System

Abstract

An object of the system according to the embodiment is to provide a 24-hour counseling environment and realize comprehensive counseling tailored to each student.SOLUTION: A system includes a counseling providing part, an analysis part, and a customization part. The counseling providing unit provides a 24-hour counseling environment. The analysis unit analyzes the consultation contents of many students at the same time. The customization unit provides comprehensive counseling tailored to each student.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] With conventional technology, there was a problem in that it was difficult to adequately address children's mental health and educational issues due to a shortage of school counselors and teachers.

[0005] The system according to the embodiment aims to provide a 24-hour counseling environment and realize comprehensive counseling tailored to each student. [Means for solving the problem]

[0006] The system according to the embodiment includes a counseling provider, an analysis unit, and a customization unit. The counseling provider provides a 24-hour counseling environment. The analysis unit analyzes the consultation contents of multiple students simultaneously. The customization unit provides comprehensive counseling tailored to each student. [Effects of the Invention]

[0007] The system according to the embodiment provides a 24-hour counseling environment and can provide comprehensive counseling tailored to each student. [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 counseling system according to an embodiment of the present invention utilizes AI to solve problems in children's mental health and education. This system provides a counseling environment available 24 hours a day, 365 days a year, and can simultaneously accommodate a large number of students while providing comprehensive counseling tailored to each student. As a result, the counseling system can provide a counseling environment available 24 hours a day, 365 days a year, and can simultaneously accommodate a large number of students while providing comprehensive counseling tailored to each student.

[0029] A counseling system according to an embodiment includes a counseling unit, an analysis unit, and a customization unit. The counseling unit provides a 24 / 7 counseling environment. For example, it uses an AI chatbot to provide an environment where students can receive counseling at any time. The counseling unit can also allocate counselors on a shift basis to provide 24 / 7 support. The counseling unit also provides a video call function through an online platform, creating an environment where students can directly interact with counselors. For example, the AI ​​chatbot uses natural language processing technology to understand the students' consultation content and provide appropriate advice. The shift counselors are available 24 / 7. The online platform is equipped with a video call function, providing an environment where students can consult with counselors face-to-face. The analysis unit simultaneously analyzes the consultation content of multiple students. For example, it uses natural language processing technology to analyze the students' consultation content and perform sentiment analysis. The analysis unit can also perform clustering of consultation content to group students with common problems. Furthermore, the analysis unit evaluates the importance of the consultation content and prioritizes the most urgent issues. For example, natural language processing technology analyzes students' consultation content as text data and performs sentiment analysis. Clustering technology automatically groups students with common issues. An importance assessment algorithm evaluates the urgency of the consultation content and prioritizes it. The customization unit provides comprehensive counseling tailored to each student. For example, it analyzes individual data such as the student's past consultation history, academic performance, and home environment to create an optimal counseling plan. The customization unit can also use an emotion estimation function to monitor the student's emotional state in real time and dynamically adjust the counseling content. Furthermore, the customization unit monitors the student's progress and updates the counseling plan as needed. For example, an individual data analysis algorithm creates an optimal counseling plan based on the student's past consultation history and academic performance. The emotion estimation function analyzes the student's facial expressions and voice to grasp their emotional state in real time. The progress monitoring system tracks the student's counseling progress and updates the plan as needed.As a result, the counseling system according to the embodiment provides a 24 / 7 counseling environment, and can simultaneously accommodate a large number of students while providing comprehensive counseling tailored to each individual student. For example, the system automatically analyzes the content of each student's consultation and provides an appropriate counseling plan. The system automatically records the counseling content and provides a replay function that allows students to review it later. The system uses an emotion estimation function to monitor the student's emotional state in real time and provide appropriate feedback.

[0030] The counseling unit can suggest related resources based on the content of the student's consultation and promote self-study. For example, the counseling unit analyzes the content of the student's consultation with the AI ​​and automatically suggests related online articles and videos. For example, if a student consults about stress management, a video on stress relief methods is suggested. The counseling unit also promotes self-study by having the AI ​​suggest related resources based on the content of the student's consultation. For example, if a student consults about academic worries, the AI ​​will provide an online article on study methods. The counseling unit also analyzes the content of the student's consultation and the AI ​​will automatically suggest related resources. For example, if a student consults about worries about friendships, a video on communication skills is provided. This can promote self-study by the student.

