system

Abstract

The system according to this embodiment aims to provide real-time feedback and individual guidance to users when they operate in a VR environment. [Solution] The system according to the embodiment comprises a feedback unit, a scenario provision unit, and a guidance unit. The feedback unit provides real-time feedback when the user operates in the VR environment. The scenario provision unit provides training scenarios that meet the user's needs. The guidance unit analyzes user behavior and provides individual guidance.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, real-time feedback and individual guidance for operations in a VR environment are not sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide real-time feedback and individual guidance when a user performs an operation in a VR environment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a feedback unit, a scenario provision unit, and a training unit. The feedback unit provides real-time feedback when the user operates in the VR environment. The scenario provision unit provides training scenarios tailored to the user's needs. The training unit analyzes user behavior and provides individualized instruction. [Effects of the Invention]

[0007] The system according to this embodiment can provide real-time feedback and individual guidance to users when they operate in a VR environment. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that provides a platform in which users can acquire skills without risk by simulating realistic environments in fields such as medicine, construction, and safety training through VR technology. The AI ​​agent system provides real-time feedback when the user operates in the VR environment. Next, the AI ​​agent system allows for diverse scenario settings and provides training scenarios tailored to the user's needs. Furthermore, the AI ​​agent system analyzes user behavior and provides individualized guidance. This allows users to develop the ability to respond in real-world situations. For example, in the medical field, the AI ​​agent system can simulate surgery, allowing doctors to practice surgical procedures. In the construction field, it can simulate construction sites, allowing workers to learn safe work procedures. In safety training, it can simulate emergencies such as fires and earthquakes to develop response capabilities. The AI ​​agent system provides a platform in which users can acquire skills without risk through real-time feedback, diverse scenario settings, user behavior analysis, and individualized guidance. As a result, the AI ​​agent system allows users to receive real-time feedback in the VR environment, be provided with training scenarios tailored to their needs, and receive individualized guidance.

[0029] The AI ​​agent system according to this embodiment comprises a feedback unit, a scenario provision unit, and a training unit. The feedback unit provides real-time feedback when the user operates in a VR environment. For example, the feedback unit reacts immediately to the user's operations and provides appropriate advice and corrections. The feedback unit can also evaluate the accuracy and efficiency of the user's operations and point out areas for improvement. For example, when the user performs a surgical simulation, the feedback unit evaluates the accuracy of the surgical procedure and provides real-time feedback. In a construction site simulation, the feedback unit can also evaluate the safety of the work procedure and provide real-time feedback. In safety training, the feedback unit can also evaluate the ability to respond to emergencies and provide real-time feedback. The scenario provision unit provides training scenarios tailored to the user's needs. For example, the scenario provision unit selects an appropriate scenario according to the user's skill level and goals. The scenario provision unit can also refer to the user's training history and provide scenarios according to their progress. For example, in the medical field, the scenario provision unit can provide surgical simulation scenarios, allowing doctors to practice surgical procedures. The Scenario Provision Department provides simulation scenarios for construction sites in the construction sector, enabling workers to learn safe work procedures. In safety training, the Scenario Provision Department provides scenarios simulating emergencies such as fires and earthquakes, fostering response capabilities. The Instruction Department analyzes user behavior and provides individualized instruction. For example, the Instruction Department analyzes user operation history and provides guidance on specific areas for improvement. The Instruction Department can also provide appropriate instruction according to the user's skill level and goals. For example, in the medical field, the Instruction Department provides specific instruction on surgical procedures to support the skill development of doctors. In the construction sector, the Instruction Department provides specific instruction on safe work procedures to improve workers' safety awareness. In safety training, the Instruction Department provides specific instruction on emergency response capabilities to improve users' response skills.As a result, the AI ​​agent system according to this embodiment allows users to receive real-time feedback in a VR environment, be provided with training scenarios tailored to their needs, and receive individualized instruction.

