System

Abstract

An object of the system according to the embodiment is to enable a teacher to learn an effective countermeasure against a helicopter parent and receive mental support.SOLUTION: A system according to an embodiment includes a scenario generation unit, a feedback provision unit, and an information storage unit. The scenario generation part generates a scenario for the teacher to perform role play to the helicopter parent. The feedback providing unit provides feedback after the role play is performed based on the scenario generated by the scenario generating unit. The information accumulation unit accumulates information on the feedback and the role play provided by the feedback providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, teachers had few opportunities to learn effective methods and procedures for dealing with 'monster parents', which placed a heavy mental burden on them.

[0005] The system according to the embodiment aims to enable teachers to learn effective ways to deal with monstrous parents and receive psychological support. [Means for solving the problem]

[0006] The system according to the embodiment includes a scenario generation unit, a feedback providing unit, and an information storage unit. The scenario generation unit generates a scenario for a teacher to role-play against a monstrous parent. The feedback providing unit provides feedback after the teacher has performed the role-play based on the scenario generated by the scenario generation unit. The information storage unit stores the feedback and role-play information provided by the feedback providing unit. [Effects of the Invention]

[0007] The system according to the embodiment allows teachers to learn effective ways to deal with monstrous parents and receive psychological support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The role-playing system according to an embodiment of the present invention allows teachers to role-play against tyrannical parents. This system utilizes a generative AI to generate scenarios, provide feedback, and accumulate role-play information. This allows teachers to prepare and be mentally prepared to deal with tyrannical parents in advance.

[0029] A role-playing system according to an embodiment includes a scenario generation unit, a feedback providing unit, and an information storage unit. The scenario generation unit generates a scenario for a teacher to role-play against a toxic parent. For example, the generation AI generates a specific scenario based on prompts including situations and problems that a teacher may face. The feedback providing unit provides feedback after a role-play based on the scenario generated by the scenario generation unit. For example, the generation AI provides specific advice on how a teacher should respond, such as, "It's important to be willing to appropriately listen to parents' opinions." The information storage unit accumulates the feedback and role-play information provided by the feedback providing unit. For example, the generation AI analyzes data from past role-plays to identify common problems and effective response methods. This allows the role-playing system according to an embodiment to help teachers prepare and mentally prepare for countermeasures against toxic parents. For example, teachers can prepare for actual responses through role-playing. They can also improve their own response methods based on the feedback. Furthermore, the accumulated information can help teachers learn more effective countermeasures.

[0030] The scenario generation unit can learn teachers' past response history and generate scenarios optimized for individual teachers. For example, the scenario generation unit uses a generation AI to learn teachers' past response history and generate scenarios optimized for individual teachers. For example, it can provide specific trouble scenarios to teachers who have had many problems with parents in the past. Furthermore, the generation AI generates scenarios optimized for individual teachers based on teachers' past response history. For example, it can provide scenarios that strengthen response methods for specific types of parents. Furthermore, the generation AI learns teachers' past response history and generates scenarios optimized for individual teachers. For example, it can provide success scenarios for similar situations based on past failure cases. This allows for more effective role-playing by providing optimized scenarios based on teachers' past response history.

[0031] The feedback providing unit can analyze the teacher's tone of voice and facial expression, and provide feedback to achieve more realistic role-playing. For example, the feedback providing unit allows the generation AI to analyze the teacher's tone of voice and facial expression, and provide feedback to achieve more realistic role-playing. For example, if the teacher's tone of voice is too high, the feedback providing unit recommends a calmer tone. The generation AI also analyzes the teacher's tone of voice and facial expression, and provides feedback to achieve more realistic role-playing. For example, if the teacher's facial expression is stiff, the feedback providing unit suggests ways to relax. The generation AI also analyzes the teacher's tone of voice and facial expression, and provides feedback to achieve more realistic role-playing. For example, if the teacher's tone of voice is too low, the feedback providing unit recommends a brighter tone. In this way, more realistic role-playing can be achieved by analyzing the teacher's tone of voice and facial expression.

