system

Abstract

The system according to this embodiment aims to propose class organization that takes into account interpersonal relationships and mental stress among students. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects students' mental health data. The analysis unit analyzes the data collected by the collection unit to understand the interpersonal relationships among students and their state of mental stress. The proposal unit proposes class assignments based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, class formation considering the interpersonal relationships and mental stress among students has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to propose class formation considering the interpersonal relationships and mental stress among students.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects mental health data of students. The analysis unit analyzes the data collected by the collection unit to grasp the state of interpersonal relationships and mental stress among students. The proposal unit proposes class formation based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose class organization that takes into account interpersonal relationships and mental stress among students. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The class placement system according to an embodiment of the present invention is a system that utilizes deep learning to optimize interpersonal relationships among students, minimizing conflict and maximizing learning effectiveness. This class placement system collects students' mental health data and proposes class placements that take mental stress into account. For example, the class placement system collects data such as the results of questionnaires and counseling sessions, and daily behavioral data. Next, it analyzes the collected data using deep learning to understand the state of interpersonal relationships and mental stress among students. For example, it analyzes whether specific students get along well, are in conflict, or are experiencing stress. Then, based on the analysis results, it proposes the optimal class placement. For example, it minimizes conflict by placing students who get along well in the same class and students who are in conflict in different classes. It also takes into consideration that students experiencing mental stress should be placed with classmates who need support. This system makes it possible to find combinations that humans alone might overlook, realizing class placements that maximize learning effectiveness. For example, by using deep learning, it is possible to learn optimal class placement patterns from past data and apply them to new student data. As a result, the class placement system can optimize interpersonal relationships among students, minimize conflict, and realize class placements that maximize learning effectiveness.

[0029] The class organization system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects students' mental health data. This data includes, but is not limited to, survey results, counseling records, and daily behavioral data. For example, the data collection unit conducts surveys to collect information on students' mental health. The data collection unit can also record the results of counseling sessions and collect them as mental health data. Furthermore, the data collection unit can collect students' daily behavioral data and use it as mental health data. For example, the data collection unit records students' attendance, behavior during class, and how they spend their breaks and collects this data as mental health data. The analysis unit analyzes the data collected by the data collection unit to understand the interpersonal relationships and mental stress levels among students. The analysis unit analyzes the data using, for example, data mining techniques, statistical analysis, and machine learning algorithms. For example, based on the collected data, the analysis unit can determine whether specific students get along well, are in conflict, or are experiencing stress. The analysis unit calculates, for example, friendship scores, conflict scores, and stress scores to evaluate the interpersonal relationships and mental stress levels among students. The proposal unit proposes class assignments based on the analysis results obtained by the analysis unit. For example, the proposal unit may place students who get along well in the same class and students who are in conflict with each other in different classes. For example, based on the analysis results, the proposal unit may also place students experiencing mental stress with classmates who need support. The proposal unit can learn optimal class assignment patterns from past data and apply them to new student data. As a result, the class assignment system according to this embodiment can optimize interpersonal relationships among students, minimize conflict, and achieve class assignments that maximize learning effectiveness.

[0030] The data collection unit can collect daily behavioral data from the results of questionnaires or counseling sessions. For example, the data collection unit can conduct questionnaires to collect information on students' mental health. Questionnaires may include, but are not limited to, questions and answer formats. The data collection unit may, for example, record the results of counseling sessions and collect them as mental health data. Counseling may include, but are not limited to, interview formats and question content. The data collection unit may, for example, collect data on students' daily behavior and use it as mental health data. Daily behavioral data may include, but are not limited to, attendance, behavior during class, and how students spend their free time. This allows for more accurate analysis by collecting mental health data from diverse data sources. Some or all of the above-described processes in the data collection unit may be performed using, for example, deep learning, or not. For example, the data collection unit can input questionnaire results into a deep learning model and have the deep learning model collect mental health data.

[0031] The analysis unit can analyze the collected data to determine whether specific students get along well, are in conflict, or are experiencing stress. The analysis unit analyzes the data using, for example, data mining techniques, statistical analysis, and machine learning algorithms. For example, based on the collected data, the analysis unit can determine whether specific students get along well, are in conflict, or are experiencing stress. The analysis unit can calculate, for example, friendship scores, conflict scores, and stress scores to evaluate the interpersonal relationships and mental stress levels of the students. For example, the analysis unit can calculate friendship scores to evaluate the level of friendship between students. It can also calculate conflict scores to evaluate the level of conflict between students. Furthermore, it can calculate stress scores to evaluate the mental stress levels of the students. This allows for an accurate understanding of the interpersonal relationships and mental stress levels of the students. Some or all of the above-described processes in the analysis unit may be performed using, for example, deep learning, or without deep learning. For example, the analysis unit can input the collected data into a deep learning model and have the deep learning model perform the analysis of the interpersonal relationships and mental stress levels of the students.

