example, it suggests relaxation music when the user feels stressed. The advice providing unit can also build a system that monitors the user's stress level in real time and suggests appropriate relaxation methods. For example, it builds a system that suggests appropriate relaxation methods when the user feels stressed. In this way, the user's stress level can be monitored in real time and appropriate relaxation methods can be suggested.

[0082] The advice providing unit can use the emotion estimation function to provide a mental health care method according to the user's emotional state, thereby enhancing emotional support. The advice providing unit, for example, uses the emotion estimation function to provide a mental health care method according to the user's emotional state. For example, if the user is feeling anxious, the advice providing unit can suggest a care method that gives the user a sense of security. The advice providing unit can also use the emotion estimation function to provide a mental health care method according to the user's emotional state, thereby enhancing emotional support. For example, if the user is feeling stressed, the advice providing unit can suggest a relaxation method. The advice providing unit can also use the emotion estimation function to provide a mental health care method according to the user's emotional state, thereby building a system that enhances emotional support. For example, a system can be built that provides an appropriate mental health care method according to changes in the user's emotions. In this way, emotional support can be enhanced by providing a mental health care method according to the user's emotional state.

[0083] The advice providing unit can propose personalized relaxation methods by taking into account the user's hobbies and interests. For example, the generation AI of the advice providing unit proposes personalized relaxation methods by taking into account the user's hobbies and interests. For example, if the user is interested in music, it proposes relaxation methods that utilize music. The advice providing unit can also propose personalized relaxation methods based on the user's hobbies and interests. For example, if the user is interested in sports, it proposes stress relief methods that utilize sports. The advice providing unit can also build a system that proposes personalized relaxation methods based on the user's hobbies and interests. For example, a system is built that proposes appropriate relaxation methods according to the user's hobbies and interests. In this way, personalized relaxation methods can be proposed by taking the user's hobbies and interests into consideration.

[0084] The advice providing unit can refer to the user's geographical location information and suggest resources and services specific to the region. For example, the generation AI refers to the user's geographical location information and suggests resources and services specific to the region. For example, it can introduce counseling services in the region where the user lives. The advice providing unit can also suggest resources and services specific to the region based on the user's geographical location information. For example, it can introduce medical services in the region where the user lives. The advice providing unit can also build a system that suggests resources and services specific to the region based on the user's geographical location information. For example, it can build a system that provides event information in the region where the user lives. In this way, it is possible to suggest resources and services specific to the region by referring to the user's geographical location information.

[0085] The advice providing unit can use the emotion estimation function to monitor the emotional reactions of the user when accepting mental health care and evaluate the effectiveness of the care in real time. The advice providing unit, for example, uses the emotion estimation function to monitor the emotional reactions of the user when accepting mental health care and evaluate the effectiveness of the care in real time. For example, the advice providing unit monitors emotional changes when the user tries a relaxation method. The advice providing unit can also use the emotion estimation function to monitor the emotional reactions of the user when accepting mental health care and evaluate the effectiveness of the care in real time. For example, the advice providing unit monitors emotional changes when the user tries a stress relief method. The advice providing unit can also use the emotion estimation function to build a system that monitors the emotional reactions of the user when accepting mental health care and evaluates the effectiveness of the care in real time. For example, a system is built that monitors the emotional reactions of the user when accepting mental health care and evaluates the effectiveness. In this way, the effectiveness of the care can be evaluated in real time by monitoring the emotional reactions of the user when accepting mental health care.

[0086] The advice providing unit can analyze the user's learning style and work history and propose an optimal growth plan. For example, the advice providing unit uses a generation AI to analyze the user's learning style and propose an optimal growth plan. For example, if the user is a visual learner, the advice providing unit proposes a learning plan that utilizes visual learning materials. The advice providing unit can also analyze the user's work history and propose an optimal growth plan. For example, it proposes a career path based on the user's past work experience. The advice providing unit can also build a system that proposes an optimal growth plan based on the user's learning style and work history. For example, a system that proposes a growth plan based on the user's learning style and work history is built. In this way, the optimal growth plan can be proposed by analyzing the user's learning style and work history.