[0032] The scenario generation unit can learn the characteristics of monstrous parents from different cultures and regions and generate international response scenarios. In the scenario generation unit, for example, the generation AI learns the characteristics of monstrous parents from different cultures and regions and generates international response scenarios. For example, it provides scenarios that reflect the differences in response methods between parents in Asia and Europe. In addition, the generation AI learns the characteristics of monstrous parents from different cultures and regions and generates international response scenarios. For example, it provides scenarios that take into account parents' expectations and demands based on their cultural background. In addition, the generation AI learns the characteristics of monstrous parents from different cultures and regions and generates international response scenarios. For example, it provides scenarios that reflect the differences in educational systems in each region. In this way, by learning the characteristics of monstrous parents from different cultures and regions and generating international response scenarios, teachers can respond to a variety of situations.

[0033] The scenario generation unit can translate the dialogue between teachers and parents in real time to realize multilingual role-playing. In the scenario generation unit, for example, the generation AI translates the dialogue between teachers and parents in real time to realize multilingual role-playing. For example, it translates from Japanese to English to simulate a dialogue with an English-speaking parent. In addition, the dialogue between teachers and parents in real time to realize multilingual role-playing. For example, it translates from French to Spanish to simulate a dialogue with a parent from a different language speaking background. In addition, the generation AI translates the dialogue between teachers and parents in real time to realize multilingual role-playing. For example, it translates from Chinese to German to simulate a dialogue with a parent from a different cultural background. In this way, the dialogue between teachers and parents can be translated in real time to realize multilingual role-playing, thereby simulating a dialogue with a parent from a different language speaking background.

[0034] The feedback providing unit can analyze the teacher's response in detail and list specific areas for improvement. In the feedback providing unit, for example, the generation AI analyzes the teacher's response in detail and lists specific areas for improvement. For example, it may suggest that the teacher take a more proactive stance in listening to parents' opinions. The generation AI may also analyze the teacher's response in detail and list specific areas for improvement. For example, it may recommend that the teacher provide clear and concise answers to parents' questions. The generation AI may also analyze the teacher's response in detail and list specific areas for improvement. For example, it may suggest that the teacher take a more sympathetic stance in understanding parents' feelings. In this way, by analyzing the teacher's response in detail and listing specific areas for improvement, the teacher's response skills can be improved.

[0035] The feedback providing unit can compare a teacher's response with that of other teachers and suggest best practices. In the feedback providing unit, for example, the generation AI compares a teacher's response with that of other teachers and suggests best practices. For example, it may suggest specific response methods based on successful cases. In addition, the generation AI compares a teacher's response with that of other teachers and suggests best practices. For example, it may introduce effective communication methods used by other teachers. In addition, the generation AI compares a teacher's response with that of other teachers and suggest best practices. For example, it may suggest effective phrases and expressions used by other teachers. In this way, by comparing a teacher's response with that of other teachers and suggesting best practices, it is possible to improve the teacher's response skills.

[0036] The feedback providing unit can visualize the feedback so that teachers can intuitively understand it. In the feedback providing unit, for example, the generation AI visualizes the feedback so that teachers can intuitively understand it. For example, it visually displays areas for improvement using graphs and charts. The feedback is also visualized so that the generation AI can intuitively understand it. For example, it color-codes areas where the response was good and areas where improvement is needed. The generation AI also visualizes the feedback so that teachers can intuitively understand it. For example, it shows the flow of the response in a flowchart and clearly shows which parts need improvement. In this way, visualizing the feedback makes it easier for teachers to understand intuitively.

[0037] The feedback providing unit provides feedback by voice, making it possible to accommodate teachers with visual impairments. For example, the generation AI provides feedback by voice, making it possible to accommodate teachers with visual impairments. For example, it explains areas for improvement by voice and provides specific advice. Feedback is also provided by voice, making it possible to accommodate teachers with visual impairments. For example, it explains in detail by voice what went well and what needs improvement. Feedback is also provided by voice, making it possible to accommodate teachers with visual impairments. For example, it explains the flow of the response by voice and clearly indicates which parts need improvement. In this way, by providing feedback by voice, it is possible to accommodate teachers with visual impairments.

[0038] The information accumulation unit can analyze the accumulated role-play information and automatically extract common problems. In the information accumulation unit, for example, the generation AI analyzes the accumulated role-play information and automatically extracts common problems. For example, it identifies points that parents are often dissatisfied with. The accumulated role-play information is also analyzed and the generation AI automatically extracts common problems. For example, it identifies mistakes in responding to specific scenarios. The generation AI also analyzes the accumulated role-play information and automatically extracts common problems. For example, it makes a list of problems that teachers frequently face. In this way, by analyzing the accumulated role-play information and automatically extracting common problems, problems faced by teachers can be effectively identified.

