System

Abstract

An object of a system according to an embodiment is to provide an environment in which a student can easily consult and to quickly provide appropriate advice.SOLUTION: A system according to an embodiment includes a consultation reception unit, an analysis unit, and an advice providing unit. The consultation reception part receives consultation contents of the student. The analysis unit analyzes the consultation contents accepted by the consultation acceptance unit. The advice providing unit provides appropriate advice on the basis of a result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology did not provide a sufficient environment for students to feel comfortable seeking advice, making it difficult to provide appropriate advice quickly.

[0005] The system according to the embodiment aims to provide an environment in which students can easily seek advice and to provide appropriate advice quickly. [Means for solving the problem]

[0006] The system according to the embodiment includes a consultation reception unit, an analysis unit, and an advice providing unit. The consultation reception unit receives consultation requests from students. The analysis unit analyzes the consultation requests received by the consultation reception unit. The advice providing unit provides appropriate advice based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment provides an environment in which students can easily seek advice and can provide appropriate advice quickly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The consultation system according to an embodiment of the present invention utilizes AI technology to allow high school students to easily seek advice on problems they are facing. This system accepts and analyzes the content of students' consultations and provides appropriate advice. As a result, the consultation system provides appropriate support for the problems high school students are facing, creating an environment where students can feel at ease and seek advice.

[0029] The consultation system according to the embodiment includes a consultation reception unit, an analysis unit, and an advice provision unit. The consultation reception unit receives consultation content from students. For example, when a student inputs a problem such as "I'm not doing well in my studies," the consultation reception unit accepts the content. The consultation reception unit can also accept anonymous consultations. For example, by anonymously inputting the consultation content, students can receive consultation while protecting their privacy. The analysis unit analyzes the consultation content received by the consultation reception unit. For example, the generation AI analyzes the consultation content using a text generation AI (e.g., LLM). The generation AI can also perform emotion analysis to understand the emotional state of the person seeking advice. The generation AI can also use data mining technology to compare the consultation content with past consultation data and generate optimal advice. For example, the generation AI provides effective advice to students with similar problems based on past consultation data. The advice provision unit provides appropriate advice based on the results of the analysis by the analysis unit. For example, the generation AI suggests specific study plans and study methods to a student struggling with poor academic performance. The generation AI can also suggest relaxation techniques and counseling resources to students struggling with mental health issues. The generation AI can also automatically generate individual counseling plans based on the consultation content and provide them to students. For example, the generation AI automatically generates a counseling plan recommended by a psychological counseling expert based on the consultation content. This allows the counseling system according to the embodiment to provide an environment where students can easily seek advice and receive appropriate support. For example, students can enter their consultation content anonymously, allowing them to receive consultation while protecting their privacy. The generation AI can also analyze the consultation content and provide optimal advice, allowing students to receive effective support for their problems.

[0030] The advice providing unit can propose specific study plans or study methods for students who are struggling with academic failure. For example, the generation AI in the advice providing unit analyzes the content of a student's consultation and automatically generates an individual counseling plan based on that content. For example, for a student struggling with academic failure, it proposes a specific study plan and study method. The advice providing unit also automatically generates a counseling plan recommended by a psychological counseling expert based on the content of the student's consultation. For example, for a student struggling with mental health, it proposes relaxation techniques and stress management methods. The advice providing unit also analyzes the content of a student's consultation and compares it with past consultation data to automatically generate an optimal counseling plan. For example, for a student struggling with friendships, it provides specific advice on improving communication skills. In this way, by providing specific study plans and study methods to students struggling with academic failure, it supports the improvement of academic performance.

[0031] The advice providing unit can automatically generate an individual counseling plan based on the consultation content. The advice providing unit, for example, uses an emotion estimation function to analyze the emotional state of the client in real time and respond appropriately according to the emotion. For example, if the client is feeling stressed, it suggests a relaxation technique. The advice providing unit also uses the emotion estimation function to analyze the emotional state of the client in real time and provide appropriate advice according to the emotion. For example, if the client is feeling anxious, it sends a message that gives a sense of security. The advice providing unit also uses the emotion estimation function to analyze the emotional state of the client in real time and provide appropriate resources according to the emotion. For example, if the client is feeling sad, it suggests counseling resources. In this way, an individual counseling plan is provided based on the student's consultation content, thereby achieving more appropriate support.

[0032] The advice-providing unit can analyze the content of the consultation and compare it with past consultation history to provide the most appropriate advice. For example, the generation AI in the advice-providing unit analyzes the content of the consultation and automatically sets up a group session to connect students with the same concerns. For example, it could connect students who are struggling with academic failure to provide a place where they can encourage each other. The advice-providing unit also automatically sets up an online group session to connect students with the same concerns based on the content of the consultation. For example, it could connect students who are struggling with mental health to provide a place to share relaxation techniques. The advice-providing unit also analyzes the content of the consultation and automatically sets up a group session to connect students with the same concerns. For example, it could connect students who are struggling with friendships to provide a place to discuss improving communication skills. This allows for more accurate advice to be provided by comparing it with past consultation history.