[0030] The feedback unit provides real-time feedback when users operate in a VR environment. Specifically, when a user operates using a VR headset or controller, that operation data is immediately sent to the feedback unit. The feedback unit uses AI algorithms to analyze the user's operations and provides appropriate advice and corrections. For example, in a surgical simulation, if the user's hand movements holding the scalpel are inaccurate, the feedback unit will detect the movement and display correction instructions on the screen. It can also communicate specific areas for improvement to the user through voice feedback. In a construction site simulation, it evaluates the safety of the user's operation of heavy machinery and immediately issues a warning if dangerous operations occur. Furthermore, in safety training, it evaluates the user's response to emergencies such as fires and earthquakes and provides real-time feedback if the response is inappropriate. The feedback unit can compare the accuracy and efficiency of the user's operations with past operation data and benchmark data. This allows users to understand how accurate and efficient their operations are and clearly identify areas for improvement. The feedback unit can also accumulate the user's operation history and build a database to support long-term skill improvement. This allows users to refer to past feedback and perform self-assessments. The feedback unit can provide not only immediate responses to user actions but also feedback to support long-term skill improvement.

[0031] The scenario provider offers training scenarios tailored to user needs. Specifically, it selects appropriate scenarios based on the user's skill level and goals, and executes them in a VR environment. The scenario provider can refer to the user's training history and provide scenarios that match their progress. For example, in the medical field, it offers a variety of scenarios, from basic surgical procedures for beginners to complex procedures for advanced users. Once a user has mastered a particular surgical procedure, it provides a more difficult scenario as the next step. In the construction field, it offers scenarios that allow users to learn step-by-step, from basic work procedures to applied work procedures used on actual construction sites. In safety training, it provides scenarios that simulate emergencies such as fires and earthquakes, allowing users to develop their ability to respond to emergencies. The scenario provider can use AI to analyze user training data and automatically select the most suitable scenario for each individual user. This allows users to efficiently train according to their skill level and goals. The scenario provider can improve the content of scenarios based on user feedback to provide more effective training. For example, if a user feels that a particular scenario is too difficult, the difficulty level of the scenario can be adjusted based on that feedback. This allows the scenario provider to offer flexible training scenarios tailored to user needs, thereby supporting users in improving their skills.

[0032] The training department analyzes user behavior and provides individualized instruction. Specifically, it analyzes the user's operation history in detail and provides guidance on specific areas for improvement. The training department uses AI to analyze user operation data and evaluate the accuracy and efficiency of operations. For example, in the medical field, it provides specific instruction on surgical procedures to support the improvement of doctors' skills. In surgical simulations, if a user makes a mistake in a particular procedure, it points out the error and shows the correct procedure. It also analyzes what kinds of mistakes users are likely to make during surgery and provides specific advice to prevent them. In the construction field, it provides specific instruction on safe work procedures to improve workers' safety awareness. For example, when operating heavy machinery, if a user does not meet safety standards, it points out this and shows safe operating methods. In safety training, it provides specific instruction on emergency response capabilities to improve users' response capabilities. For example, in fire simulations, if a user fails to select an appropriate evacuation route, it points this out and shows the correct evacuation route. The training department can provide appropriate instruction according to the user's skill level and goals. For example, it carefully instructs beginners on basic operation methods and provides more advanced techniques and knowledge to advanced users. The training department can improve its instruction based on user feedback, thereby providing more effective instruction. This allows the training department to support users' skill development and maximize the effectiveness of the training.

[0033] The feedback unit can provide optimal feedback by referring to the user's past operation history when providing feedback. For example, the feedback unit can provide specific areas for improvement for operations where the user has made mistakes in the past. The feedback unit can also highlight operations where the user has made successes in the past and provide positive feedback. The feedback unit can also analyze the user's operation history and provide focused feedback on areas where mistakes are frequently made. In this way, optimal feedback can be provided by referring to the user's past operation history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's operation history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0034] The feedback unit can adjust the level of detail in the feedback based on the frequency and type of user errors. For example, if a user makes frequent errors, the feedback unit can provide detailed feedback and specific suggestions for improvement. If a user makes rare errors, the feedback unit can provide concise feedback and points to note. The feedback unit can also customize the content of the feedback and provide appropriate advice depending on the type of user error. This allows for the provision of appropriate feedback by adjusting the level of detail based on the frequency and type of user errors. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user error data into a generating AI and have the generating AI adjust the level of detail in the feedback.