[0031] The counseling unit can analyze the content of a student's consultation and automatically escalate it to an appropriate specialist. For example, the counseling unit analyzes the content of a student's consultation and the AI ​​automatically escalates it to an appropriate specialist. For example, if a serious mental health problem is detected, a psychological counselor will be notified. The counseling unit will also add a function that analyzes the content of a student's consultation and the AI ​​escalates it to an appropriate specialist. For example, if a physical health problem is detected, a doctor will be notified. The counseling unit will also analyze the content of a student's consultation and the AI ​​automatically escalates it to an expert. For example, if a highly urgent problem is detected, an expert will be contacted immediately. This enables a rapid response by automatically escalating to an appropriate specialist.

[0032] The counseling provision unit can introduce voice recognition technology to add a function that allows students to seek advice by voice. The counseling provision unit, for example, introduces voice recognition technology into the counseling environment to add a function that allows students to seek advice by voice. For example, students use a microphone to input the content of their consultation by voice. The counseling provision unit also uses voice recognition technology to provide a function that allows students to seek advice by voice. For example, the voice input is converted into text and analyzed by AI. The counseling provision unit also introduces voice recognition technology into the counseling environment to enable students to seek advice by voice. For example, the content of their consultation is input using voice commands. This allows students to seek advice by voice, enabling more natural communication.

[0033] The counseling provision unit can provide a replay function that automatically records counseling content and allows students to review it later. The counseling provision unit, for example, provides a replay function that automatically records counseling content and allows students to review it later. For example, the consultation content is saved as text or audio. The counseling provision unit also uses the automatic recording function to save the counseling content and allow students to review it later. For example, the consultation content is saved in the cloud so that it can be accessed at any time. The counseling provision unit also automatically records counseling content and provides a replay function. For example, the consultation content is saved in video format so that students can play it back later. This allows students to review the counseling content later.

[0034] The analysis unit can analyze the content of students' consultations, automatically group students with common problems, and provide group counseling. For example, AI can analyze the content of students' consultations and automatically group students with common problems. For example, students with the same concerns can be grouped together. The analysis unit can also analyze the content of students' consultations and AI can automatically provide group counseling. For example, students who have consultations about stress management can be grouped together and counseling can be provided. The analysis unit can also analyze the content of students' consultations and AI can group students with common problems. For example, students with academic concerns can be grouped together and counseling can be provided. This allows for more effective support by grouping students with common problems and providing group counseling.

[0035] The analysis unit can add a chatbot function to the counseling system, allowing students to seek advice in a text-based manner. The analysis unit, for example, adds a chatbot function to the counseling system, allowing students to seek advice in a text-based manner. For example, students input their consultation content through a chat window. The analysis unit also uses the chatbot function to provide a system that allows students to seek advice in a text-based manner. For example, AI analyzes text messages and provides appropriate advice. The analysis unit also adds a chatbot function to the counseling system, allowing students to seek advice in a text-based manner. For example, the chatbot answers students' questions in real time. This allows students to seek advice in a text-based manner, providing a more diverse means of communication.

[0036] The analysis unit analyzes the content of the student's consultation, and the AI ​​automatically presents related past consultation cases for reference. For example, the analysis unit analyzes the content of the student's consultation, and the AI ​​automatically presents related past consultation cases. For example, it refers to the consultation cases of past students who had the same problem. The analysis unit also analyzes the content of the student's consultation, and the AI ​​automatically presents related past consultation cases. For example, it refers to past cases where the same problem was solved. In this way, by referring to past consultation cases, it is possible to provide more appropriate advice.

[0037] The customization department analyzes each student's individual data, and the AI ​​automatically customizes the counseling plan and monitors their progress. For example, the customization department analyzes each student's individual data, and the AI ​​automatically customizes the counseling plan. For example, it provides the optimal plan taking into account academic performance and family environment. The customization department also analyzes each student's individual data, and the AI ​​automatically customizes the counseling plan and monitors their progress. For example, it regularly checks the student's condition and adjusts the plan. The customization department also analyzes each student's individual data, and the AI ​​automatically customizes the counseling plan. For example, it provides the optimal advice based on past counseling history. This makes it possible to customize a counseling plan based on each student's individual data and monitor their progress, enabling more effective support.

[0038] The customization unit allows the AI ​​to automatically provide personalized mental health resources based on the student's consultation history. For example, the customization unit analyzes the student's consultation history, and the AI ​​automatically provides personalized mental health resources. For example, it suggests relaxation techniques based on the content of past consultations. The customization unit also provides personalized mental health resources based on the student's consultation history. For example, it individually suggests stress management methods and relaxation techniques. The customization unit also analyzes the student's consultation history, and the AI ​​automatically provides personalized mental health resources. For example, it suggests stress relief methods based on the content of past consultations. This makes it possible to provide more effective support by providing personalized mental health resources based on the student's consultation history.