[0033] The advice providing unit can automatically set up a group session based on the consultation content and connect students with the same concerns. The advice providing unit, for example, uses an emotion estimation function to automatically suggest relaxation music according to the emotional state of the person seeking advice. For example, if the person seeking advice is feeling stressed, it suggests relaxing music. The advice providing unit also uses the emotion estimation function to automatically suggest relaxation videos according to the emotional state of the person seeking advice. For example, if the person seeking advice is feeling anxious, it suggests videos that give a sense of security. The advice providing unit also uses the emotion estimation function to automatically suggest relaxation music or videos according to the emotional state of the person seeking advice. For example, if the person seeking advice is feeling sad, it suggests soothing music or videos. In this way, students with the same concerns can be connected, allowing them to encourage each other and support each other in solving their problems.

[0034] The advice providing unit analyzes the content of the consultation and can automatically make a referral to a local counseling center or specialist. In the advice providing unit, for example, the generation AI analyzes the content of the consultation and automatically makes a referral to a local counseling center or specialist. For example, for a consultation regarding mental health, a referral to a nearby counseling center is made. In addition, the advice providing unit, the generation AI automatically makes a referral to a local specialist based on the content of the consultation. For example, for a consultation regarding poor academic performance, a referral to a local education specialist is made. In addition, the advice providing unit, the generation AI analyzes the content of the consultation and automatically makes a referral to a local counseling center or specialist. For example, for a consultation regarding friendships, a referral to a local psychological counselor is made. In this way, more specialized support can be provided by automatically making a referral to a local counseling center or specialist.

[0035] The advice-providing unit can analyze the learning history and automatically generate and provide an individual learning plan. In the advice-providing unit, for example, the generation AI analyzes the student's learning history and automatically generates an individual learning plan based on that data. For example, it may provide a plan to focus on weak areas in a particular subject. In addition, the advice-providing unit analyzes the student's learning history and automatically generates an individual learning plan based on past grades and learning patterns. For example, it may suggest efficient study methods and time management methods. In addition, the advice-providing unit analyzes the student's learning history and automatically generates an individual learning plan. For example, it may suggest appropriate learning materials and resources according to the student's learning progress. In this way, by providing an individual learning plan based on the student's learning history, it supports the improvement of academic performance.

[0036] The advice-providing unit can monitor learning progress in real time and automatically adjust the learning plan as needed. In the advice-providing unit, for example, the generation AI monitors the student's learning progress in real time and automatically adjusts the learning plan as needed. For example, if learning progress is falling behind, the learning plan is revised and an efficient study method is suggested. In addition, the advice-providing unit monitors the student's learning progress in real time and automatically adjusts the learning plan. For example, if progress is falling behind in a particular subject, a learning plan focusing on that subject is suggested. In addition, the advice-providing unit monitors the student's learning progress in real time and automatically adjusts the learning plan as needed. For example, if learning progress is going well, a learning plan for moving on to the next step is suggested. In this way, automatic adjustment of the learning plan according to learning progress supports efficient learning.

[0037] The advice-providing unit can analyze the causes of poor academic performance and make suggestions for improving the home environment and lifestyle. For example, the advice-providing unit uses a generation AI to analyze the causes of a student's poor academic performance and make suggestions for improving the home environment and lifestyle. For example, it provides specific advice for creating a good learning environment at home. The advice-providing unit also uses a generation AI to analyze the causes of a student's poor academic performance and make suggestions for improving lifestyle. For example, it suggests maintaining a regular lifestyle and ensuring adequate sleep time. The advice-providing unit also uses a generation AI to analyze the causes of a student's poor academic performance and make suggestions for improving the home environment and lifestyle. For example, it emphasizes the importance of communication at home and promotes dialogue between parents and children. In this way, the advice-providing unit analyzes the causes of poor academic performance and makes suggestions for improving the home environment and lifestyle, thereby supporting improvement of academic performance.

[0038] The advice providing unit can automatically collect learning resources and suggest the most suitable learning materials for students. For example, the generative AI in the advice providing unit automatically collects learning resources and suggests the most suitable learning materials for students. For example, it suggests online learning materials or reference books for a specific subject. The advice providing unit also automatically collects learning resources and suggests learning materials that suit the student's learning style. For example, it suggests video learning materials for students who are good at visual learning. The advice providing unit also automatically collects learning resources and suggests the most suitable learning materials for students. For example, it suggests learning materials at an appropriate level according to the student's learning progress. In this way, the automatic collection of learning resources and suggesting the most suitable learning materials supports the improvement of academic performance.