[0087] The advice providing unit can monitor the user's goal setting and progress in real time and provide appropriate feedback. For example, the generation AI of the advice providing unit monitors the user's goal setting and progress in real time and provides appropriate feedback. For example, the generation AI analyzes the progress toward the goals set by the user and provides advice according to the degree of achievement. The advice providing unit can also monitor the user's goal setting and progress in real time and provide appropriate feedback. For example, the advice providing unit can monitor whether the user is making progress toward the goals and provide corrective advice as necessary. The advice providing unit can also build a system that monitors the user's goal setting and progress in real time and provides appropriate feedback. For example, a system can be built that monitors whether the user is making progress toward the goals and provides feedback according to the progress. In this way, the user's goal setting and progress can be monitored in real time and appropriate feedback can be provided.

[0088] The advice providing unit can use the emotion estimation function to provide a growth plan according to the user's emotional state and strengthen emotional support. The advice providing unit, for example, uses the emotion estimation function to provide a growth plan according to the user's emotional state. For example, if the user is feeling stressed, the advice providing unit can propose a growth plan aimed at reducing stress. The advice providing unit can also use the emotion estimation function to provide a growth plan according to the user's emotional state and strengthen emotional support. For example, if the user is feeling anxious, the advice providing unit can propose a growth plan that gives the user a sense of security. The advice providing unit can also use the emotion estimation function to build a system that provides a growth plan according to the user's emotional state and strengthens emotional support. For example, a system is built that provides an appropriate growth plan according to changes in the user's emotions. In this way, emotional support can be strengthened by providing a growth plan according to the user's emotional state.

[0089] The advice providing unit can propose a personalized growth plan by taking into account the user's hobbies and interests. For example, the generation AI of the advice providing unit proposes a personalized growth plan by taking into account the user's hobbies and interests. For example, if the user is interested in music, it proposes a study plan that utilizes music. The advice providing unit can also propose a personalized growth plan based on the user's hobbies and interests. For example, if the user is interested in sports, it proposes a growth plan that utilizes sports. The advice providing unit can also build a system that proposes a personalized growth plan based on the user's hobbies and interests. For example, a system is built that proposes an appropriate growth plan according to the user's hobbies and interests. In this way, a personalized growth plan can be proposed by taking into account the user's hobbies and interests.

[0090] The advice providing unit can refer to the user's geographical location information and suggest resources and services specific to the region. For example, the generation AI refers to the user's geographical location information and suggests resources and services specific to the region. For example, it introduces educational institutions and vocational training facilities in the area where the user lives. The advice providing unit can also suggest resources and services specific to the region based on the user's geographical location information. For example, it can introduce medical services in the area where the user lives. The advice providing unit can also build a system that suggests resources and services specific to the region based on the user's geographical location information. For example, it builds a system that provides event information in the area where the user lives. In this way, it is possible to suggest resources and services specific to the region by referring to the user's geographical location information.

[0091] The advice providing unit can use the emotion estimation function to monitor the emotional reaction of the user when accepting the growth plan and evaluate the effectiveness of the plan in real time. The advice providing unit, for example, uses the emotion estimation function to monitor the emotional reaction of the user when accepting the growth plan and evaluate the effectiveness of the plan in real time. For example, the advice providing unit monitors emotional changes when the user tries a new learning method. The advice providing unit can also use the emotion estimation function to monitor the emotional reaction of the user when accepting the growth plan and evaluate the effectiveness of the plan in real time. For example, the advice providing unit monitors emotional changes when the user tries a new career path. The advice providing unit can also use the emotion estimation function to build a system that monitors the emotional reaction of the user when accepting the growth plan and evaluates the effectiveness of the plan in real time. For example, a system is built that monitors the emotional reaction of the user when accepting the growth plan and evaluates its effectiveness. In this way, the effectiveness of the plan can be evaluated in real time by monitoring the emotional reaction of the user when accepting the growth plan.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The emotion recognition unit recognizes the user's emotions. For example, the emotion recognition unit captures the user's facial expressions with a camera and analyzes the emotions using an emotion estimation algorithm. The emotion recognition unit can also record the user's voice and estimate the emotions using voice analysis technology. The emotion recognition unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and analyze the emotions using an emotion estimation algorithm. The management suggestion unit suggests an appropriate management method based on the emotions recognized by the emotion recognition unit. For example, if the user is feeling stressed, the management suggestion unit can suggest relaxation methods or stress relief methods. If the user is feeling anxious, the management suggestion unit can also suggest counseling methods or self-care methods. If the user is feeling angry, the management suggestion unit can also suggest anger control methods. The communication support unit improves the user's communication skills based on the management method suggested by the management suggestion unit. For example, if the user is experiencing difficulties in interpersonal relationships, the communication support unit can suggest appropriate communication methods and conversation tips. If the user wants to improve their presentation skills, the communication support unit can also suggest effective presentation methods. Furthermore, if the user wants to improve their leadership skills, the communication support unit can suggest leadership tips. The advice providing unit provides advice tailored to individual needs based on the skills improved by the communication support unit. For example, if the user is facing a specific challenge, the advice providing unit can analyze the challenge and suggest specific solutions and support methods. Furthermore, if the user feels there are barriers to success in their studies or at work, the advice providing unit can analyze the barriers and suggest specific measures and advice for growth. Furthermore, if the user is aiming for personal growth, the advice providing unit can suggest a specific plan for growth.As a result, the system for supporting the improvement of mental health and social-emotional skills according to the embodiment can remove barriers to improving users' mental health and personal success, and promote inclusive growth in educational and work environments.