[0039] The customization department uses AI to automatically propose study plans based on each student's individual data, supporting both their academic performance and mental health. For example, the customization department analyzes each student's individual data, and AI automatically proposes study plans. For example, it provides the optimal plan taking into account academic performance and learning style. The customization department also uses AI to propose study plans that support both their academic performance and mental health based on each student's individual data. For example, it provides a study plan that incorporates stress management methods. The customization department also analyzes each student's individual data, and AI automatically proposes study plans. For example, it proposes the optimal study method based on past learning history. This allows for more effective support by proposing study plans based on each student's individual data and supporting both their academic performance and mental health.

[0040] The customization unit analyzes the content of students' counseling sessions, and the AI ​​automatically provides feedback to parents and teachers, strengthening the collaborative relationship. For example, the customization unit analyzes the content of students' counseling sessions, and the AI ​​automatically provides feedback to parents and teachers. For example, it summarizes important consultation content and notifies parents. The customization unit also analyzes the content of students' counseling sessions, and the AI ​​automatically provides feedback to parents and teachers. For example, it shares important information about academics and mental health. The customization unit also analyzes the content of students' counseling sessions, and the AI ​​automatically provides feedback to parents and teachers. For example, it makes suggestions to strengthen the collaborative relationship based on the content of the consultation. In this way, by providing feedback on the content of students' counseling sessions to parents and teachers, it is possible to strengthen the collaborative relationship and provide more effective support.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] Counseling departments can use virtual reality (VR) technology to provide virtual spaces where students can relax. For example, they can provide virtual spaces that combine natural scenery and relaxation music. Counseling departments can also use VR technology to provide interactive experiences to help students reduce stress. For example, they can provide guided meditation or deep breathing sessions in a virtual space. Counseling departments can also use VR technology to provide art therapy to encourage students to express themselves. For example, they can provide experiences such as drawing or creating music in a virtual space. This allows students to relax and reduce stress through self-expression.

[0043] The counseling provider can incorporate gamification elements to enable students to advance their self-study while having fun. For example, the learning content can be provided in a game format, allowing students to deepen their knowledge by solving quizzes and puzzles. The counseling provider can also motivate students by providing rewards and badges according to the progress of their self-study. For example, a digital badge can be earned when a specific learning goal is achieved. The counseling provider can also provide the learning content in a storytelling format, allowing students to learn while progressing through the story. For example, a history lesson can be presented as an adventure story. This allows students to advance their self-study while having fun.

[0044] The counseling department can expand its network of experts to allow students to access a wider variety of experts. For example, this could include not only psychological counselors but also experts such as nutritionists and fitness trainers. The counseling department can also display expert profiles and ratings to allow students to select an expert that suits them best. For example, it can display the expert's background, area of ​​expertise, and ratings from past clients. The counseling department can also introduce an expert matching algorithm to automatically recommend the expert best suited to the student's consultation. For example, it could recommend the most experienced expert for a specific problem. This allows students to access a wider variety of experts and receive appropriate support.

[0045] The counseling provision unit can introduce voice recognition technology to add a function that allows students to seek advice by voice. For example, students can use a microphone to input the content of their consultation by voice. The counseling provision unit also uses voice recognition technology to provide a function that allows students to seek advice by voice. For example, the voice input is converted into text and analyzed by AI. The counseling provision unit can also introduce voice recognition technology into the counseling environment to enable students to seek advice by voice. For example, the content of their consultation is input using voice commands. This allows students to seek advice by voice, enabling more natural communication.

[0046] The counseling provision unit can provide a replay function that automatically records the counseling content and allows students to review it later. For example, the counseling content is saved as text or audio. The counseling provision unit also uses the automatic recording function to save the counseling content and allow students to review it later. For example, the counseling content is saved in the cloud so that it can be accessed at any time. The counseling provision unit also automatically records the counseling content and provides a replay function. For example, the counseling content is saved in video format so that students can play it back later. This allows students to review the counseling content later.

[0047] The analysis unit can analyze the content of students' consultations, automatically group students with common problems, and provide group counseling. For example, AI can analyze the content of students' consultations and automatically group students with common problems. For example, students with the same concerns can be grouped together. The analysis unit can also analyze the content of students' consultations, and AI can automatically provide group counseling. For example, students who have consultations about stress management can be grouped together and counseling can be provided. The analysis unit can also analyze the content of students' consultations, and AI can group students with common problems. For example, students with academic concerns can be grouped together and counseling can be provided. This allows students with common problems to be grouped together and group counseling can be provided, making it possible to provide more effective support.