[0032] The proposal unit can, based on the analysis results, place students who get along well in the same class and students who are in conflict with each other in different classes. For example, the proposal unit can, based on the analysis results, place students who get along well in the same class and students who are in conflict with each other in different classes. The proposal unit can, for example, propose class assignments based on friendship scores and conflict scores. For example, the proposal unit can, for example, place students with high friendship scores in the same class and students with high conflict scores in different classes. The proposal unit can also, based on the analysis results, assign classmates who need support to students experiencing mental stress. For example, the proposal unit can, based on stress scores, assign classmates who can provide appropriate support to students experiencing mental stress. This minimizes conflict among students and achieves optimal class assignments. Some or all of the above processing in the proposal unit may be performed using, for example, deep learning, or not. For example, the proposal unit can input the analysis results into a deep learning model and have the deep learning model execute the class assignment proposal.

[0033] The proposal unit can assign classmates who need support to students experiencing mental stress. For example, the proposal unit assigns classmates who need support to students experiencing mental stress based on analysis results. For example, the proposal unit assigns classmates who can provide appropriate support to students experiencing mental stress based on stress scores. For example, the proposal unit can select classmates who need support based on counseling results or teacher evaluations. For example, the proposal unit assigns classmates who can provide appropriate support to students experiencing mental stress based on counseling results. The proposal unit can also assign classmates who can provide appropriate support to students experiencing mental stress based on teacher evaluations. This makes it possible to provide appropriate support to students experiencing mental stress. Some or all of the above processing in the proposal unit may be performed using deep learning, for example, or without deep learning. For example, the proposal unit can input analysis results into a deep learning model and have the deep learning model perform the assignment of classmates who need support.

[0034] The proposed system can learn class composition patterns from past data and apply them to new student data. For example, the proposed system can learn class composition patterns from past data and apply them to new student data. For example, the proposed system can use a machine learning algorithm to learn past class composition data and find the optimal class composition pattern. For example, the proposed system can learn the optimal class composition pattern based on past class composition data and apply it to new student data. For example, the proposed system can learn class composition patterns that take into account factors such as the balance of academic ability, compatibility of personalities, and the degree of agreement of interests, based on past class composition data. This makes it possible to achieve more accurate class composition by utilizing past data. Some or all of the above processing in the proposed system may be performed using deep learning, for example, or without deep learning. For example, the proposed system can input past class composition data into a deep learning model and have the deep learning model learn class composition patterns.

[0035] The data collection unit can analyze students' past mental health data and select the optimal data collection method. For example, the data collection unit can analyze past survey results and select the most effective question format. For example, the data collection unit can select the optimal counseling method based on past counseling data. Furthermore, the data collection unit can analyze past behavioral data and select the most reliable data collection method. This allows for the selection of more effective data collection methods by utilizing past data. Some or all of the above processing in the data collection unit may be performed using deep learning, for example, or without deep learning. For example, the data collection unit can input past mental health data into a deep learning model and have the deep learning model select the optimal data collection method.

[0036] The data collection unit can filter data based on the student's current living situation and areas of interest during data collection. For example, the data collection unit can select appropriate questionnaire items based on the student's living situation. For example, the data collection unit can adjust the content of counseling based on the student's areas of interest. Furthermore, the data collection unit can adjust the timing of behavioral data collection based on the student's daily rhythm. This enables data collection tailored to the individual circumstances of each student. Some or all of the above-described processes in the data collection unit may be performed using deep learning, for example, or without deep learning. For example, the data collection unit can input data on the student's living situation and areas of interest into a deep learning model and have the deep learning model perform the data collection filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the student's geographical location information during data collection. For example, if the student is at school, the data collection unit can prioritize the collection of learning-related data. If the student is at home, the data collection unit can prioritize the collection of data related to the home environment. Furthermore, if the student is in a public place, the data collection unit can prioritize the collection of data related to social behavior. This allows for the collection of appropriate data based on the student's location information. Some or all of the above processing in the data collection unit may be performed using, for example, deep learning, or without deep learning. For example, the data collection unit can input the student's geographical location information into a deep learning model and have the deep learning model perform the collection of highly relevant data.

[0038] The data collection unit can analyze students' social media activities and collect relevant data during data collection. For example, the data collection unit can analyze students' social media posts and collect data on their mental health. For example, the data collection unit can analyze students' social media friendships and collect data on their relationships. Furthermore, the data collection unit can analyze students' social media activity time and collect data on their daily routines. This allows for the collection of data on students' mental health and relationships through their social media activities. Some or all of the above processing in the data collection unit may be performed using deep learning, for example, or without deep learning. For example, the data collection unit can input students' social media data into a deep learning model and have the deep learning model perform the collection of relevant data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. Furthermore, for example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. This allows for appropriate analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using deep learning, for example, or without deep learning. For example, the analysis unit can input the importance of the data into a deep learning model and have the deep learning model perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an emotion analysis algorithm to mental health data. For example, the analysis unit can apply a behavioral pattern analysis algorithm to behavioral data. Furthermore, the analysis unit can apply a network analysis algorithm to relationship data. This allows for appropriate analysis according to the data category. Some or all of the above processing in the analysis unit may be performed using deep learning, for example, or without deep learning. For example, the analysis unit can input the data category into a deep learning model and have the deep learning model execute the application of different analysis algorithms.