[0094] The emotion recognition unit can refer to the user's past emotional history and propose a management method that takes into account emotional transitions. For example, the generation AI analyzes the user's past emotional history and proposes a management method that takes into account emotional transitions. For example, based on past emotional data, it can identify times and situations in which the user is likely to feel stressed and propose preventive measures. The emotion recognition unit can also analyze the emotional transitions based on the user's past emotional history and propose appropriate management methods. For example, it can analyze the emotions the user has felt in the past and propose relaxation and stress relief methods that take those transitions into account. The emotion recognition unit can also analyze the emotional transitions based on the user's past emotional history and propose appropriate counseling and self-care methods. In this way, more appropriate management methods can be proposed by taking into account the user's past emotional history.

[0095] The emotion recognition unit acquires the user's physical vital data in real time, allowing for more accurate capture of emotional changes. For example, the generation AI acquires the user's heart rate and electrodermal activity in real time to more accurately capture emotional changes. For example, it determines whether the user is feeling stressed based on an increase in heart rate or changes in electrodermal activity. The emotion recognition unit can also analyze emotional changes based on the user's vital data and suggest appropriate management methods. For example, it can analyze fluctuations in the user's heart rate and electrodermal activity to suggest relaxation and stress relief methods. The emotion recognition unit can also analyze emotional changes based on the user's vital data and suggest appropriate counseling and self-care methods. In this way, by acquiring the user's physical vital data in real time, it is possible to more accurately capture emotional changes.

[0096] The emotion recognition unit can use the emotion estimation function to estimate a user's emotion in real time and automatically provide music or video content corresponding to that emotion. For example, the emotion estimation function can be used to estimate a user's emotion in real time and automatically provide music corresponding to that emotion. For example, if a user feels like relaxing, music with a relaxing effect can be played. The emotion recognition unit can also use the emotion estimation function to estimate a user's emotion in real time and automatically provide video content corresponding to that emotion. For example, if a user feels like cheering up, an encouraging video can be played. The emotion recognition unit can also use the emotion estimation function to build a system that estimates a user's emotion in real time and automatically provides music or video content corresponding to that emotion. For example, a system can be built that provides appropriate music or video content according to a user's changes in emotion. This makes it possible to support emotion management by automatically providing music or video content corresponding to the user's emotion.

[0097] The emotion recognition unit can analyze the content of a user's social media posts to understand emotional trends. For example, the generative AI analyzes the content of a user's social media posts to understand emotional trends. For example, it analyzes keywords and phrases in the posts to identify the user's recent emotions. The emotion recognition unit can also analyze emotional trends based on the content of a user's social media posts and suggest appropriate management methods. For example, if the user is feeling stressed, it can suggest relaxation methods and stress relief techniques based on that information. The emotion recognition unit can also analyze emotional trends based on the content of a user's social media posts and suggest appropriate counseling methods and self-care methods. In this way, emotional trends can be understood by analyzing the content of a user's social media posts.