[0039] The information accumulation unit can present effective response methods in a ranking format based on past role-play data. In the information accumulation unit, for example, the generation AI presents effective response methods in a ranking format based on past role-play data. For example, the most successful response method is displayed at the top. The generation AI also presents effective response methods in a ranking format based on past role-play data. For example, it lists response methods with a high success rate. The generation AI also presents effective response methods in a ranking format based on past role-play data. For example, it displays response methods that are highly rated by teachers at the top. In this way, by presenting effective response methods in a ranking format based on past role-play data, teachers can learn the optimal response methods.

[0040] The information accumulation unit can share the accumulated information with other educational institutions and build a wide-ranging countermeasure cycle. For example, the information accumulation unit shares the information accumulated by the generation AI with other educational institutions and builds a wide-ranging countermeasure cycle. For example, common problems and effective response methods are shared. The accumulated information is also shared with other educational institutions and the generation AI builds a wide-ranging countermeasure cycle. For example, information exchange between educational institutions is promoted. The generation AI also shares the accumulated information with other educational institutions and builds a wide-ranging countermeasure cycle. For example, a joint training program is implemented. In this way, by sharing the accumulated information with other educational institutions and building a wide-ranging countermeasure cycle, effective countermeasures can be taken across the entire educational institution.

[0041] The information accumulation unit can automatically generate training programs for teachers based on the accumulated information. In the information accumulation unit, for example, a generation AI automatically generates training programs for teachers based on the accumulated information. For example, a program is provided for learning how to deal with specific problems. The generation AI also automatically generates training programs for teachers based on the accumulated information. For example, a program is provided for learning effective communication skills. The generation AI also automatically generates training programs for teachers based on the accumulated information. For example, a program is provided for learning stress management and relaxation techniques. In this way, by automatically generating training programs for teachers based on the accumulated information, teachers can learn effectively.

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

[0043] The role-playing system can further include an analysis unit that analyzes the teacher's tone of voice and facial expressions. The analysis unit can analyze the teacher's tone of voice and facial expressions and provide feedback to achieve more realistic role-playing. For example, if the teacher's tone of voice is too high, it can recommend a calmer tone. Also, if the teacher's facial expressions are stiff, it can suggest ways to relax. In this way, by analyzing the teacher's tone of voice and facial expressions, more realistic role-playing can be achieved.

[0044] The role-play system can further include a cultural learning module that learns the characteristics of toxic parents from different cultures and regions. The cultural learning module can learn the characteristics of toxic parents from different cultures and regions and generate international response scenarios. For example, it can provide scenarios that reflect the differences in response methods between Asian and Western parents. It can also provide scenarios that take into account parents' expectations and demands based on their cultural background. This allows teachers to respond to a variety of situations by learning the characteristics of toxic parents from different cultures and regions and generating international response scenarios.

[0045] The role-play system can further include a translation unit that translates the conversation between the teacher and the parent in real time. The translation unit can translate the conversation between the teacher and the parent in real time, enabling multilingual role-play. For example, it can translate from Japanese to English to simulate a conversation with an English-speaking parent. It can also translate from French to Spanish to simulate a conversation with a parent from a different language speaking area. In this way, the conversation between the teacher and the parent can be translated in real time, enabling multilingual role-play, enabling a conversation with a parent from a different language speaking area to be simulated.

[0046] The role-play system can further include an analysis unit that performs a detailed analysis of the teacher's response. The analysis unit can analyze the teacher's response in detail and list specific areas for improvement. For example, it can suggest that the teacher be more proactive in listening to parents' opinions. It can also recommend that the teacher provide clear and concise answers to parents' questions. In this way, the teacher's response skills can be improved by analyzing the teacher's response in detail and listing specific areas for improvement.

[0047] The role-play system can further include a comparison section that compares a teacher's response with that of other teachers. The comparison section can compare a teacher's response with that of other teachers and suggest best practices. For example, it can suggest specific response methods based on successful cases. It can also introduce effective communication methods used by other teachers. In this way, by comparing a teacher's response with that of other teachers and suggesting best practices, it is possible to improve the teacher's response skills.