[0041] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may prioritize the analysis of current data while referring to past data. Furthermore, the analysis unit may prioritize the analysis of data collected during a specific period. This allows for appropriate analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using deep learning, for example, or without deep learning. For example, the analysis unit may input the data collection period into a deep learning model and have the deep learning model determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This allows analysis to be performed in an appropriate order based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using deep learning, for example, or without deep learning. For example, the analysis unit can input the relevance of the data into a deep learning model and have the deep learning model perform the adjustment of the analysis order.

[0043] The proposal unit can adjust the level of detail of its proposals based on the importance of the class organization. For example, the proposal unit can provide detailed proposals for highly important class organization, simplified proposals for less important class organization, and proposals with an appropriate level of detail for moderately important class organization. This allows for appropriate proposals according to the importance of the class organization. Some or all of the above processing in the proposal unit may be performed using deep learning, for example, or without deep learning. For example, the proposal unit can input the importance of the class organization into a deep learning model and have the deep learning model adjust the level of detail of the proposals.

[0044] The proposal unit can apply different proposal algorithms depending on the class composition category during the proposal process. For example, the proposal unit can apply an emotion analysis algorithm to a class composition that takes mental health into consideration. For example, the proposal unit can apply a learning pattern analysis algorithm to a class composition that emphasizes learning effectiveness. Furthermore, the proposal unit can apply a network analysis algorithm to a class composition that emphasizes interpersonal relationships. This allows for appropriate proposals to be made according to the class composition category. Some or all of the above processing in the proposal unit may be performed using deep learning, for example, or without deep learning. For example, the proposal unit can input the class composition category into a deep learning model and have the deep learning model execute the application of different proposal algorithms.

[0045] The proposal unit can determine the priority of proposals based on past class formation data when making a proposal. For example, the proposal unit can prioritize the most suitable proposal based on past class formation data. For example, the proposal unit can learn from past data and make the proposal that best suits the current situation. Furthermore, the proposal unit can make proposals that prioritize new data while referring to past data. This allows for more appropriate proposals to be made by utilizing past data. Some or all of the above processing in the proposal unit may be performed using deep learning, for example, or without deep learning. For example, the proposal unit can input past class formation data into a deep learning model and have the deep learning model perform the determination of proposal priorities.

[0046] The proposal unit can adjust the order of proposals based on the relevance of the class organization during the proposal process. For example, the proposal unit may prioritize proposing highly relevant class organization configurations. For example, the proposal unit may postpone proposing less relevant class organization configurations. Furthermore, the proposal unit can dynamically adjust the order of proposals based on the relevance of the class organization configurations. This allows proposals to be made in an appropriate order based on the relevance of the class organization configurations. Some or all of the above processing in the proposal unit may be performed using deep learning, for example, or without deep learning. For example, the proposal unit can input the relevance of the class organization configurations into a deep learning model and have the deep learning model perform the adjustment of the proposal order.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The class placement system can also propose class assignments that take into account students' learning styles. For example, the data collection unit collects data on students' learning styles, and the analysis unit analyzes this data to determine whether students are visual, auditory, or experiential learners. Based on the analysis results, the proposal unit can place students with similar learning styles in the same class. This results in class placement that maximizes students' learning effectiveness. Furthermore, by placing students with different learning styles in a balanced manner, it is possible to provide a learning environment where students complement each other.

[0049] The class placement system can also propose class assignments that take students' interests into account. For example, the data collection unit collects data on students' interests, and the analysis unit analyzes this data to understand what subjects and activities students are interested in. Based on the analysis results, the proposal unit can place students with similar interests in the same class. This results in class placements that enhance student motivation. Furthermore, by placing students with different interests in a balanced manner, it is possible to provide a learning environment with diverse perspectives.

[0050] The class placement system can also propose class assignments that take into account students' home environments. For example, the data collection unit collects data on students' home environments, and the analysis unit analyzes this data to understand the kind of home environment in which students grew up. Based on the analysis results, the proposal unit can place students with similar home environments in the same class. This results in class placements that students can easily relate to. Furthermore, by placing students with different home environments in a balanced manner, it is possible to provide a learning environment with diverse values.

[0051] The class placement system can also propose class assignments that take into account students' future goals. For example, the data collection unit collects data on students' future goals, and the analysis unit analyzes this data to understand what kind of occupations and career paths students are aiming for. Based on the analysis results, the proposal unit can place students with similar goals in the same class. This creates a class structure where students encourage each other and work towards their goals. Furthermore, by balancing students with different goals, it is possible to provide a learning environment with diverse career paths.