[0098] The emotion recognition unit can share the results of the emotion recognition with the user's friends and family, thereby building an emotional support network. For example, the results of the emotion recognition can be shared with the user's friends and family to build an emotional support network. For example, if the user is feeling stressed, the user can notify their friends and family of this information and request support. The emotion recognition unit can also build an emotional support network by linking with the user's friends and family based on the results of the emotion recognition. For example, if the user is feeling anxious, the user can share this information with their friends and family and request support. The emotion recognition unit can also build a system by linking with the user's friends and family based on the results of the emotion recognition to build an emotional support network. For example, a system can be built in which the user can request support from friends and family depending on changes in their emotions. In this way, an emotional support network can be built by sharing the results of the emotion recognition.

[0099] The communication support unit can analyze the user's past dialogue history and suggest specific areas for improvement. For example, the generation AI can analyze the user's past dialogue history and suggest specific areas for improvement. For example, it can identify failures and successes in past dialogues and suggest improvement measures. The communication support unit can also analyze specific areas for improvement based on the user's past dialogue history and suggest appropriate communication methods. For example, it can analyze what types of dialogues the user has had in the past and suggest areas for improvement. The communication support unit can also analyze specific areas for improvement based on the user's past dialogue history and suggest tips for appropriate dialogue. In this way, specific areas for improvement can be suggested by analyzing the user's past dialogue history.

[0100] The communication support unit can learn intercultural communication skills and provide advice from a global perspective. For example, a generative AI can learn intercultural communication skills and provide advice from a global perspective. For example, it can suggest ways to communicate with people from different cultural backgrounds. The communication support unit can also provide advice from a global perspective based on intercultural communication skills. For example, if a user is having difficulty in intercultural dialogue, it can suggest ways to communicate with them. The communication support unit can also build a system that provides advice from a global perspective based on intercultural communication skills. For example, if a user is having difficulty in intercultural dialogue, it can build a system that suggests ways to communicate with them. In this way, by learning intercultural communication skills, it can provide advice from a global perspective.

[0101] The communication support unit can provide a simulation environment using virtual reality (VR) and conduct practical training. For example, a generation AI can provide a simulation environment using virtual reality (VR) to improve a user's communication skills. For example, a dialogue scenario can be reproduced in VR and practical training can be conducted. The communication support unit can also conduct practical training based on a simulation environment using virtual reality (VR). For example, a user can experience a dialogue scenario in VR and suggest improvements. The communication support unit can also build a system for conducting practical training based on a simulation environment using virtual reality (VR). For example, a system can be built in which a user can experience a dialogue scenario in VR and suggest improvements. In this way, practical training can be conducted by providing a simulation environment using virtual reality (VR).

[0102] The advice providing unit can analyze the user's lifestyle and daily routine and suggest specific improvement measures. For example, the generation AI analyzes the user's lifestyle and daily routine and suggests specific improvement measures. For example, it analyzes the user's diet and exercise habits and suggests healthy lifestyle habits. The advice providing unit can also suggest specific improvement measures based on the user's lifestyle and daily routine. For example, it can suggest specific methods for the user to improve their daily routine. The advice providing unit can also build a system that suggests specific improvement measures based on the user's lifestyle and daily routine. For example, it can build a system that suggests specific methods for the user to improve their daily routine. In this way, it is possible to suggest specific improvement measures by analyzing the user's lifestyle and daily routine.

[0103] The advice providing unit can provide optimal advice by referring to the user's past experiences of success and failure. For example, the generation AI can provide optimal advice by referring to the user's past experiences of success and failure. For example, it can suggest ways to succeed in similar situations based on past experiences of success. The advice providing unit can also provide optimal advice based on the user's past experiences of success and failure. For example, it can suggest ways to avoid failure in similar situations based on past experiences of failure. The advice providing unit can also build a system that provides optimal advice based on the user's past experiences of success and failure. For example, it can build a system that provides optimal advice based on the user's past experiences of success and failure. In this way, it is possible to provide optimal advice by referring to the user's past experiences of success and failure.