[0048] The role-play system can further include a visualization unit that visualizes the feedback. The visualization unit can visualize the feedback to enable teachers to intuitively understand it. For example, it can visually display areas for improvement using graphs or charts. It can also color-code areas where the response was good and areas where improvement is needed. In this way, visualizing the feedback makes it easier for teachers to intuitively understand.

[0049] The role-play system can further include an audio providing unit that provides feedback by voice. The audio providing unit provides feedback by voice, making it possible to accommodate teachers with visual impairments. For example, it explains areas for improvement by voice and provides specific advice. It also provides detailed audio explanations of what went well and what needs improvement. In this way, by providing feedback by voice, it can accommodate teachers with visual impairments.

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

[0051] Step 1: The scenario generator generates scenarios for teachers to role-play against monstrous parents. For example, the AI ​​generates specific scenarios based on prompts containing situations and problems that teachers may face. Step 2: The feedback section provides feedback after role-playing based on the scenario generated by the scenario generation section. For example, the generation AI might provide specific advice on how teachers should respond, such as, "It's important to be willing to listen to parents' opinions appropriately." Step 3: The information storage unit stores the feedback and role-play information provided by the feedback providing unit. For example, the generation AI analyzes data from past role-plays to identify common problems and effective ways to address them.

[0052] (Example 2) The role-playing system according to an embodiment of the present invention allows teachers to role-play against tyrannical parents. This system utilizes a generative AI to generate scenarios, provide feedback, and accumulate role-play information. This allows teachers to prepare and be mentally prepared to deal with tyrannical parents in advance.

[0053] A role-playing system according to an embodiment includes a scenario generation unit, a feedback providing unit, and an information storage unit. The scenario generation unit generates a scenario for a teacher to role-play against a toxic parent. For example, the generation AI generates a specific scenario based on prompts including situations and problems that a teacher may face. The feedback providing unit provides feedback after a role-play based on the scenario generated by the scenario generation unit. For example, the generation AI provides specific advice on how a teacher should respond, such as, "It's important to be willing to appropriately listen to parents' opinions." The information storage unit accumulates the feedback and role-play information provided by the feedback providing unit. For example, the generation AI analyzes data from past role-plays to identify common problems and effective response methods. This allows the role-playing system according to an embodiment to help teachers prepare and mentally prepare for countermeasures against toxic parents. For example, teachers can prepare for actual responses through role-playing. They can also improve their own response methods based on the feedback. Furthermore, the accumulated information can help teachers learn more effective countermeasures.

[0054] The scenario generation unit can learn teachers' past response history and generate scenarios optimized for individual teachers. For example, the scenario generation unit uses a generation AI to learn teachers' past response history and generate scenarios optimized for individual teachers. For example, it can provide specific trouble scenarios to teachers who have had many problems with parents in the past. Furthermore, the generation AI generates scenarios optimized for individual teachers based on teachers' past response history. For example, it can provide scenarios that strengthen response methods for specific types of parents. Furthermore, the generation AI learns teachers' past response history and generates scenarios optimized for individual teachers. For example, it can provide success scenarios for similar situations based on past failure cases. This allows for more effective role-playing by providing optimized scenarios based on teachers' past response history.

[0055] The feedback providing unit can analyze the teacher's tone of voice and facial expression, and provide feedback to achieve more realistic role-playing. For example, the feedback providing unit allows the generation AI to analyze the teacher's tone of voice and facial expression, and provide feedback to achieve more realistic role-playing. For example, if the teacher's tone of voice is too high, the feedback providing unit recommends a calmer tone. The generation AI also analyzes the teacher's tone of voice and facial expression, and provides feedback to achieve more realistic role-playing. For example, if the teacher's facial expression is stiff, the feedback providing unit suggests ways to relax. The generation AI also analyzes the teacher's tone of voice and facial expression, and provides feedback to achieve more realistic role-playing. For example, if the teacher's tone of voice is too low, the feedback providing unit recommends a brighter tone. In this way, more realistic role-playing can be achieved by analyzing the teacher's tone of voice and facial expression.