[0048] The analysis unit can add a chatbot function to the counseling system, allowing students to receive text-based consultations. For example, students input their consultation content through a chat window. The analysis unit also uses the chatbot function to provide a system that allows students to receive text-based consultations. For example, AI analyzes text messages and provides appropriate advice. The analysis unit also adds a chatbot function to the counseling system, allowing students to receive text-based consultations. For example, the chatbot answers students' questions in real time. This allows students to receive text-based consultations, providing a more diverse means of communication.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The counseling department will provide a 24 / 7 counseling environment. For example, they could use an AI chatbot to provide an environment where students can consult at any time. They could also allocate counselors on a shift basis to provide 24 / 7 support. Furthermore, they could provide a video call function through an online platform, creating an environment where students can talk directly with counselors. Step 2: The analysis unit simultaneously analyzes the consultation contents of multiple students. For example, it uses natural language processing technology to analyze the students' consultation contents and perform sentiment analysis. It can also cluster the consultation contents and group students who have common problems. It also evaluates the importance of the consultation contents and prioritizes the most urgent issues. Step 3: The customization department provides comprehensive counseling tailored to each student. For example, it analyzes individual data such as the student's past counseling history, academic performance, and home environment to create an optimal counseling plan. It also uses an emotion estimation function to monitor the student's emotional state in real time and dynamically adjust the counseling content. It also monitors the student's progress and updates the counseling plan as needed.

[0051] (Example 2) A counseling system according to an embodiment of the present invention utilizes AI to solve problems in children's mental health and education. This system provides a counseling environment available 24 hours a day, 365 days a year, and can simultaneously accommodate a large number of students while providing comprehensive counseling tailored to each student. As a result, the counseling system can provide a counseling environment available 24 hours a day, 365 days a year, and can simultaneously accommodate a large number of students while providing comprehensive counseling tailored to each student.

[0052] A counseling system according to an embodiment includes a counseling unit, an analysis unit, and a customization unit. The counseling unit provides a 24 / 7 counseling environment. For example, it uses an AI chatbot to provide an environment where students can receive counseling at any time. The counseling unit can also allocate counselors on a shift basis to provide 24 / 7 support. The counseling unit also provides a video call function through an online platform, creating an environment where students can directly interact with counselors. For example, the AI ​​chatbot uses natural language processing technology to understand the students' consultation content and provide appropriate advice. The shift counselors are available 24 / 7. The online platform is equipped with a video call function, providing an environment where students can consult with counselors face-to-face. The analysis unit simultaneously analyzes the consultation content of multiple students. For example, it uses natural language processing technology to analyze the students' consultation content and perform sentiment analysis. The analysis unit can also perform clustering of consultation content to group students with common problems. Furthermore, the analysis unit evaluates the importance of the consultation content and prioritizes the most urgent issues. For example, natural language processing technology analyzes students' consultation content as text data and performs sentiment analysis. Clustering technology automatically groups students with common issues. An importance assessment algorithm evaluates the urgency of the consultation content and prioritizes it. The customization unit provides comprehensive counseling tailored to each student. For example, it analyzes individual data such as the student's past consultation history, academic performance, and home environment to create an optimal counseling plan. The customization unit can also use an emotion estimation function to monitor the student's emotional state in real time and dynamically adjust the counseling content. Furthermore, the customization unit monitors the student's progress and updates the counseling plan as needed. For example, an individual data analysis algorithm creates an optimal counseling plan based on the student's past consultation history and academic performance. The emotion estimation function analyzes the student's facial expressions and voice to grasp their emotional state in real time. The progress monitoring system tracks the student's counseling progress and updates the plan as needed.As a result, the counseling system according to the embodiment provides a 24 / 7 counseling environment, and can simultaneously accommodate a large number of students while providing comprehensive counseling tailored to each individual student. For example, the system automatically analyzes the content of each student's consultation and provides an appropriate counseling plan. The system automatically records the counseling content and provides a replay function that allows students to review it later. The system uses an emotion estimation function to monitor the student's emotional state in real time and provide appropriate feedback.