[0104] The advice providing unit can analyze the user's learning style and work history and propose an optimal growth plan. For example, the generation AI analyzes the user's learning style and proposes an optimal growth plan. For example, if the user is a visual learner, it proposes a learning plan that utilizes visual learning materials. The advice providing unit can also analyze the user's work history and propose an optimal growth plan. For example, it proposes a career path based on the user's past work experience. The advice providing unit can also build a system that proposes an optimal growth plan based on the user's learning style and work history. For example, it builds a system that proposes a growth plan based on the user's learning style and work history. In this way, it is possible to propose an optimal growth plan by analyzing the user's learning style and work history.

[0105] The advice providing unit can monitor the user's goal setting and progress in real time and provide appropriate feedback. For example, the generation AI monitors the user's goal setting and progress in real time and provides appropriate feedback. For example, it analyzes the progress toward the goals set by the user and provides advice according to the level of achievement. The advice providing unit can also monitor the user's goal setting and progress in real time and provide appropriate feedback. For example, it monitors whether the user is making progress toward the goals and provides corrective advice as necessary. The advice providing unit can also build a system that monitors the user's goal setting and progress in real time and provides appropriate feedback. For example, it builds a system that monitors whether the user is making progress toward the goals and provides feedback according to the progress. In this way, the user's goal setting and progress can be monitored in real time and appropriate feedback can be provided.

[0106] The advice providing unit can use the emotion estimation function to provide a growth plan according to the user's emotional state and enhance emotional support. For example, the emotion estimation function can be used to provide a growth plan according to the user's emotional state. For example, if the user is feeling stressed, a growth plan aimed at stress reduction can be proposed. The advice providing unit can also use the emotion estimation function to provide a growth plan according to the user's emotional state and enhance emotional support. For example, if the user is feeling anxious, a growth plan that provides a sense of security can be proposed. The advice providing unit can also use the emotion estimation function to build a system that provides a growth plan according to the user's emotional state and enhances emotional support. For example, a system can be built that provides an appropriate growth plan according to changes in the user's emotions. In this way, emotional support can be enhanced by providing a growth plan according to the user's emotional state.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The emotion recognition unit recognizes the user's emotions. For example, the emotion recognition unit captures the user's facial expressions with a camera and analyzes the emotions using an emotion estimation algorithm. The emotion recognition unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the emotion recognition unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and analyze the emotions using an emotion estimation algorithm. Step 2: The management suggestion unit suggests an appropriate management method based on the emotion recognized by the emotion recognition unit. For example, if the user is feeling stressed, the management suggestion unit suggests relaxation methods or stress relief methods. Also, if the user is feeling anxious, the management suggestion unit can suggest counseling methods or self-care methods. Furthermore, if the user is feeling angry, the management suggestion unit can suggest anger control methods. Step 3: The communication support unit improves the user's communication skills based on the management method suggested by the management suggestion unit. For example, if the user is experiencing difficulties in interpersonal relationships, the communication support unit can suggest appropriate communication methods and conversation tips. Also, if the user wants to improve their presentation skills, the communication support unit can suggest effective presentation methods. Furthermore, if the user wants to improve their leadership skills, the communication support unit can suggest leadership tips. Step 4: The advice provider provides advice tailored to individual needs based on the skills improved by the communication support unit. For example, if the user is facing a specific challenge, the advice provider analyzes the challenge and proposes specific solutions and support methods. If the user feels there are barriers to success in school or at work, the advice provider can analyze those barriers and propose specific measures and advice for growth. Furthermore, if the user is aiming for personal growth, the advice provider can propose a specific plan for growth.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0153] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] 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.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an emotion recognition unit that recognizes the emotion of a user; a management suggestion unit that suggests an appropriate management method based on the emotion recognized by the emotion recognition unit; a communication support unit that improves the communication skills of a user based on the management method proposed by the management proposal unit; an advice providing unit that provides advice tailored to individual needs based on the skills improved by the communication support unit. A system characterized by:

2. The emotion recognition unit Obtaining the user's physical vital data in real time to more accurately capture emotional changes 2. The system of claim 1.

3. The communication support unit Analyze the user's past interaction history and suggest specific improvements 2. The system of claim 1.

4. The advice providing unit Refer to the user's past successes and failures to provide optimal advice 2. The system of claim 1.

5. The advice providing unit Monitors the user's stress level in real time and suggests appropriate relaxation methods 2. The system of claim 1.

6. The emotion recognition unit Estimates the user's emotions in real time and automatically provides music and video content that matches those emotions.

2. The system of claim 1.

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