[0039] The advice providing unit can analyze mental health conditions and automatically generate and provide individual mental health plans. For example, the advice providing unit uses a generation AI to analyze a student's mental health condition and automatically generate an individual mental health plan based on that data. For example, it can provide a plan that includes stress management and relaxation techniques. The advice providing unit also uses a generation AI to analyze a student's mental health condition and automatically generate a mental health plan recommended by a psychological counseling expert. For example, it can suggest regular counseling sessions and relaxation techniques. The advice providing unit also uses a generation AI to analyze a student's mental health condition and compare it with past data to automatically generate an optimal mental health plan. For example, it can provide a specific action plan related to mental health. This supports mental health by providing an individual mental health plan based on the student's mental health condition.

[0040] The advice-providing unit can monitor the mental health state and automatically encourage consultation with a specialist when necessary. In the advice-providing unit, for example, the generating AI monitors the student's mental health state in real time and automatically encourages consultation with a specialist when necessary. For example, if the student's mental health state is deteriorating, it will suggest consulting a counselor. In addition, the advice-providing unit monitors the student's mental health state and automatically encourages consultation with a psychological counselor or doctor. For example, if the stress level is high, it will suggest specialist support. In addition, the advice-providing unit monitors the student's mental health state and automatically encourages consultation with a specialist when necessary. For example, if the student's mental health state is unstable, it will suggest regular counseling. In this way, the mental health state is monitored and appropriate support is provided by encouraging consultation with a specialist when necessary.

[0041] The advice-providing unit can analyze the student's mental health status and suggest support systems at school and at home. For example, the generative AI in the advice-providing unit analyzes the student's mental health status and suggests a support system at school. For example, it may suggest workshops or counseling sessions related to mental health. The generative AI in the advice-providing unit also analyzes the student's mental health status and suggests a support system at home. For example, it may emphasize the importance of communication at home and promote dialogue between parents and children. The generative AI in the advice-providing unit also analyzes the student's mental health status and suggests a support system at school and at home. For example, it may introduce mental health resources and support groups. In this way, support systems at school and at home are suggested based on the student's mental health status, thereby supporting mental health.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The consultation system can also be equipped with a "reminder section." This section has the function of periodically reminding students of the goals and tasks they have set. For example, if a student sets a goal of "studying for 30 minutes every day," the reminder section will send a reminder at the specified time every day. The reminder section can also monitor students' progress and send encouraging messages to help them achieve their goals. Furthermore, the reminder section can send notifications when the deadline for a task set by the student is approaching, encouraging them to complete the task. This makes it easier for students to achieve their goals and helps improve their academic performance.

[0044] The consultation system can further include a "feedback unit." The feedback unit has the function of collecting and analyzing feedback on the advice received by students. For example, after a student implements the advice, the feedback unit can input feedback on its effectiveness. The feedback unit can also make suggestions for improving the quality of the advice based on the collected feedback. Furthermore, the feedback unit can monitor the extent to which the student has implemented the advice and provide additional advice according to the degree of implementation. This maximizes the effectiveness of the advice and helps students solve their problems.

[0045] The counseling system can also be equipped with a "resource provider." This has the function of providing the learning and support resources that students need. For example, if a student is having trouble with a particular subject, it can suggest online learning materials or reference books related to that subject. The resource provider can also provide relaxation techniques and counseling resources if a student seeks advice about mental health. Furthermore, if a student is having trouble with friendships, the resource provider can provide resources to improve communication skills. This allows the system to provide students with the appropriate resources they need and help them solve their problems.

[0046] The counseling system can also be equipped with a "community section." This section has the function of connecting students with the same concerns. For example, it can connect students who are struggling with academic failure and provide a place where they can encourage each other. The community section can also connect students who are struggling with mental health and provide a place to share relaxation techniques. Furthermore, the community section can connect students who are struggling with friendships and provide a place to hold discussions to improve communication skills. This allows students to support each other and help solve their problems.

[0047] The consultation system can further be equipped with a "progress monitoring unit." This progress monitoring unit has the function of monitoring students' learning progress in real time and automatically adjusting their learning plans as necessary. For example, if their learning progress is falling behind, it will review their learning plan and suggest more efficient study methods. In addition, if their progress is falling behind in a specific subject, the progress monitoring unit can also suggest a learning plan that focuses on that subject. Furthermore, if their learning progress is going well, the progress monitoring unit can also suggest a learning plan for moving on to the next step. This allows the system to provide appropriate support according to students' learning progress and help improve their academic performance.

[0048] The counseling system can further include a "mental health plan generation unit." The mental health plan generation unit has the function of analyzing students' mental health conditions and automatically generating individual mental health plans based on that data. For example, it can provide plans that include stress management and relaxation techniques. The mental health plan generation unit can also automatically generate mental health plans recommended by psychological counseling experts. Furthermore, the mental health plan generation unit can automatically generate the optimal mental health plan by comparing it with past data. This allows the system to provide individual mental health plans based on students' mental health conditions and support their mental health.