[0052] The class placement system can also propose class placements that take into account students' academic abilities. For example, the data collection unit collects data on students' academic abilities, and the analysis unit analyzes this data to understand the level of academic ability each student possesses. Based on the analysis results, the proposal unit can place students with similar academic abilities in the same class. This creates a class placement where students compete with each other and improve their academic performance. Furthermore, by placing students with different academic abilities in a balanced manner, it is possible to provide a learning environment where students teach each other.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The data collection department collects student mental health data. This data includes survey results, counseling records, and daily behavioral data. For example, the data collection department can conduct surveys to collect information about students' mental health. They can also record counseling results and collect them as mental health data. Furthermore, they can collect data on students' daily behavior (attendance, behavior in class, how they spend their breaks, etc.) and use it as mental health data. Step 2: The analysis unit analyzes the data collected by the data collection unit to understand the interpersonal relationships and mental stress levels among students. The analysis unit uses data mining techniques, statistical analysis, and machine learning algorithms to analyze the data. For example, based on the collected data, the analysis unit determines whether specific students are getting along well, are in conflict, or are experiencing stress. It calculates friendship scores, conflict scores, and stress scores to evaluate the interpersonal relationships and mental stress levels among students. Step 3: The proposal unit proposes class assignments based on the analysis results obtained by the analysis unit. The proposal unit places students who get along well in the same class and students who are at odds with each other in different classes. It can also place students experiencing emotional stress with classmates who need support. The proposal unit can learn optimal class assignment patterns from past data and apply them to new student data.

[0055] (Example of form 2) The class placement system according to an embodiment of the present invention is a system that utilizes deep learning to optimize interpersonal relationships among students, minimizing conflict and maximizing learning effectiveness. This class placement system collects students' mental health data and proposes class placements that take mental stress into account. For example, the class placement system collects data such as the results of questionnaires and counseling sessions, and daily behavioral data. Next, it analyzes the collected data using deep learning to understand the state of interpersonal relationships and mental stress among students. For example, it analyzes whether specific students get along well, are in conflict, or are experiencing stress. Then, based on the analysis results, it proposes the optimal class placement. For example, it minimizes conflict by placing students who get along well in the same class and students who are in conflict in different classes. It also takes into consideration that students experiencing mental stress should be placed with classmates who need support. This system makes it possible to find combinations that humans alone might overlook, realizing class placements that maximize learning effectiveness. For example, by using deep learning, it is possible to learn optimal class placement patterns from past data and apply them to new student data. As a result, the class placement system can optimize interpersonal relationships among students, minimize conflict, and realize class placements that maximize learning effectiveness.

[0056] The class organization system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects students' mental health data. This data includes, but is not limited to, survey results, counseling records, and daily behavioral data. For example, the data collection unit conducts surveys to collect information on students' mental health. The data collection unit can also record the results of counseling sessions and collect them as mental health data. Furthermore, the data collection unit can collect students' daily behavioral data and use it as mental health data. For example, the data collection unit records students' attendance, behavior during class, and how they spend their breaks and collects this data as mental health data. The analysis unit analyzes the data collected by the data collection unit to understand the interpersonal relationships and mental stress levels among students. The analysis unit analyzes the data using, for example, data mining techniques, statistical analysis, and machine learning algorithms. For example, based on the collected data, the analysis unit can determine whether specific students get along well, are in conflict, or are experiencing stress. The analysis unit calculates, for example, friendship scores, conflict scores, and stress scores to evaluate the interpersonal relationships and mental stress levels among students. The proposal unit proposes class assignments based on the analysis results obtained by the analysis unit. For example, the proposal unit may place students who get along well in the same class and students who are in conflict with each other in different classes. For example, based on the analysis results, the proposal unit may also place students experiencing mental stress with classmates who need support. The proposal unit can learn optimal class assignment patterns from past data and apply them to new student data. As a result, the class assignment system according to this embodiment can optimize interpersonal relationships among students, minimize conflict, and achieve class assignments that maximize learning effectiveness.

[0057] The data collection unit can collect daily behavioral data from the results of questionnaires or counseling sessions. For example, the data collection unit can conduct questionnaires to collect information on students' mental health. Questionnaires may include, but are not limited to, questions and answer formats. The data collection unit may, for example, record the results of counseling sessions and collect them as mental health data. Counseling may include, but are not limited to, interview formats and question content. The data collection unit may, for example, collect data on students' daily behavior and use it as mental health data. Daily behavioral data may include, but are not limited to, attendance, behavior during class, and how students spend their free time. This allows for more accurate analysis by collecting mental health data from diverse data sources. Some or all of the above-described processes in the data collection unit may be performed using, for example, deep learning, or not. For example, the data collection unit can input questionnaire results into a deep learning model and have the deep learning model collect mental health data.