[0035] The feedback unit can provide highly relevant feedback by considering the user's geographical location information. For example, if the user is operating in a specific area, the feedback unit can provide information relevant to that area. If the user is on the move, the feedback unit can also provide information relevant to their destination. If the user is operating within a specific facility, the feedback unit can also provide information relevant to that facility. This allows the feedback unit to provide highly relevant feedback by considering the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information data into a generating AI and have the generating AI perform the task of providing highly relevant feedback.

[0036] The feedback unit can analyze the user's social media activity and provide relevant feedback when providing feedback. For example, the feedback unit can provide relevant feedback based on information the user has shared on social media. The feedback unit can also provide feedback related to accounts the user follows on social media. The feedback unit can also analyze the user's social media activity and provide feedback based on their interests. This allows the feedback unit to provide relevant feedback by analyzing the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant feedback.

[0037] The scenario provider can provide the optimal scenario by referring to the user's past training history when providing a scenario. For example, the scenario provider can provide the optimal scenario based on the training content the user has previously performed. The scenario provider can also analyze the user's training history and provide a scenario that matches their progress. The scenario provider can also provide a scenario that includes areas for improvement, based on the user's past training results. In this way, the optimal scenario can be provided by referring to the user's past training history. Some or all of the above processing in the scenario provider may be performed using AI, for example, or without using AI. For example, the scenario provider can input the user's training history data into a generating AI and have the generating AI execute the provision of the optimal scenario.

[0038] The scenario provider can provide different scenarios depending on the user's skill level. For example, the scenario provider can provide a scenario including basic operations to a beginner user. The scenario provider can also provide a scenario including advanced operations to an intermediate user. The scenario provider can also provide a scenario including complex operations to an advanced user. This allows for appropriate training by providing different scenarios according to the user's skill level. Some or all of the above processing in the scenario provider can be performed using AI, for example, or without AI. For example, the scenario provider can input user skill level data into a generating AI and have the generating AI perform the task of providing different scenarios.

[0039] The scenario provider can provide highly relevant scenarios by considering the user's geographical location information when providing scenarios. For example, if the user is performing an operation in a specific region, the scenario provider can provide a scenario related to that region. If the user is on the move, the scenario provider can also provide a scenario related to the user's destination. If the user is performing an operation within a specific facility, the scenario provider can also provide a scenario related to that facility. In this way, highly relevant scenarios can be provided by considering the user's geographical location information. Some or all of the above processing in the scenario provider may be performed using AI, for example, or without using AI. For example, the scenario provider can input the user's geographical location information data into a generating AI and have the generating AI perform the task of providing highly relevant scenarios.

[0040] The scenario provider can analyze a user's social media activity and provide relevant scenarios when providing them. For example, the scenario provider can provide relevant scenarios based on information shared by the user on social media. The scenario provider can also provide scenarios related to accounts followed by the user on social media. The scenario provider can also analyze a user's social media activity and provide scenarios based on their interests. In this way, relevant scenarios can be provided by analyzing a user's social media activity. Some or all of the above processing in the scenario provider may be performed using AI, for example, or without AI. For example, the scenario provider can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant scenarios.

[0041] The instruction department can provide optimal guidance by referring to the user's past behavior history during instruction. For example, the instruction department can provide specific areas for improvement in operations the user has performed in the past. The instruction department can also provide positive guidance by highlighting operations the user has successfully performed in the past. The instruction department can also analyze the user's behavior history and provide focused guidance on areas where mistakes are frequently made. In this way, optimal guidance can be provided by referring to the user's past behavior history. Some or all of the above processes in the instruction department may be performed using AI, for example, or not using AI. For example, the instruction department can input user behavior history data into a generating AI and have the generating AI perform the task of providing optimal guidance.