[0049] The counseling system can further include a "support system suggestion unit." The support system suggestion unit has the function of analyzing the student's mental health status and proposing support systems at school and at home. For example, it can suggest workshops or counseling sessions related to mental health. The support system suggestion unit can also emphasize the importance of communication at home and promote dialogue between parents and children. Furthermore, the support system suggestion unit can introduce mental health resources and support groups. This allows the system to suggest support systems at school and at home based on the student's mental health status and support mental health.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The consultation reception unit accepts the student's consultation content. For example, if a student inputs a concern such as "I'm not doing well in my studies," the consultation reception unit will accept the content. The consultation reception unit can also accept anonymous consultations. For example, by having students input their consultation content anonymously, they can receive consultation while protecting their privacy. Step 2: The analysis unit analyzes the consultation content received by the consultation reception unit. For example, the generation AI analyzes the consultation content using a text generation AI (e.g., LLM). The generation AI can also perform emotion analysis to understand the emotional state of the person seeking advice. The generation AI can also use data mining technology to compare the advice with past consultation data and generate optimal advice. For example, the generation AI can provide effective advice to students with similar problems based on past consultation data. Step 3: The advice provider provides appropriate advice based on the results of the analysis by the analyzer. For example, the generator AI suggests specific study plans and methods for students struggling with academic performance. The generator AI can also suggest relaxation techniques and counseling resources for students struggling with mental health issues. The generator AI can also automatically generate individual counseling plans based on the content of the consultation and provide them to students. For example, the generator AI automatically generates a counseling plan recommended by a psychological counseling expert based on the content of the consultation.

[0052] (Example 2) The consultation system according to an embodiment of the present invention utilizes AI technology to allow high school students to easily seek advice on problems they are facing. This system accepts and analyzes the content of students' consultations and provides appropriate advice. As a result, the consultation system provides appropriate support for the problems high school students are facing, creating an environment where students can feel at ease and seek advice.

[0053] The consultation system according to the embodiment includes a consultation reception unit, an analysis unit, and an advice provision unit. The consultation reception unit receives consultation content from students. For example, when a student inputs a problem such as "I'm not doing well in my studies," the consultation reception unit accepts the content. The consultation reception unit can also accept anonymous consultations. For example, by anonymously inputting the consultation content, students can receive consultation while protecting their privacy. The analysis unit analyzes the consultation content received by the consultation reception unit. For example, the generation AI analyzes the consultation content using a text generation AI (e.g., LLM). The generation AI can also perform emotion analysis to understand the emotional state of the person seeking advice. The generation AI can also use data mining technology to compare the consultation content with past consultation data and generate optimal advice. For example, the generation AI provides effective advice to students with similar problems based on past consultation data. The advice provision unit provides appropriate advice based on the results of the analysis by the analysis unit. For example, the generation AI suggests specific study plans and study methods to a student struggling with poor academic performance. The generation AI can also suggest relaxation techniques and counseling resources to students struggling with mental health issues. The generation AI can also automatically generate individual counseling plans based on the consultation content and provide them to students. For example, the generation AI automatically generates a counseling plan recommended by a psychological counseling expert based on the consultation content. This allows the counseling system according to the embodiment to provide an environment where students can easily seek advice and receive appropriate support. For example, students can enter their consultation content anonymously, allowing them to receive consultation while protecting their privacy. The generation AI can also analyze the consultation content and provide optimal advice, allowing students to receive effective support for their problems.

[0054] The advice providing unit can propose specific study plans or study methods for students who are struggling with academic failure. For example, the generation AI in the advice providing unit analyzes the content of a student's consultation and automatically generates an individual counseling plan based on that content. For example, for a student struggling with academic failure, it proposes a specific study plan and study method. The advice providing unit also automatically generates a counseling plan recommended by a psychological counseling expert based on the content of the student's consultation. For example, for a student struggling with mental health, it proposes relaxation techniques and stress management methods. The advice providing unit also analyzes the content of a student's consultation and compares it with past consultation data to automatically generate an optimal counseling plan. For example, for a student struggling with friendships, it provides specific advice on improving communication skills. In this way, by providing specific study plans and study methods to students struggling with academic failure, it supports the improvement of academic performance.

[0055] The advice-providing unit can suggest relaxation methods or counseling resources for mental health. For example, the generation AI in the advice-providing unit analyzes the content of a student's consultation and compares it with past consultation history to provide the most appropriate advice. For example, for a consultation about poor academic performance, it suggests methods that have solved similar problems in the past. The advice-providing unit also analyzes the content of a student's consultation and compares it with past consultation history to provide the most appropriate resources and support. For example, for a consultation about mental health, it suggests relaxation methods or counseling resources that have been effective in the past. The advice-providing unit also analyzes the content of a student's consultation and compares it with past consultation history to provide the most appropriate advice. For example, for a consultation about friendships, it suggests methods for improving communication skills that have been successful in the past. In this way, relaxation methods and counseling resources are provided to students who are concerned about their mental health, thereby supporting their mental health.