[0058] The analysis unit can analyze the collected data to determine whether specific students get along well, are in conflict, or are experiencing stress. The analysis unit analyzes the data using, for example, data mining techniques, statistical analysis, and machine learning algorithms. For example, based on the collected data, the analysis unit can determine whether specific students get along well, are in conflict, or are experiencing stress. The analysis unit can calculate, for example, friendship scores, conflict scores, and stress scores to evaluate the interpersonal relationships and mental stress levels of the students. For example, the analysis unit can calculate friendship scores to evaluate the level of friendship between students. It can also calculate conflict scores to evaluate the level of conflict between students. Furthermore, it can calculate stress scores to evaluate the mental stress levels of the students. This allows for an accurate understanding of the interpersonal relationships and mental stress levels of the students. Some or all of the above-described processes in the analysis unit may be performed using, for example, deep learning, or without deep learning. For example, the analysis unit can input the collected data into a deep learning model and have the deep learning model perform the analysis of the interpersonal relationships and mental stress levels of the students.

[0059] The proposal unit can, based on the analysis results, place students who get along well in the same class and students who are in conflict with each other in different classes. For example, the proposal unit can, based on the analysis results, place students who get along well in the same class and students who are in conflict with each other in different classes. The proposal unit can, for example, propose class assignments based on friendship scores and conflict scores. For example, the proposal unit can, for example, place students with high friendship scores in the same class and students with high conflict scores in different classes. The proposal unit can also, based on the analysis results, assign classmates who need support to students experiencing mental stress. For example, the proposal unit can, based on stress scores, assign classmates who can provide appropriate support to students experiencing mental stress. This minimizes conflict among students and achieves optimal class assignments. Some or all of the above processing in the proposal unit may be performed using, for example, deep learning, or not. For example, the proposal unit can input the analysis results into a deep learning model and have the deep learning model execute the class assignment proposal.

[0060] The proposal unit can assign classmates who need support to students experiencing mental stress. For example, the proposal unit assigns classmates who need support to students experiencing mental stress based on analysis results. For example, the proposal unit assigns classmates who can provide appropriate support to students experiencing mental stress based on stress scores. For example, the proposal unit can select classmates who need support based on counseling results or teacher evaluations. For example, the proposal unit assigns classmates who can provide appropriate support to students experiencing mental stress based on counseling results. The proposal unit can also assign classmates who can provide appropriate support to students experiencing mental stress based on teacher evaluations. This makes it possible to provide appropriate support to students experiencing mental stress. Some or all of the above processing in the proposal unit may be performed using deep learning, for example, or without deep learning. For example, the proposal unit can input analysis results into a deep learning model and have the deep learning model perform the assignment of classmates who need support.

[0061] The proposed system can learn class composition patterns from past data and apply them to new student data. For example, the proposed system can learn class composition patterns from past data and apply them to new student data. For example, the proposed system can use a machine learning algorithm to learn past class composition data and find the optimal class composition pattern. For example, the proposed system can learn the optimal class composition pattern based on past class composition data and apply it to new student data. For example, the proposed system can learn class composition patterns that take into account factors such as the balance of academic ability, compatibility of personalities, and the degree of agreement of interests, based on past class composition data. This makes it possible to achieve more accurate class composition by utilizing past data. Some or all of the above processing in the proposed system may be performed using deep learning, for example, or without deep learning. For example, the proposed system can input past class composition data into a deep learning model and have the deep learning model learn class composition patterns.

[0062] The data collection unit can estimate students' emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can conduct questionnaires during times when students are relaxed to collect accurate mental health data. For example, if a student is stressed, the data collection unit can adjust the timing of counseling to collect appropriate data. The data collection unit can also collect behavioral data after classes when students are concentrating to understand their daily behavioral patterns. This allows for data collection at the appropriate time according to the students' emotions. Some or all of the above processing in the data collection unit may be performed using deep learning, for example, or without deep learning. For example, the data collection unit can input student emotion data into a deep learning model and have the deep learning model adjust the timing of data collection.

[0063] The data collection unit can analyze students' past mental health data and select the optimal data collection method. For example, the data collection unit can analyze past survey results and select the most effective question format. For example, the data collection unit can select the optimal counseling method based on past counseling data. Furthermore, the data collection unit can analyze past behavioral data and select the most reliable data collection method. This allows for the selection of more effective data collection methods by utilizing past data. Some or all of the above processing in the data collection unit may be performed using deep learning, for example, or without deep learning. For example, the data collection unit can input past mental health data into a deep learning model and have the deep learning model select the optimal data collection method.

[0064] The data collection unit can filter data based on the student's current living situation and areas of interest during data collection. For example, the data collection unit can select appropriate questionnaire items based on the student's living situation. For example, the data collection unit can adjust the content of counseling based on the student's areas of interest. Furthermore, the data collection unit can adjust the timing of behavioral data collection based on the student's daily rhythm. This enables data collection tailored to the individual circumstances of each student. Some or all of the above-described processes in the data collection unit may be performed using deep learning, for example, or without deep learning. For example, the data collection unit can input data on the student's living situation and areas of interest into a deep learning model and have the deep learning model perform the data collection filtering.