[0056] The feedback providing unit can use the emotion estimation function to monitor the emotional state of the teacher in real time and interrupt or adjust the role-play at an appropriate time. The feedback providing unit, for example, uses the emotion estimation function to monitor the emotional state of the teacher in real time and interrupt or adjust the role-play at an appropriate time. For example, pausing the role-play if the teacher feels stressed. The feedback providing unit also monitors the emotional state of the teacher in real time and interrupts or adjusts the role-play at an appropriate time using the emotion estimation function. For example, setting aside time for the teacher to relax if the teacher feels anxious. The feedback providing unit also monitors the emotional state of the teacher in real time and interrupts or adjusts the role-play at an appropriate time. For example, providing advice on how to stay calm if the teacher feels angry. In this way, by monitoring the emotional state of the teacher in real time and interrupting or adjusting the role-play at an appropriate time, the teacher's stress can be reduced.

[0057] The scenario generation unit can learn the characteristics of monstrous parents from different cultures and regions and generate international response scenarios. In the scenario generation unit, for example, the generation AI learns the characteristics of monstrous parents from different cultures and regions and generates international response scenarios. For example, it provides scenarios that reflect the differences in response methods between parents in Asia and Europe. In addition, the generation AI learns the characteristics of monstrous parents from different cultures and regions and generates international response scenarios. For example, it provides scenarios that take into account parents' expectations and demands based on their cultural background. In addition, the generation AI learns the characteristics of monstrous parents from different cultures and regions and generates international response scenarios. For example, it provides scenarios that reflect the differences in educational systems in each region. In this way, by learning the characteristics of monstrous parents from different cultures and regions and generating international response scenarios, teachers can respond to a variety of situations.

[0058] The scenario generation unit can translate the dialogue between teachers and parents in real time to realize multilingual role-playing. In the scenario generation unit, for example, the generation AI translates the dialogue between teachers and parents in real time to realize multilingual role-playing. For example, it translates from Japanese to English to simulate a dialogue with an English-speaking parent. In addition, the dialogue between teachers and parents in real time to realize multilingual role-playing. For example, it translates from French to Spanish to simulate a dialogue with a parent from a different language speaking background. In addition, the generation AI translates the dialogue between teachers and parents in real time to realize multilingual role-playing. For example, it translates from Chinese to German to simulate a dialogue with a parent from a different cultural background. In this way, the dialogue between teachers and parents can be translated in real time to realize multilingual role-playing, thereby simulating a dialogue with a parent from a different language speaking background.

[0059] The feedback providing unit can use the emotion estimation function to measure the stress level felt by the teacher during role-playing and provide advice for stress reduction. For example, the feedback providing unit can use the emotion estimation function to measure the stress level felt by the teacher during role-playing and provide advice for stress reduction. For example, the feedback providing unit can suggest deep breathing or relaxation techniques. The emotion estimation function can also be used to measure the stress level felt by the teacher during role-playing and provide advice for stress reduction. For example, the emotion estimation function can ...

[0060] The feedback providing unit can analyze the teacher's response in detail and list specific areas for improvement. In the feedback providing unit, for example, the generation AI analyzes the teacher's response in detail and lists specific areas for improvement. For example, it may suggest that the teacher take a more proactive stance in listening to parents' opinions. The generation AI may also analyze the teacher's response in detail and list specific areas for improvement. For example, it may recommend that the teacher provide clear and concise answers to parents' questions. The generation AI may also analyze the teacher's response in detail and list specific areas for improvement. For example, it may suggest that the teacher take a more sympathetic stance in understanding parents' feelings. In this way, by analyzing the teacher's response in detail and listing specific areas for improvement, the teacher's response skills can be improved.

[0061] The feedback providing unit can compare a teacher's response with that of other teachers and suggest best practices. In the feedback providing unit, for example, the generation AI compares a teacher's response with that of other teachers and suggests best practices. For example, it may suggest specific response methods based on successful cases. In addition, the generation AI compares a teacher's response with that of other teachers and suggests best practices. For example, it may introduce effective communication methods used by other teachers. In addition, the generation AI compares a teacher's response with that of other teachers and suggest best practices. For example, it may suggest effective phrases and expressions used by other teachers. In this way, by comparing a teacher's response with that of other teachers and suggesting best practices, it is possible to improve the teacher's response skills.