[0056] The advice providing unit can automatically generate an individual counseling plan based on the consultation content. The advice providing unit, for example, uses an emotion estimation function to analyze the emotional state of the client in real time and respond appropriately according to the emotion. For example, if the client is feeling stressed, it suggests a relaxation technique. The advice providing unit also uses the emotion estimation function to analyze the emotional state of the client in real time and provide appropriate advice according to the emotion. For example, if the client is feeling anxious, it sends a message that gives a sense of security. The advice providing unit also uses the emotion estimation function to analyze the emotional state of the client in real time and provide appropriate resources according to the emotion. For example, if the client is feeling sad, it suggests counseling resources. In this way, an individual counseling plan is provided based on the student's consultation content, thereby achieving more appropriate support.

[0057] The advice-providing unit can analyze the content of the consultation and compare it with past consultation history to provide the most appropriate advice. For example, the generation AI in the advice-providing unit analyzes the content of the consultation and automatically sets up a group session to connect students with the same concerns. For example, it could connect students who are struggling with academic failure to provide a place where they can encourage each other. The advice-providing unit also automatically sets up an online group session to connect students with the same concerns based on the content of the consultation. For example, it could connect students who are struggling with mental health to provide a place to share relaxation techniques. The advice-providing unit also analyzes the content of the consultation and automatically sets up a group session to connect students with the same concerns. For example, it could connect students who are struggling with friendships to provide a place to discuss improving communication skills. This allows for more accurate advice to be provided by comparing it with past consultation history.

[0058] The advice providing unit uses an emotion estimation function to analyze the emotional state of the person seeking advice in real time and can respond appropriately based on that emotion. In the advice providing unit, for example, the generation AI analyzes the content of the consultation and automatically makes a referral to a local counseling center or specialist. For example, for a consultation about mental health, a referral to a nearby counseling center is made. In addition, the advice providing unit automatically makes a referral to a local specialist based on the content of the consultation. For example, for a consultation about poor academic performance, a referral to a local education specialist is made. In addition, the advice providing unit analyzes the content of the consultation and automatically makes a referral to a local counseling center or specialist. For example, for a consultation about friendships, a referral to a local psychological counselor is made. In this way, more effective support can be provided by responding appropriately based on the emotional state of the person seeking advice.

[0059] The advice providing unit can automatically set up a group session based on the consultation content and connect students with the same concerns. The advice providing unit, for example, uses an emotion estimation function to automatically suggest relaxation music according to the emotional state of the person seeking advice. For example, if the person seeking advice is feeling stressed, it suggests relaxing music. The advice providing unit also uses the emotion estimation function to automatically suggest relaxation videos according to the emotional state of the person seeking advice. For example, if the person seeking advice is feeling anxious, it suggests videos that give a sense of security. The advice providing unit also uses the emotion estimation function to automatically suggest relaxation music or videos according to the emotional state of the person seeking advice. For example, if the person seeking advice is feeling sad, it suggests soothing music or videos. In this way, students with the same concerns can be connected, allowing them to encourage each other and support each other in solving their problems.

[0060] The advice providing unit analyzes the content of the consultation and can automatically make a referral to a local counseling center or specialist. In the advice providing unit, for example, the generation AI analyzes the content of the consultation and automatically makes a referral to a local counseling center or specialist. For example, for a consultation regarding mental health, a referral to a nearby counseling center is made. In addition, the advice providing unit, the generation AI automatically makes a referral to a local specialist based on the content of the consultation. For example, for a consultation regarding poor academic performance, a referral to a local education specialist is made. In addition, the advice providing unit, the generation AI analyzes the content of the consultation and automatically makes a referral to a local counseling center or specialist. For example, for a consultation regarding friendships, a referral to a local psychological counselor is made. In this way, more specialized support can be provided by automatically making a referral to a local counseling center or specialist.

[0061] The advice providing unit can use the emotion estimation function to automatically suggest relaxation music or videos according to the emotional state of the client. For example, the advice providing unit uses the emotion estimation function to automatically suggest relaxation music according to the emotional state of the client. For example, if the client is feeling stressed, it suggests relaxing music. The advice providing unit also uses the emotion estimation function to automatically suggest relaxation videos according to the emotional state of the client. For example, if the client is feeling anxious, it suggests videos that give a sense of security. The advice providing unit also uses the emotion estimation function to automatically suggest relaxation music or videos according to the emotional state of the client. For example, if the client is feeling sad, it suggests music or videos that are soothing to the soul. In this way, providing relaxation music or videos according to the emotional state of the client supports mental stability.