[0053] The counseling unit can suggest related resources based on the content of the student's consultation and promote self-study. For example, the counseling unit analyzes the content of the student's consultation with the AI ​​and automatically suggests related online articles and videos. For example, if a student consults about stress management, a video on stress relief methods is suggested. The counseling unit also promotes self-study by having the AI ​​suggest related resources based on the content of the student's consultation. For example, if a student consults about academic worries, the AI ​​will provide an online article on study methods. The counseling unit also analyzes the content of the student's consultation and the AI ​​will automatically suggest related resources. For example, if a student consults about worries about friendships, a video on communication skills is provided. This can promote self-study by the student.

[0054] The counseling unit can analyze the content of a student's consultation and automatically escalate it to an appropriate specialist. For example, the counseling unit analyzes the content of a student's consultation and the AI ​​automatically escalates it to an appropriate specialist. For example, if a serious mental health problem is detected, a psychological counselor will be notified. The counseling unit will also add a function that analyzes the content of a student's consultation and the AI ​​escalates it to an appropriate specialist. For example, if a physical health problem is detected, a doctor will be notified. The counseling unit will also analyze the content of a student's consultation and the AI ​​automatically escalates it to an expert. For example, if a highly urgent problem is detected, an expert will be contacted immediately. This enables a rapid response by automatically escalating to an appropriate specialist.

[0055] The counseling provision unit can use the emotion estimation function to monitor the emotional state of the student in real time and provide counseling content according to the emotional state. The counseling provision unit, for example, uses the emotion estimation function to monitor the emotional state of the student in real time. For example, it analyzes the facial expressions and voice of the student during consultation to understand the emotional state. The counseling provision unit also monitors the emotional state of the student in real time and provides counseling content according to the emotion. For example, if stress is high, it suggests relaxation methods. The counseling provision unit also uses the emotion estimation function to monitor the emotional state of the student and provide appropriate counseling content. For example, if anxiety is high, it provides advice that gives a sense of security. This allows for more effective support by providing counseling content according to the student's emotional state.

[0056] The counseling provision unit can introduce voice recognition technology to add a function that allows students to seek advice by voice. The counseling provision unit, for example, introduces voice recognition technology into the counseling environment to add a function that allows students to seek advice by voice. For example, students use a microphone to input the content of their consultation by voice. The counseling provision unit also uses voice recognition technology to provide a function that allows students to seek advice by voice. For example, the voice input is converted into text and analyzed by AI. The counseling provision unit also introduces voice recognition technology into the counseling environment to enable students to seek advice by voice. For example, the content of their consultation is input using voice commands. This allows students to seek advice by voice, enabling more natural communication.

[0057] The counseling provision unit can provide a replay function that automatically records counseling content and allows students to review it later. The counseling provision unit, for example, provides a replay function that automatically records counseling content and allows students to review it later. For example, the consultation content is saved as text or audio. The counseling provision unit also uses the automatic recording function to save the counseling content and allow students to review it later. For example, the consultation content is saved in the cloud so that it can be accessed at any time. The counseling provision unit also automatically records counseling content and provides a replay function. For example, the consultation content is saved in video format so that students can play it back later. This allows students to review the counseling content later.

[0058] The counseling provision unit can use the emotion estimation function to predict the emotional state of the student before the student starts the consultation and start counseling at the optimal timing. The counseling provision unit, for example, uses the emotion estimation function to predict the emotional state of the student before the student starts the consultation. For example, it analyzes the student's facial expressions and behavioral patterns to understand the student's emotional state. The counseling provision unit also predicts the student's emotional state and starts counseling at the optimal timing. For example, it suggests counseling when stress is high. The counseling provision unit also uses the emotion estimation function to predict the student's emotional state and start counseling at an appropriate timing. For example, it provides counseling when anxiety is high. This makes it possible to predict the student's emotional state and start counseling at the optimal timing, thereby enabling more effective support.

[0059] The analysis unit can analyze the content of students' consultations, automatically group students with common problems, and provide group counseling. For example, AI can analyze the content of students' consultations and automatically group students with common problems. For example, students with the same concerns can be grouped together. The analysis unit can also analyze the content of students' consultations and AI can automatically provide group counseling. For example, students who have consultations about stress management can be grouped together and counseling can be provided. The analysis unit can also analyze the content of students' consultations and AI can group students with common problems. For example, students with academic concerns can be grouped together and counseling can be provided. This allows for more effective support by grouping students with common problems and providing group counseling.