[0042] The instruction unit can apply different teaching methods depending on the user's skill level during instruction. For example, the instruction unit may provide instruction including basic operations to beginner users. The instruction unit may also provide instruction including advanced operations to intermediate users. The instruction unit may also provide instruction including complex operations to advanced users. In this way, appropriate instruction can be provided by applying different teaching methods according to the user's skill level. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not using AI. For example, the instruction unit can input user skill level data into a generating AI and have the generating AI execute the application of different teaching methods.

[0043] The instruction unit can provide highly relevant instruction by considering the user's geographical location during instruction. For example, if the user is operating in a specific region, the instruction unit can provide instruction relevant to that region. If the user is on the move, the instruction unit can also provide instruction relevant to the user's destination. If the user is operating within a specific facility, the instruction unit can also provide instruction relevant to that facility. In this way, highly relevant instruction can be provided by considering the user's geographical location. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant instruction.

[0044] The guidance department can analyze the user's social media activity and provide relevant guidance during the guidance process. For example, the guidance department can provide relevant guidance based on information the user has shared on social media. The guidance department can also provide guidance related to accounts the user follows on social media. The guidance department can also analyze the user's social media activity and provide guidance based on their interests. This allows the guidance department to provide relevant guidance by analyzing the user's social media activity. Some or all of the above processes in the guidance department may be performed using AI, for example, or not using AI. For example, the guidance department can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant guidance.

[0045] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0046] The feedback unit can analyze the user's operation speed and provide appropriate feedback. For example, if the user is in a hurry, the feedback unit will provide concise feedback to avoid disrupting the flow of operation. Conversely, if the user is operating slowly, the feedback unit will provide detailed feedback to deepen their understanding of the operation. The feedback unit can also adjust the timing of feedback according to the user's operation speed. This allows for the provision of optimal feedback tailored to the user's operating speed.

[0047] The feedback unit can evaluate the accuracy of the user's actions and provide appropriate feedback. For example, if the user performs an action correctly, the feedback unit will provide positive feedback to increase the user's motivation. Conversely, if the user makes an error, the feedback unit will provide feedback indicating specific areas for improvement. Furthermore, the feedback unit can customize the content of the feedback according to the accuracy of the user's actions. This allows for the provision of optimal feedback tailored to the accuracy of the user's actions.

[0048] The feedback unit can evaluate the efficiency of the user's operations and provide appropriate feedback. For example, if the user operates efficiently, the feedback unit will provide positive feedback to encourage the user to operate efficiently in the future. Conversely, if the user operates inefficiently, the feedback unit will provide feedback indicating specific areas for improvement. Furthermore, the feedback unit can customize the content of the feedback according to the efficiency of the user's operations. This allows for the provision of optimal feedback tailored to the efficiency of the user's operations.

[0049] The feedback unit can evaluate the consistency of user actions and provide appropriate feedback. For example, if a user consistently performs accurate actions, the feedback unit will provide positive feedback to encourage consistent user actions. Conversely, if a user performs inconsistent actions, the feedback unit will provide feedback indicating specific areas for improvement. Furthermore, the feedback unit can customize the content of the feedback based on the consistency of the user's actions. This allows for the provision of optimal feedback tailored to the consistency of the user's actions.

[0050] The feedback unit can evaluate the user's proficiency and provide appropriate feedback. For example, if a user demonstrates high proficiency, the feedback unit provides positive feedback to encourage further improvement. Conversely, if a user demonstrates low proficiency, the feedback unit provides feedback indicating specific areas for improvement. Furthermore, the feedback unit can customize the content of the feedback according to the user's proficiency. This allows for the provision of optimal feedback tailored to the user's skill level.

[0051] The following briefly describes the processing flow for example form 1.