[0062] The advice-providing unit can analyze the learning history and automatically generate and provide an individual learning plan. In the advice-providing unit, for example, the generation AI analyzes the student's learning history and automatically generates an individual learning plan based on that data. For example, it may provide a plan to focus on weak areas in a particular subject. In addition, the advice-providing unit analyzes the student's learning history and automatically generates an individual learning plan based on past grades and learning patterns. For example, it may suggest efficient study methods and time management methods. In addition, the advice-providing unit analyzes the student's learning history and automatically generates an individual learning plan. For example, it may suggest appropriate learning materials and resources according to the student's learning progress. In this way, by providing an individual learning plan based on the student's learning history, it supports the improvement of academic performance.

[0063] The advice-providing unit can monitor learning progress in real time and automatically adjust the learning plan as needed. In the advice-providing unit, for example, the generation AI monitors the student's learning progress in real time and automatically adjusts the learning plan as needed. For example, if learning progress is falling behind, the learning plan is revised and an efficient study method is suggested. In addition, the advice-providing unit monitors the student's learning progress in real time and automatically adjusts the learning plan. For example, if progress is falling behind in a particular subject, a learning plan focusing on that subject is suggested. In addition, the advice-providing unit monitors the student's learning progress in real time and automatically adjusts the learning plan as needed. For example, if learning progress is going well, a learning plan for moving on to the next step is suggested. In this way, automatic adjustment of the learning plan according to learning progress supports efficient learning.

[0064] The advice providing unit can use the emotion estimation function to provide advice to increase a student's motivation for their studies. For example, the advice providing unit uses the emotion estimation function to provide advice to increase a student's motivation for their studies. For example, if a student's motivation to study is declining, the advice providing unit presents an encouraging message or a success story. The advice providing unit also uses the emotion estimation function to provide specific advice to increase a student's motivation for their studies. For example, the advice providing unit sets a learning goal and suggests steps to achieve that goal. The advice providing unit also uses the emotion estimation function to provide advice to increase a student's motivation for their studies. For example, the advice providing unit provides positive feedback according to the student's progress in learning to maintain their motivation to study. In this way, the advice provided to increase a student's motivation for their studies improves their motivation to study.

[0065] The advice-providing unit can analyze the causes of poor academic performance and make suggestions for improving the home environment and lifestyle. For example, the advice-providing unit uses a generation AI to analyze the causes of a student's poor academic performance and make suggestions for improving the home environment and lifestyle. For example, it provides specific advice for creating a good learning environment at home. The advice-providing unit also uses a generation AI to analyze the causes of a student's poor academic performance and make suggestions for improving lifestyle. For example, it suggests maintaining a regular lifestyle and ensuring adequate sleep time. The advice-providing unit also uses a generation AI to analyze the causes of a student's poor academic performance and make suggestions for improving the home environment and lifestyle. For example, it emphasizes the importance of communication at home and promotes dialogue between parents and children. In this way, the advice-providing unit analyzes the causes of poor academic performance and makes suggestions for improving the home environment and lifestyle, thereby supporting improvement of academic performance.

[0066] The advice providing unit can automatically collect learning resources and suggest the most suitable learning materials for students. For example, the generative AI in the advice providing unit automatically collects learning resources and suggests the most suitable learning materials for students. For example, it suggests online learning materials or reference books for a specific subject. The advice providing unit also automatically collects learning resources and suggests learning materials that suit the student's learning style. For example, it suggests video learning materials for students who are good at visual learning. The advice providing unit also automatically collects learning resources and suggests the most suitable learning materials for students. For example, it suggests learning materials at an appropriate level according to the student's learning progress. In this way, the automatic collection of learning resources and suggesting the most suitable learning materials supports the improvement of academic performance.

[0067] The advice providing unit can use the emotion estimation function to suggest relaxation methods to reduce academic stress. The advice providing unit, for example, uses the emotion estimation function to suggest relaxation methods to reduce academic stress for students. For example, if the student is feeling stressed, it may suggest deep breathing or meditation. The advice providing unit also uses the emotion estimation function to suggest specific relaxation methods to reduce academic stress for students. For example, it may suggest relaxing music or videos. The advice providing unit also uses the emotion estimation function to suggest relaxation methods to reduce academic stress for students. For example, it may suggest apps or tools for stress management. In this way, suggesting relaxation methods to reduce academic stress supports mental health.

[0068] The advice providing unit can analyze mental health conditions and automatically generate and provide individual mental health plans. For example, the advice providing unit uses a generation AI to analyze a student's mental health condition and automatically generate an individual mental health plan based on that data. For example, it can provide a plan that includes stress management and relaxation techniques. The advice providing unit also uses a generation AI to analyze a student's mental health condition and automatically generate a mental health plan recommended by a psychological counseling expert. For example, it can suggest regular counseling sessions and relaxation techniques. The advice providing unit also uses a generation AI to analyze a student's mental health condition and compare it with past data to automatically generate an optimal mental health plan. For example, it can provide a specific action plan related to mental health. This supports mental health by providing an individual mental health plan based on the student's mental health condition.