[0060] The analysis unit can use the emotion estimation function to monitor the emotional balance within the group and suggest individual counseling as needed. The analysis unit, for example, uses the emotion estimation function to monitor the emotional balance within the group. For example, it analyzes the emotional states of students in the group in real time. The analysis unit also monitors the emotional balance within the group and suggests individual counseling as needed. For example, it provides individual counseling when a specific student is feeling stressed. The analysis unit also uses the emotion estimation function to monitor the emotional balance within the group and suggest appropriate counseling. For example, it provides individual counseling when the emotional state of the entire group is unstable. This enables more effective support by monitoring the emotional balance within the group and suggesting individual counseling as needed.

[0061] The analysis unit can add a chatbot function to the counseling system, allowing students to seek advice in a text-based manner. The analysis unit, for example, adds a chatbot function to the counseling system, allowing students to seek advice in a text-based manner. For example, students input their consultation content through a chat window. The analysis unit also uses the chatbot function to provide a system that allows students to seek advice in a text-based manner. For example, AI analyzes text messages and provides appropriate advice. The analysis unit also adds a chatbot function to the counseling system, allowing students to seek advice in a text-based manner. For example, the chatbot answers students' questions in real time. This allows students to seek advice in a text-based manner, providing a more diverse means of communication.

[0062] The analysis unit analyzes the content of the student's consultation, and the AI ​​automatically presents related past consultation cases for reference. For example, the analysis unit analyzes the content of the student's consultation, and the AI ​​automatically presents related past consultation cases. For example, it refers to the consultation cases of past students who had the same problem. The analysis unit also analyzes the content of the student's consultation, and the AI ​​automatically presents related past consultation cases. For example, it refers to past cases where the same problem was solved. In this way, by referring to past consultation cases, it is possible to provide more appropriate advice.

[0063] The analysis unit can use the emotion estimation function to compare the emotional states of students who are consulted simultaneously in real time and select the optimal counseling method. The analysis unit, for example, uses the emotion estimation function to compare the emotional states of students who are consulted simultaneously in real time. For example, it analyzes the emotion score of each student and selects the optimal counseling method. The analysis unit also compares the emotional states of students who are consulted simultaneously in real time and selects the optimal counseling method. For example, it provides special support to students with high emotion scores. The analysis unit also uses the emotion estimation function to compare the emotional states of students who are consulted simultaneously and selects the appropriate counseling method. For example, it customizes the counseling content according to the emotional state. This enables more effective support by comparing the emotional states of students who are consulted simultaneously in real time and selecting the optimal counseling method.

[0064] The customization department analyzes each student's individual data, and the AI ​​automatically customizes the counseling plan and monitors their progress. For example, the customization department analyzes each student's individual data, and the AI ​​automatically customizes the counseling plan. For example, it provides the optimal plan taking into account academic performance and family environment. The customization department also analyzes each student's individual data, and the AI ​​automatically customizes the counseling plan and monitors their progress. For example, it regularly checks the student's condition and adjusts the plan. The customization department also analyzes each student's individual data, and the AI ​​automatically customizes the counseling plan. For example, it provides the optimal advice based on past counseling history. This makes it possible to customize a counseling plan based on each student's individual data and monitor their progress, enabling more effective support.

[0065] The customization unit allows the AI ​​to automatically provide personalized mental health resources based on the student's consultation history. For example, the customization unit analyzes the student's consultation history, and the AI ​​automatically provides personalized mental health resources. For example, it suggests relaxation techniques based on the content of past consultations. The customization unit also provides personalized mental health resources based on the student's consultation history. For example, it individually suggests stress management methods and relaxation techniques. The customization unit also analyzes the student's consultation history, and the AI ​​automatically provides personalized mental health resources. For example, it suggests stress relief methods based on the content of past consultations. This makes it possible to provide more effective support by providing personalized mental health resources based on the student's consultation history.

[0066] The customization unit can use the emotion estimation function to track changes in the student's emotions and dynamically adjust the counseling content according to the emotions. The customization unit, for example, uses the emotion estimation function to track changes in the student's emotions. For example, it analyzes the student's facial expressions and voice during counseling to grasp the student's emotional state in real time. The customization unit also tracks changes in the student's emotions and dynamically adjusts the counseling content according to the emotions. For example, if stress increases, it suggests relaxation methods. The customization unit also uses the emotion estimation function to track changes in the student's emotions and provide appropriate counseling content. For example, if anxiety is strong, it provides advice that provides a sense of security. In this way, more effective support can be provided by tracking changes in the student's emotions and dynamically adjusting the counseling content according to the emotions.