[0065] The data collection unit can estimate students' emotions and determine the priority of data to collect based on the estimated emotions. For example, if a student is stressed, the data collection unit may prioritize collecting data related to mental health. For example, if a student is relaxed, the data collection unit may prioritize collecting data related to learning. Also, if a student is excited, the data collection unit may prioritize collecting behavioral data. This allows for the priority collection of important data according to the student's emotions. Some or all of the above processing in the data collection unit may be performed using deep learning, for example, or without deep learning. For example, the data collection unit can input student emotion data into a deep learning model and have the deep learning model perform the data prioritization.

[0066] The data collection unit can prioritize the collection of highly relevant data by considering the student's geographical location information during data collection. For example, if the student is at school, the data collection unit can prioritize the collection of learning-related data. If the student is at home, the data collection unit can prioritize the collection of data related to the home environment. Furthermore, if the student is in a public place, the data collection unit can prioritize the collection of data related to social behavior. This allows for the collection of appropriate data based on the student's location information. Some or all of the above processing in the data collection unit may be performed using, for example, deep learning, or without deep learning. For example, the data collection unit can input the student's geographical location information into a deep learning model and have the deep learning model perform the collection of highly relevant data.

[0067] The data collection unit can analyze students' social media activities and collect relevant data during data collection. For example, the data collection unit can analyze students' social media posts and collect data on their mental health. For example, the data collection unit can analyze students' social media friendships and collect data on their relationships. Furthermore, the data collection unit can analyze students' social media activity time and collect data on their daily routines. This allows for the collection of data on students' mental health and relationships through their social media activities. Some or all of the above processing in the data collection unit may be performed using deep learning, for example, or without deep learning. For example, the data collection unit can input students' social media data into a deep learning model and have the deep learning model perform the collection of relevant data.

[0068] The analysis unit can estimate the student's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the student is stressed, the analysis unit can provide a simple, visual analysis result. For example, if the student is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the student is excited, the analysis unit can provide a visually stimulating analysis result. This allows for the provision of appropriate analysis results according to the student's emotions. Some or all of the above processing in the analysis unit may be performed using, for example, deep learning, or without deep learning. For example, the analysis unit can input student emotion data into a deep learning model and have the deep learning model adjust the presentation of the analysis.

[0069] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. Furthermore, for example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. This allows for appropriate analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using deep learning, for example, or without deep learning. For example, the analysis unit can input the importance of the data into a deep learning model and have the deep learning model perform the adjustment of the level of detail of the analysis.

[0070] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an emotion analysis algorithm to mental health data. For example, the analysis unit can apply a behavioral pattern analysis algorithm to behavioral data. Furthermore, the analysis unit can apply a network analysis algorithm to relationship data. This allows for appropriate analysis according to the data category. Some or all of the above processing in the analysis unit may be performed using deep learning, for example, or without deep learning. For example, the analysis unit can input the data category into a deep learning model and have the deep learning model execute the application of different analysis algorithms.

[0071] The analysis unit can estimate the student's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the student is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the student is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the student is excited, the analysis unit can provide a visually stimulating analysis result. This allows for the provision of analysis results of appropriate length according to the student's emotions. Some or all of the above processing in the analysis unit may be performed using, for example, deep learning, or without deep learning. For example, the analysis unit can input student emotion data into a deep learning model and have the deep learning model perform the adjustment of the analysis length.

[0072] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may prioritize the analysis of current data while referring to past data. Furthermore, the analysis unit may prioritize the analysis of data collected during a specific period. This allows for appropriate analysis based on the data collection period. Some or all of the above-described processes in the analysis unit may be performed using deep learning, for example, or without deep learning. For example, the analysis unit may input the data collection period into a deep learning model and have the deep learning model determine the analysis priority.

[0073] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This allows analysis to be performed in an appropriate order based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using deep learning, for example, or without deep learning. For example, the analysis unit can input the relevance of the data into a deep learning model and have the deep learning model perform the adjustment of the analysis order.

[0074] The suggestion unit can estimate the student's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, if the student is stressed, the suggestion unit can make simple, visual suggestions. If the student is relaxed, for example, the suggestion unit can make detailed suggestions. Also, if the student is excited, for example, the suggestion unit can make visually stimulating suggestions. This allows the suggestion unit to make appropriate suggestions according to the student's emotions. Some or all of the processing described above in the suggestion unit may be performed using deep learning, for example, or without deep learning. For example, the suggestion unit can input student emotion data into a deep learning model and have the deep learning model adjust the way the suggestion is presented.

[0075] The proposal unit can adjust the level of detail of its proposals based on the importance of the class organization. For example, the proposal unit can provide detailed proposals for highly important class organization, simplified proposals for less important class organization, and proposals with an appropriate level of detail for moderately important class organization. This allows for appropriate proposals according to the importance of the class organization. Some or all of the above processing in the proposal unit may be performed using deep learning, for example, or without deep learning. For example, the proposal unit can input the importance of the class organization into a deep learning model and have the deep learning model adjust the level of detail of the proposals.