[0069] The advice-providing unit can monitor the mental health state and automatically encourage consultation with a specialist when necessary. In the advice-providing unit, for example, the generating AI monitors the student's mental health state in real time and automatically encourages consultation with a specialist when necessary. For example, if the student's mental health state is deteriorating, it will suggest consulting a counselor. In addition, the advice-providing unit monitors the student's mental health state and automatically encourages consultation with a psychological counselor or doctor. For example, if the stress level is high, it will suggest specialist support. In addition, the advice-providing unit monitors the student's mental health state and automatically encourages consultation with a specialist when necessary. For example, if the student's mental health state is unstable, it will suggest regular counseling. In this way, the mental health state is monitored and appropriate support is provided by encouraging consultation with a specialist when necessary.

[0070] The advice providing unit can use the emotion estimation function to suggest relaxation methods and counseling resources according to the student's mental health state. For example, the advice providing unit uses the emotion estimation function to suggest relaxation methods according to the student's mental health state. For example, if the student is feeling stressed, it suggests deep breathing or meditation. The advice providing unit also uses the emotion estimation function to suggest counseling resources according to the student's mental health state. For example, if the student's mental health state is deteriorating, it suggests consulting a psychological counselor. The advice providing unit also uses the emotion estimation function to suggest relaxation methods and counseling resources according to the student's mental health state. For example, if the student's mental health state is unstable, it suggests relaxation music or videos. In this way, mental health is supported by providing relaxation methods and counseling resources according to the student's mental health state.

[0071] The advice-providing unit can analyze the student's mental health status and suggest support systems at school and at home. For example, the generative AI in the advice-providing unit analyzes the student's mental health status and suggests a support system at school. For example, it may suggest workshops or counseling sessions related to mental health. The generative AI in the advice-providing unit also analyzes the student's mental health status and suggests a support system at home. For example, it may emphasize the importance of communication at home and promote dialogue between parents and children. The generative AI in the advice-providing unit also analyzes the student's mental health status and suggests a support system at school and at home. For example, it may introduce mental health resources and support groups. In this way, support systems at school and at home are suggested based on the student's mental health status, thereby supporting mental health.

[0072] The advice providing unit can use the emotion estimation function to suggest hobbies and activities according to the student's mental health state, thereby encouraging mental refreshment. The advice providing unit, for example, uses the emotion estimation function to suggest hobbies and activities according to the student's mental health state. For example, if the student is feeling stressed, the advice providing unit suggests hobbies and activities that will help them relax. The advice providing unit also uses the emotion estimation function to suggest specific hobbies and activities according to the student's mental health state. For example, if the student's mental health state is deteriorating, the advice providing unit suggests creative activities such as art or music. The advice providing unit also uses the emotion estimation function to suggest hobbies and activities according to the student's mental health state, thereby encouraging mental refreshment. For example, if the student's mental health state is unstable, the advice providing unit suggests outdoor activities such as nature walks or gardening. In this way, suggesting hobbies and activities according to the student's mental health state supports mental refreshment.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The consultation system can also be equipped with a "reminder section." This section has the function of periodically reminding students of the goals and tasks they have set. For example, if a student sets a goal of "studying for 30 minutes every day," the reminder section will send a reminder at the specified time every day. The reminder section can also monitor students' progress and send encouraging messages to help them achieve their goals. Furthermore, the reminder section can send notifications when the deadline for a task set by the student is approaching, encouraging them to complete the task. This makes it easier for students to achieve their goals and helps improve their academic performance.

[0075] The consultation system can further include a "feedback unit." The feedback unit has the function of collecting and analyzing feedback on the advice received by students. For example, after a student implements the advice, the feedback unit can input feedback on its effectiveness. The feedback unit can also make suggestions for improving the quality of the advice based on the collected feedback. Furthermore, the feedback unit can monitor the extent to which the student has implemented the advice and provide additional advice according to the degree of implementation. This maximizes the effectiveness of the advice and helps students solve their problems.

[0076] The counseling system can also be equipped with a "resource provider." This has the function of providing the learning and support resources that students need. For example, if a student is having trouble with a particular subject, it can suggest online learning materials or reference books related to that subject. The resource provider can also provide relaxation techniques and counseling resources if a student seeks advice about mental health. Furthermore, if a student is having trouble with friendships, the resource provider can provide resources to improve communication skills. This allows the system to provide students with the appropriate resources they need and help them solve their problems.

[0077] The counseling system can further include a "motivation improvement unit." This motivation improvement unit has the function of increasing students' academic motivation. For example, if a student loses motivation to study, it can present encouraging messages and success stories. The motivation improvement unit can also suggest steps for achieving the learning goals set by the student and provide positive feedback according to their progress. Furthermore, the motivation improvement unit can provide a system whereby students can receive rewards according to their learning progress. This increases students' academic motivation and improves their willingness to study.