[0062] The feedback providing unit can use the emotion estimation function to analyze the emotional response of the teacher and provide specific advice for maintaining emotional stability. The feedback providing unit, for example, uses the emotion estimation function to analyze the emotional response of the teacher and provide specific advice for maintaining emotional stability. For example, it may suggest taking deep breaths when feeling stressed. The emotion estimation function may also be used to analyze the emotional response of the teacher and provide specific advice for maintaining emotional stability. For example, it may suggest simple exercises for relaxation. The emotion estimation function may also be used to analyze the emotional response of the teacher and provide specific advice for maintaining emotional stability. For example, it may suggest affirmations to encourage positive thinking. In this way, by analyzing the emotional response of the teacher and providing specific advice for maintaining emotional stability, it is possible to maintain the mental stability of the teacher.

[0063] The feedback providing unit can visualize the feedback so that teachers can intuitively understand it. In the feedback providing unit, for example, the generation AI visualizes the feedback so that teachers can intuitively understand it. For example, it visually displays areas for improvement using graphs and charts. The feedback is also visualized so that the generation AI can intuitively understand it. For example, it color-codes areas where the response was good and areas where improvement is needed. The generation AI also visualizes the feedback so that teachers can intuitively understand it. For example, it shows the flow of the response in a flowchart and clearly shows which parts need improvement. In this way, visualizing the feedback makes it easier for teachers to understand intuitively.

[0064] The feedback providing unit provides feedback by voice, making it possible to accommodate teachers with visual impairments. For example, the generation AI provides feedback by voice, making it possible to accommodate teachers with visual impairments. For example, it explains areas for improvement by voice and provides specific advice. Feedback is also provided by voice, making it possible to accommodate teachers with visual impairments. For example, it explains in detail by voice what went well and what needs improvement. Feedback is also provided by voice, making it possible to accommodate teachers with visual impairments. For example, it explains the flow of the response by voice and clearly indicates which parts need improvement. In this way, by providing feedback by voice, it is possible to accommodate teachers with visual impairments.

[0065] The feedback providing unit can use the emotion estimation function to evaluate the emotional impact that the content of the feedback has on the teacher and prioritize positive feedback. For example, the feedback providing unit uses the emotion estimation function to evaluate the emotional impact that the content of the feedback has on the teacher and prioritizes positive feedback. For example, positive words are used when pointing out areas for improvement. The emotion estimation function also evaluates the emotional impact that the content of the feedback has on the teacher and prioritizes positive feedback. For example, success stories are emphasized to increase the teacher's confidence. The emotion estimation function also evaluates the emotional impact that the content of the feedback has on the teacher and prioritizes positive feedback. For example, good points are praised before pointing out areas for improvement. In this way, the emotional impact that the content of the feedback has on the teacher and priority is given to positive feedback can be evaluated, thereby improving the motivation of the teacher.

[0066] The information accumulation unit can analyze the accumulated role-play information and automatically extract common problems. In the information accumulation unit, for example, the generation AI analyzes the accumulated role-play information and automatically extracts common problems. For example, it identifies points that parents are often dissatisfied with. The accumulated role-play information is also analyzed and the generation AI automatically extracts common problems. For example, it identifies mistakes in responding to specific scenarios. The generation AI also analyzes the accumulated role-play information and automatically extracts common problems. For example, it makes a list of problems that teachers frequently face. In this way, by analyzing the accumulated role-play information and automatically extracting common problems, problems faced by teachers can be effectively identified.

[0067] The information accumulation unit can present effective response methods in a ranking format based on past role-play data. In the information accumulation unit, for example, the generation AI presents effective response methods in a ranking format based on past role-play data. For example, the most successful response method is displayed at the top. The generation AI also presents effective response methods in a ranking format based on past role-play data. For example, it lists response methods with a high success rate. The generation AI also presents effective response methods in a ranking format based on past role-play data. For example, it displays response methods that are highly rated by teachers at the top. In this way, by presenting effective response methods in a ranking format based on past role-play data, teachers can learn the optimal response methods.

[0068] The information accumulation unit can use the emotion estimation function to analyze the emotional reactions of the teacher from past role-play data and identify an emotionally stable way of responding. The information accumulation unit, for example, uses the emotion estimation function to analyze the emotional reactions of the teacher from past role-play data and identify an emotionally stable way of responding. For example, it identifies a way of responding that is less stressful. Furthermore, it analyzes the emotional reactions of the teacher from past role-play data using the emotion estimation function and identifies an emotionally stable way of responding. For example, it identifies a way that allows the teacher to respond in a relaxed manner. Furthermore, it uses the emotion estimation function to analyze the emotional reactions of the teacher from past role-play data and identifies an emotionally stable way of responding. For example, it identifies a way that allows the teacher to respond with confidence. In this way, by analyzing the emotional reactions of the teacher from past role-play data and identifying an emotionally stable way of responding, the teacher can learn ways to respond without feeling stressed.