[0067] The customization department uses AI to automatically propose study plans based on each student's individual data, supporting both their academic performance and mental health. For example, the customization department analyzes each student's individual data, and AI automatically proposes study plans. For example, it provides the optimal plan taking into account academic performance and learning style. The customization department also uses AI to propose study plans that support both their academic performance and mental health based on each student's individual data. For example, it provides a study plan that incorporates stress management methods. The customization department also analyzes each student's individual data, and AI automatically proposes study plans. For example, it proposes the optimal study method based on past learning history. This allows for more effective support by proposing study plans based on each student's individual data and supporting both their academic performance and mental health.

[0068] The customization unit analyzes the content of students' counseling sessions, and the AI ​​automatically provides feedback to parents and teachers, strengthening the collaborative relationship. For example, the customization unit analyzes the content of students' counseling sessions, and the AI ​​automatically provides feedback to parents and teachers. For example, it summarizes important consultation content and notifies parents. The customization unit also analyzes the content of students' counseling sessions, and the AI ​​automatically provides feedback to parents and teachers. For example, it shares important information about academics and mental health. The customization unit also analyzes the content of students' counseling sessions, and the AI ​​automatically provides feedback to parents and teachers. For example, it makes suggestions to strengthen the collaborative relationship based on the content of the consultation. In this way, by providing feedback on the content of students' counseling sessions to parents and teachers, it is possible to strengthen the collaborative relationship and provide more effective support.

[0069] The customization unit uses the emotion estimation function to enable the AI ​​to automatically suggest activities for relaxation and stress relief based on the student's emotional state. For example, the customization unit uses the emotion estimation function to suggest activities for relaxation and stress relief based on the student's emotional state. For example, it suggests meditation or deep breathing techniques. The customization unit also analyzes the student's emotional state in real time, and the AI ​​automatically suggests activities for relaxation and stress relief. For example, it provides relaxing music. The customization unit also uses the emotion estimation function to suggest appropriate activities based on the student's emotional state. For example, it suggests yoga or exercise depending on the emotional state. This enables more effective support by suggesting activities for relaxation and stress relief based on the student's emotional state.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] Counseling departments can use virtual reality (VR) technology to provide virtual spaces where students can relax. For example, they can provide virtual spaces that combine natural scenery and relaxation music. Counseling departments can also use VR technology to provide interactive experiences to help students reduce stress. For example, they can provide guided meditation or deep breathing sessions in a virtual space. Counseling departments can also use VR technology to provide art therapy to encourage students to express themselves. For example, they can provide experiences such as drawing or creating music in a virtual space. This allows students to relax and reduce stress through self-expression.

[0072] The counseling provider can incorporate gamification elements to enable students to advance their self-study while having fun. For example, the learning content can be provided in a game format, allowing students to deepen their knowledge by solving quizzes and puzzles. The counseling provider can also motivate students by providing rewards and badges according to the progress of their self-study. For example, a digital badge can be earned when a specific learning goal is achieved. The counseling provider can also provide the learning content in a storytelling format, allowing students to learn while progressing through the story. For example, a history lesson can be presented as an adventure story. This allows students to advance their self-study while having fun.

[0073] The counseling department can expand its network of experts to allow students to access a wider variety of experts. For example, this could include not only psychological counselors but also experts such as nutritionists and fitness trainers. The counseling department can also display expert profiles and ratings to allow students to select an expert that suits them best. For example, it can display the expert's background, area of ​​expertise, and ratings from past clients. The counseling department can also introduce an expert matching algorithm to automatically recommend the expert best suited to the student's consultation. For example, it could recommend the most experienced expert for a specific problem. This allows students to access a wider variety of experts and receive appropriate support.

[0074] The counseling provision unit can use the emotion estimation function to monitor the student's emotional state in real time and provide relaxation techniques according to the emotional state. For example, if stress is high, it can provide meditation or deep breathing guidance. The counseling provision unit can also monitor the student's emotional state and provide music or images according to the emotion. For example, if anxiety is high, it can display relaxing music or natural scenes. The counseling provision unit can also use the emotion estimation function to monitor the student's emotional state and provide appropriate feedback. For example, it can send positive messages according to the emotional state. This allows for more effective support by providing relaxation techniques according to the student's emotional state.

[0075] The counseling provision unit can introduce voice recognition technology to add a function that allows students to seek advice by voice. For example, students can use a microphone to input the content of their consultation by voice. The counseling provision unit also uses voice recognition technology to provide a function that allows students to seek advice by voice. For example, the voice input is converted into text and analyzed by AI. The counseling provision unit can also introduce voice recognition technology into the counseling environment to enable students to seek advice by voice. For example, the content of their consultation is input using voice commands. This allows students to seek advice by voice, enabling more natural communication.