[0076] The proposal unit can apply different proposal algorithms depending on the class composition category during the proposal process. For example, the proposal unit can apply an emotion analysis algorithm to a class composition that takes mental health into consideration. For example, the proposal unit can apply a learning pattern analysis algorithm to a class composition that emphasizes learning effectiveness. Furthermore, the proposal unit can apply a network analysis algorithm to a class composition that emphasizes interpersonal relationships. This allows for appropriate proposals to be made according to the class composition category. Some or all of the above processing in the proposal unit may be performed using deep learning, for example, or without deep learning. For example, the proposal unit can input the class composition category into a deep learning model and have the deep learning model execute the application of different proposal algorithms.

[0077] The suggestion unit can estimate the student's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the student is in a hurry, the suggestion unit can make a short, concise suggestion. If the student is relaxed, for example, the suggestion unit can make a detailed suggestion. Also, if the student is excited, for example, the suggestion unit can make a visually stimulating suggestion. This allows the suggestion unit to make suggestions of appropriate length according to the student's emotions. Some or all of the above processing in the suggestion unit may be performed using deep learning, for example, or without deep learning. For example, the suggestion unit can input student emotion data into a deep learning model and have the deep learning model adjust the length of the suggestion.

[0078] The proposal unit can determine the priority of proposals based on past class formation data when making a proposal. For example, the proposal unit can prioritize the most suitable proposal based on past class formation data. For example, the proposal unit can learn from past data and make the proposal that best suits the current situation. Furthermore, the proposal unit can make proposals that prioritize new data while referring to past data. This allows for more appropriate proposals to be made by utilizing past data. Some or all of the above processing in the proposal unit may be performed using deep learning, for example, or without deep learning. For example, the proposal unit can input past class formation data into a deep learning model and have the deep learning model perform the determination of proposal priorities.

[0079] The proposal unit can adjust the order of proposals based on the relevance of the class organization during the proposal process. For example, the proposal unit may prioritize proposing highly relevant class organization configurations. For example, the proposal unit may postpone proposing less relevant class organization configurations. Furthermore, the proposal unit can dynamically adjust the order of proposals based on the relevance of the class organization configurations. This allows proposals to be made in an appropriate order based on the relevance of the class organization configurations. Some or all of the above processing in the proposal unit may be performed using deep learning, for example, or without deep learning. For example, the proposal unit can input the relevance of the class organization configurations into a deep learning model and have the deep learning model perform the adjustment of the proposal order. === Hard Collateral 1-1 === Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects student behavior data using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A collects questionnaire results and counseling records. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using deep learning to understand the interpersonal relationships and mental stress levels of the students. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes the optimal class composition based on the analysis results. The proposal unit may be implemented, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects student behavior data using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A collects questionnaire results and counseling records. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using deep learning to understand the interpersonal relationships and mental stress levels of the students. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and proposes the optimal class composition based on the analysis results. The proposal unit may be implemented, for example, in the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects student behavior data using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A collects questionnaire results and counseling records. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using deep learning to understand the interpersonal relationships and mental stress levels of the students. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes the optimal class composition based on the analysis results. The proposal unit may be implemented in the control unit 46A of the headset terminal 314, for example. === Hard Collateral 1-4 === Each of the multiple elements described above, including the data collection unit, analysis unit, and proposal unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects student behavior data using the camera 42 and microphone 238 of the robot 414, and the control unit 46A collects questionnaire results and counseling records. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data using deep learning to understand the interpersonal relationships and mental stress levels of the students. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes the optimal class composition based on the analysis results. The proposal unit may be implemented, for example, by the control unit 46A of the robot 414.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] The class placement system can also propose class assignments that take into account students' learning styles. For example, the data collection unit collects data on students' learning styles, and the analysis unit analyzes this data to determine whether students are visual, auditory, or experiential learners. Based on the analysis results, the proposal unit can place students with similar learning styles in the same class. This results in class placement that maximizes students' learning effectiveness. Furthermore, by placing students with different learning styles in a balanced manner, it is possible to provide a learning environment where students complement each other.

[0082] The class placement system can also propose class assignments that take students' interests into account. For example, the data collection unit collects data on students' interests, and the analysis unit analyzes this data to understand what subjects and activities students are interested in. Based on the analysis results, the proposal unit can place students with similar interests in the same class. This results in class placements that enhance student motivation. Furthermore, by placing students with different interests in a balanced manner, it is possible to provide a learning environment with diverse perspectives.

[0083] The class placement system can also propose class assignments that take into account students' home environments. For example, the data collection unit collects data on students' home environments, and the analysis unit analyzes this data to understand the kind of home environment in which students grew up. Based on the analysis results, the proposal unit can place students with similar home environments in the same class. This results in class placements that students can easily relate to. Furthermore, by placing students with different home environments in a balanced manner, it is possible to provide a learning environment with diverse values.

[0084] The class placement system can also propose class assignments that take into account students' future goals. For example, the data collection unit collects data on students' future goals, and the analysis unit analyzes this data to understand what kind of occupations and career paths students are aiming for. Based on the analysis results, the proposal unit can place students with similar goals in the same class. This creates a class structure where students encourage each other and work towards their goals. Furthermore, by balancing students with different goals, it is possible to provide a learning environment with diverse career paths.