[0069] The information accumulation unit can share the accumulated information with other educational institutions and build a wide-ranging countermeasure cycle. For example, the information accumulation unit shares the information accumulated by the generation AI with other educational institutions and builds a wide-ranging countermeasure cycle. For example, common problems and effective response methods are shared. The accumulated information is also shared with other educational institutions and the generation AI builds a wide-ranging countermeasure cycle. For example, information exchange between educational institutions is promoted. The generation AI also shares the accumulated information with other educational institutions and builds a wide-ranging countermeasure cycle. For example, a joint training program is implemented. In this way, by sharing the accumulated information with other educational institutions and building a wide-ranging countermeasure cycle, effective countermeasures can be taken across the entire educational institution.

[0070] The information accumulation unit can automatically generate training programs for teachers based on the accumulated information. In the information accumulation unit, for example, a generation AI automatically generates training programs for teachers based on the accumulated information. For example, a program is provided for learning how to deal with specific problems. The generation AI also automatically generates training programs for teachers based on the accumulated information. For example, a program is provided for learning effective communication skills. The generation AI also automatically generates training programs for teachers based on the accumulated information. For example, a program is provided for learning stress management and relaxation techniques. In this way, by automatically generating training programs for teachers based on the accumulated information, teachers can learn effectively.

[0071] The information accumulation unit can analyze the emotional tendencies of teachers from the accumulated information using the emotion estimation function and propose new measures to strengthen emotional support. For example, the information accumulation unit can analyze the emotional tendencies of teachers from the accumulated information using the emotion estimation function and propose new measures to strengthen emotional support. For example, it can propose a new relaxation method to reduce stress. It can also analyze the emotional tendencies of teachers from the accumulated information using the emotion estimation function and propose new measures to strengthen emotional support. For example, it can propose a new activity to elicit positive emotions. It can also analyze the emotional tendencies of teachers from the accumulated information using the emotion estimation function and propose new measures to strengthen emotional support. For example, it can propose a new mental health program to maintain emotional stability. In this way, it is possible to analyze the emotional tendencies of teachers from the accumulated information and propose new measures to strengthen emotional support, thereby strengthening mental support for teachers.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The role-playing system can further include an analysis unit that analyzes the teacher's tone of voice and facial expressions. The analysis unit can analyze the teacher's tone of voice and facial expressions and provide feedback to achieve more realistic role-playing. For example, if the teacher's tone of voice is too high, it can recommend a calmer tone. Also, if the teacher's facial expressions are stiff, it can suggest ways to relax. In this way, by analyzing the teacher's tone of voice and facial expressions, more realistic role-playing can be achieved.

[0074] The role-play system can further include an emotion monitoring unit that monitors the emotional state of the teacher in real time. The emotion monitoring unit can monitor the emotional state of the teacher in real time and interrupt or adjust the role-play at an appropriate time. For example, it can pause the role-play if the teacher feels stressed, or set aside time for the teacher to relax if the teacher feels anxious. In this way, the teacher's stress can be reduced by monitoring the emotional state of the teacher in real time and interrupting or adjusting the role-play at an appropriate time.

[0075] The role-play system can further include a cultural learning module that learns the characteristics of toxic parents from different cultures and regions. The cultural learning module can learn the characteristics of toxic parents from different cultures and regions and generate international response scenarios. For example, it can provide scenarios that reflect the differences in response methods between Asian and Western parents. It can also provide scenarios that take into account parents' expectations and demands based on their cultural background. This allows teachers to respond to a variety of situations by learning the characteristics of toxic parents from different cultures and regions and generating international response scenarios.

[0076] The role-play system can further include a translation unit that translates the conversation between the teacher and the parent in real time. The translation unit can translate the conversation between the teacher and the parent in real time, enabling multilingual role-play. For example, it can translate from Japanese to English to simulate a conversation with an English-speaking parent. It can also translate from French to Spanish to simulate a conversation with a parent from a different language speaking area. In this way, the conversation between the teacher and the parent can be translated in real time, enabling multilingual role-play, enabling a conversation with a parent from a different language speaking area to be simulated.