[0076] The counseling provision unit can provide a replay function that automatically records the counseling content and allows students to review it later. For example, the counseling content is saved as text or audio. The counseling provision unit also uses the automatic recording function to save the counseling content and allow students to review it later. For example, the counseling content is saved in the cloud so that it can be accessed at any time. The counseling provision unit also automatically records the counseling content and provides a replay function. For example, the counseling content is saved in video format so that students can play it back later. This allows students to review the counseling content later.

[0077] The counseling provision unit can use the emotion estimation function to predict the emotional state of a student before the student begins counseling, and start counseling at the optimal timing. For example, it analyzes the student's facial expressions and behavioral patterns to understand the student's emotional state. The counseling provision unit also predicts the student's emotional state and starts counseling at the optimal timing. For example, it suggests counseling when stress is high. The counseling provision unit also uses the emotion estimation function to predict the student's emotional state and start counseling at the appropriate timing. For example, it offers counseling when anxiety is high. This makes it possible to predict the student's emotional state and start counseling at the optimal timing, enabling more effective support.

[0078] The analysis unit can analyze the content of students' consultations, automatically group students with common problems, and provide group counseling. For example, AI can analyze the content of students' consultations and automatically group students with common problems. For example, students with the same concerns can be grouped together. The analysis unit can also analyze the content of students' consultations, and AI can automatically provide group counseling. For example, students who have consultations about stress management can be grouped together and counseling can be provided. The analysis unit can also analyze the content of students' consultations, and AI can group students with common problems. For example, students with academic concerns can be grouped together and counseling can be provided. This allows students with common problems to be grouped together and group counseling can be provided, making it possible to provide more effective support.

[0079] The analysis unit can use the emotion estimation function to monitor the emotional balance within the group and suggest individual counseling as needed. For example, the emotion estimation function is used to monitor the emotional balance within the group. For example, the emotional states of students in the group are analyzed in real time. The analysis unit also monitors the emotional balance within the group and suggests individual counseling as needed. For example, individual counseling is provided when a specific student is feeling stressed. The analysis unit also uses the emotion estimation function to monitor the emotional balance within the group and suggest appropriate counseling. For example, individual counseling is provided when the emotional state of the entire group is unstable. This enables more effective support by monitoring the emotional balance within the group and suggesting individual counseling as needed.

[0080] The analysis unit can add a chatbot function to the counseling system, allowing students to receive text-based consultations. For example, students input their consultation content through a chat window. The analysis unit also uses the chatbot function to provide a system that allows students to receive text-based consultations. For example, AI analyzes text messages and provides appropriate advice. The analysis unit also adds a chatbot function to the counseling system, allowing students to receive text-based consultations. For example, the chatbot answers students' questions in real time. This allows students to receive text-based consultations, providing a more diverse means of communication.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The counseling department will provide a 24 / 7 counseling environment. For example, they could use an AI chatbot to provide an environment where students can consult at any time. They could also allocate counselors on a shift basis to provide 24 / 7 support. Furthermore, they could provide a video call function through an online platform, creating an environment where students can talk directly with counselors. Step 2: The analysis unit simultaneously analyzes the consultation contents of multiple students. For example, it uses natural language processing technology to analyze the students' consultation contents and perform sentiment analysis. It can also cluster the consultation contents and group students who have common problems. It also evaluates the importance of the consultation contents and prioritizes the most urgent issues. Step 3: The customization department provides comprehensive counseling tailored to each student. For example, it analyzes individual data such as the student's past counseling history, academic performance, and home environment to create an optimal counseling plan. It also uses an emotion estimation function to monitor the student's emotional state in real time and dynamically adjust the counseling content. It also monitors the student's progress and updates the counseling plan as needed.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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. 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. The Counseling Department provides a 24-hour counseling environment, An analysis section that simultaneously analyzes the consultation contents of multiple students, We have a customization department that provides comprehensive counseling tailored to each student. A system characterized by:

2. The counseling providing unit Analyze the student's consultation and automatically escalate it to the appropriate specialist 2. The system of claim 1.

3. The analysis unit Analyze the consultation contents of the students, automatically group students who have common problems, and provide group counseling 2. The system of claim 1.

4. The customization unit By analyzing the student's individual data, the AI ​​automatically customizes a counseling plan and monitors progress.

2. The system of claim 1.

5. The counseling providing unit Monitor the emotional state of the student in real time and provide counseling content according to the emotional state.

2. The system of claim 1.

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