[0085] The class placement system can also propose class placements that take into account students' academic abilities. For example, the data collection unit collects data on students' academic abilities, and the analysis unit analyzes this data to understand the level of academic ability each student possesses. Based on the analysis results, the proposal unit can place students with similar academic abilities in the same class. This creates a class placement where students compete with each other and improve their academic performance. Furthermore, by placing students with different academic abilities in a balanced manner, it is possible to provide a learning environment where students teach each other.

[0086] The class placement system can further estimate students' emotions and propose class placements based on those estimates. For example, the data collection unit collects data on students' emotions, and the analysis unit analyzes this data to understand the students' emotional state. Based on the analysis results, the proposal unit can place students with stable emotional states in the same class. This results in class placements that create a stable atmosphere within the classroom. In addition, students with unstable emotional states can receive emotional support by being placed with classmates who need it.

[0087] The class assignment system can further estimate students' emotions and propose role assignments within the class based on those estimates. For example, the data collection unit collects data on students' emotions, and the analysis unit analyzes this data to understand the students' emotional states. Based on the analysis results, the proposal unit can assign students who can demonstrate leadership to class leaders and students who can provide support to support roles. This clarifies the roles within the class and enables efficient class management. In addition, students with unstable emotional states can receive emotional support by being assigned to support roles.

[0088] The class organization system can further estimate students' emotions and suggest communication methods within the class based on those estimates. For example, the data collection unit collects data on students' emotions, and the analysis unit analyzes this data to understand the students' emotional state. Based on the analysis results, the suggestion unit can encourage proactive communication for students with stable emotional states and recommend calmer communication for students with unstable emotional states. This facilitates smoother communication within the class and fosters positive interpersonal relationships. Additionally, emotional support can be provided to students with unstable emotional states by assigning them a support role.

[0089] The class organization system can further estimate students' emotions and propose class activities based on those estimates. For example, the data collection unit collects data on students' emotions, and the analysis unit analyzes this data to understand the students' emotional state. Based on the analysis results, the proposal unit can suggest challenging activities for students with stable emotional states and relaxing activities for students with unstable emotional states. This ensures that appropriate activities are provided according to the students' emotional states, leading to a better classroom atmosphere. In addition, emotional support can be provided to students with unstable emotional states by assigning them a support person.

[0090] The class placement system can further estimate students' emotions and propose seating arrangements within the class based on those estimates. For example, the data collection unit collects data on students' emotions, and the analysis unit analyzes this data to understand the students' emotional state. Based on the analysis results, the proposal unit can place students with stable emotional states at the front and students with unstable emotional states at the back. This optimizes the seating arrangement within the class according to emotional states. In addition, emotional support can be provided to students with unstable emotional states by assigning them a support person.

[0091] The following briefly describes the processing flow for example form 2.

[0092] Step 1: The data collection department collects student mental health data. This data includes survey results, counseling records, and daily behavioral data. For example, the data collection department can conduct surveys to collect information about students' mental health. They can also record counseling results and collect them as mental health data. Furthermore, they can collect data on students' daily behavior (attendance, behavior in class, how they spend their breaks, etc.) and use it as mental health data. Step 2: The analysis unit analyzes the data collected by the data collection unit to understand the interpersonal relationships and mental stress levels among students. The analysis unit uses data mining techniques, statistical analysis, and machine learning algorithms to analyze the data. For example, based on the collected data, the analysis unit determines whether specific students are getting along well, are in conflict, or are experiencing stress. It calculates friendship scores, conflict scores, and stress scores to evaluate the interpersonal relationships and mental stress levels among students. Step 3: The proposal unit proposes class assignments based on the analysis results obtained by the analysis unit. The proposal unit places students who get along well in the same class and students who are at odds with each other in different classes. It can also place students experiencing emotional stress with classmates who need support. The proposal unit can learn optimal class assignment patterns from past data and apply them to new student data.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0095] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0097] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0098] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0103] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0104] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0105] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0106] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0107] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0113] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0114] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0130] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0137] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0146] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0148] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0149] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0150] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0154] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0155] 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.

[0156] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0157] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0158] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0159] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0161] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0162] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0163] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects student mental health data, The data collected by the aforementioned collection unit is analyzed by an analysis unit to understand the interpersonal relationships and mental stress levels among students, A proposal unit proposes a class organization based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is We collect daily behavioral data based on the results of questionnaires or counseling sessions. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to determine whether specific students get along well, whether they are in conflict, and whether they are experiencing stress. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, students who get along well will be placed in the same class, and students who are at odds with each other will be placed in different classes. The system according to feature 1.

5. The aforementioned proposal section is, For students experiencing mental stress, we will assign them to classmates who also need support. The system according to feature 1.

6. The aforementioned proposal section is, Learn class composition patterns from past data and apply them to new student data. The system according to feature 1.

7. The aforementioned collection unit is We estimate students' emotions and adjust the timing of data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze students' past mental health data and select the optimal data collection method. The system according to feature 1.

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