[0077] The role-play system can further include a stress measurement unit that measures the stress level felt by the teacher during role-play. The stress measurement unit can measure the stress level felt by the teacher during role-play and provide advice for stress reduction. For example, it can suggest deep breathing or relaxation techniques, or recommend taking a short break. In this way, by measuring the stress level felt by the teacher during role-play and providing advice for stress reduction, it is possible to reduce the mental burden on the teacher.

[0078] The role-play system can further include an analysis unit that performs a detailed analysis of the teacher's response. The analysis unit can analyze the teacher's response in detail and list specific areas for improvement. For example, it can suggest that the teacher be more proactive in listening to parents' opinions. It can also recommend that the teacher provide clear and concise answers to parents' questions. In this way, the teacher's response skills can be improved by analyzing the teacher's response in detail and listing specific areas for improvement.

[0079] The role-play system can further include a comparison section that compares a teacher's response with that of other teachers. The comparison section can compare a teacher's response with that of other teachers and suggest best practices. For example, it can suggest specific response methods based on successful cases. It can also introduce effective communication methods used by other teachers. In this way, by comparing a teacher's response with that of other teachers and suggesting best practices, it is possible to improve the teacher's response skills.

[0080] The role-play system can further include an emotion analysis unit that analyzes the emotional reactions of the teacher. The emotion analysis unit can analyze the emotional reactions of the teacher and provide specific advice to maintain emotional stability. For example, it can suggest taking deep breaths when feeling stressed, or suggest simple exercises to relax. In this way, the emotional reactions of the teacher can be analyzed and specific advice to maintain emotional stability can be provided, thereby maintaining the mental stability of the teacher.

[0081] The role-play system can further include a visualization unit that visualizes the feedback. The visualization unit can visualize the feedback to enable teachers to intuitively understand it. For example, it can visually display areas for improvement using graphs or charts. It can also color-code areas where the response was good and areas where improvement is needed. In this way, visualizing the feedback makes it easier for teachers to intuitively understand.

[0082] The role-play system can further include an audio providing unit that provides feedback by voice. The audio providing unit provides feedback by voice, making it possible to accommodate teachers with visual impairments. For example, it explains areas for improvement by voice and provides specific advice. It also provides detailed audio explanations of what went well and what needs improvement. In this way, by providing feedback by voice, it can accommodate teachers with visual impairments.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The scenario generator generates scenarios for teachers to role-play against monstrous parents. For example, the AI ​​generates specific scenarios based on prompts containing situations and problems that teachers may face. Step 2: The feedback section provides feedback after role-playing based on the scenario generated by the scenario generation section. For example, the generation AI might provide specific advice on how teachers should respond, such as, "It's important to be willing to listen to parents' opinions appropriately." Step 3: The information storage unit stores the feedback and role-play information provided by the feedback providing unit. For example, the generation AI analyzes data from past role-plays to identify common problems and effective ways to address them.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] 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.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a scenario generation unit that generates a scenario for a teacher to role-play against a monstrous parent; a feedback providing unit that provides feedback after a role play is performed based on the scenario generated by the scenario generating unit; an information storage unit that stores the feedback provided by the feedback providing unit and information on role-playing; A system characterized by:

2. The scenario generation unit The past response history of the teacher is learned, and the scenario optimized for each teacher is generated.

2. The system of claim 1.

3. The feedback providing unit: Analyze the teacher's tone of voice and facial expressions to provide feedback to make the role-play more realistic 2. The system of claim 1.

4. The feedback providing unit: Monitor the teacher's emotional state in real time and interrupt or adjust the role-play at the appropriate time.

2. The system of claim 1.

5. The scenario generation unit Learn the characteristics of such monstrous parents in different cultures and regions and generate international response scenarios 2. The system of claim 1.

6. The scenario generation unit The dialogue between the teacher and the parent is translated in real time, enabling the role-play to be performed in multiple languages.

2. The system of claim 1.

7. The feedback providing unit: Measuring the stress level felt by the teacher during the role-play and providing advice to reduce stress 2. The system of claim 1.

8. The feedback providing unit: Analyze the teacher's response in detail and list specific areas for improvement.

2. The system of claim 1.

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