[0078] The counseling system can also be equipped with a "community section." This section has the function of connecting students with the same concerns. For example, it can connect students who are struggling with academic failure and provide a place where they can encourage each other. The community section can also connect students who are struggling with mental health and provide a place to share relaxation techniques. Furthermore, the community section can connect students who are struggling with friendships and provide a place to hold discussions to improve communication skills. This allows students to support each other and help solve their problems.

[0079] The counseling system can further be equipped with an "emotion estimation unit." The emotion estimation unit has the function of analyzing the student's emotional state in real time and responding appropriately according to the emotion. For example, if a student is feeling stressed, it can suggest relaxation techniques. If a student is feeling anxious, the emotion estimation unit can also send a message of reassurance. Furthermore, if a student is feeling sad, the emotion estimation unit can suggest counseling resources. This allows for appropriate responses according to the student's emotional state and supports their mental health.

[0080] The consultation system can further be equipped with a "progress monitoring unit." This progress monitoring unit has the function of monitoring students' learning progress in real time and automatically adjusting their learning plans as necessary. For example, if their learning progress is falling behind, it will review their learning plan and suggest more efficient study methods. In addition, if their progress is falling behind in a specific subject, the progress monitoring unit can also suggest a learning plan that focuses on that subject. Furthermore, if their learning progress is going well, the progress monitoring unit can also suggest a learning plan for moving on to the next step. This allows the system to provide appropriate support according to students' learning progress and help improve their academic performance.

[0081] The counseling system can further include a "mental health plan generation unit." The mental health plan generation unit has the function of analyzing students' mental health conditions and automatically generating individual mental health plans based on that data. For example, it can provide plans that include stress management and relaxation techniques. The mental health plan generation unit can also automatically generate mental health plans recommended by psychological counseling experts. Furthermore, the mental health plan generation unit can automatically generate the optimal mental health plan by comparing it with past data. This allows the system to provide individual mental health plans based on students' mental health conditions and support their mental health.

[0082] The counseling system can further include a "hobby suggestion unit." The hobby suggestion unit has the function of suggesting hobbies and activities according to the student's mental health condition. For example, if a student is feeling stressed, it will suggest hobbies and activities that will help them relax. The hobby suggestion unit can also suggest creative activities such as art or music if the student's mental health condition is deteriorating. Furthermore, the hobby suggestion unit can also suggest outdoor activities such as nature walks or gardening if the student's mental health condition is unstable. This allows the system to suggest hobbies and activities according to the student's mental health condition and help them refresh their mind.

[0083] The counseling system can further include a "support system suggestion unit." The support system suggestion unit has the function of analyzing the student's mental health status and proposing support systems at school and at home. For example, it can suggest workshops or counseling sessions related to mental health. The support system suggestion unit can also emphasize the importance of communication at home and promote dialogue between parents and children. Furthermore, the support system suggestion unit can introduce mental health resources and support groups. This allows the system to suggest support systems at school and at home based on the student's mental health status and support mental health.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The consultation reception unit accepts the student's consultation content. For example, if a student inputs a concern such as "I'm not doing well in my studies," the consultation reception unit will accept the content. The consultation reception unit can also accept anonymous consultations. For example, by having students input their consultation content anonymously, they can receive consultation while protecting their privacy. Step 2: The analysis unit analyzes the consultation content received by the consultation reception unit. For example, the generation AI analyzes the consultation content using a text generation AI (e.g., LLM). The generation AI can also perform emotion analysis to understand the emotional state of the person seeking advice. The generation AI can also use data mining technology to compare the advice with past consultation data and generate optimal advice. For example, the generation AI can provide effective advice to students with similar problems based on past consultation data. Step 3: The advice provider provides appropriate advice based on the results of the analysis by the analyzer. For example, the generator AI suggests specific study plans and methods for students struggling with academic performance. The generator AI can also suggest relaxation techniques and counseling resources for students struggling with mental health issues. The generator AI can also automatically generate individual counseling plans based on the content of the consultation and provide them to students. For example, the generator AI automatically generates a counseling plan recommended by a psychological counseling expert based on the content of the consultation.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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]

[0153] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A consultation reception department that accepts student inquiries, an analysis unit that analyzes the consultation content received by the consultation reception unit; an advice providing unit that provides appropriate advice based on the results of the analysis by the analysis unit; A system characterized by:

2. The advice providing unit Suggest specific study plans or strategies to address poor academic performance 2. The system of claim 1.

3. The advice providing unit Suggest relaxation or counseling resources for said mental health 2. The system of claim 1.

4. The advice providing unit Automatically generate an individual counseling plan based on the consultation content 2. The system of claim 1.

5. The advice providing unit Analyze the content of the consultation and compare it with past consultation history to provide optimal advice 2. The system of claim 1.

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