[0052] Step 1: The feedback unit provides real-time feedback as the user operates in the VR environment. For example, it reacts immediately to the user's actions and provides appropriate advice and corrections. The feedback unit can also evaluate the accuracy and efficiency of the user's actions and point out areas for improvement. Specifically, in surgical simulations, construction site simulations, and safety training, it evaluates the accuracy and safety of procedures and the ability to respond to emergencies, and provides real-time feedback. Step 2: The scenario provider provides training scenarios tailored to the user's needs. For example, they select appropriate scenarios based on the user's skill level and goals. The scenario provider can also refer to the user's training history and provide scenarios that match their progress. Specifically, they provide simulation scenarios for surgery in the medical field, simulation scenarios for construction sites in the construction field, and simulation scenarios for emergencies such as fires and earthquakes in safety training. Step 3: The training department analyzes user behavior and provides individualized instruction. For example, they analyze the user's operation history and provide guidance on specific areas for improvement. The training department can also provide appropriate instruction according to the user's skill level and goals. Specifically, this could include specific instruction on surgical procedures in the medical field, specific instruction on safe work procedures in the construction field, and specific instruction on emergency response capabilities in safety training.

[0053] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that provides a platform in which users can acquire skills without risk by simulating realistic environments in fields such as medicine, construction, and safety training through VR technology. The AI ​​agent system provides real-time feedback when the user operates in the VR environment. Next, the AI ​​agent system allows for diverse scenario settings and provides training scenarios tailored to the user's needs. Furthermore, the AI ​​agent system analyzes user behavior and provides individualized guidance. This allows users to develop the ability to respond in real-world situations. For example, in the medical field, the AI ​​agent system can simulate surgery, allowing doctors to practice surgical procedures. In the construction field, it can simulate construction sites, allowing workers to learn safe work procedures. In safety training, it can simulate emergencies such as fires and earthquakes to develop response capabilities. The AI ​​agent system provides a platform in which users can acquire skills without risk through real-time feedback, diverse scenario settings, user behavior analysis, and individualized guidance. As a result, the AI ​​agent system allows users to receive real-time feedback in the VR environment, be provided with training scenarios tailored to their needs, and receive individualized guidance.

[0054] The AI ​​agent system according to this embodiment comprises a feedback unit, a scenario provision unit, and a training unit. The feedback unit provides real-time feedback when the user operates in a VR environment. For example, the feedback unit reacts immediately to the user's operations and provides appropriate advice and corrections. The feedback unit can also evaluate the accuracy and efficiency of the user's operations and point out areas for improvement. For example, when the user performs a surgical simulation, the feedback unit evaluates the accuracy of the surgical procedure and provides real-time feedback. In a construction site simulation, the feedback unit can also evaluate the safety of the work procedure and provide real-time feedback. In safety training, the feedback unit can also evaluate the ability to respond to emergencies and provide real-time feedback. The scenario provision unit provides training scenarios tailored to the user's needs. For example, the scenario provision unit selects an appropriate scenario according to the user's skill level and goals. The scenario provision unit can also refer to the user's training history and provide scenarios according to their progress. For example, in the medical field, the scenario provision unit can provide surgical simulation scenarios, allowing doctors to practice surgical procedures. The Scenario Provision Department provides simulation scenarios for construction sites in the construction sector, enabling workers to learn safe work procedures. In safety training, the Scenario Provision Department provides scenarios simulating emergencies such as fires and earthquakes, fostering response capabilities. The Instruction Department analyzes user behavior and provides individualized instruction. For example, the Instruction Department analyzes user operation history and provides guidance on specific areas for improvement. The Instruction Department can also provide appropriate instruction according to the user's skill level and goals. For example, in the medical field, the Instruction Department provides specific instruction on surgical procedures to support the skill development of doctors. In the construction sector, the Instruction Department provides specific instruction on safe work procedures to improve workers' safety awareness. In safety training, the Instruction Department provides specific instruction on emergency response capabilities to improve users' response skills.As a result, the AI ​​agent system according to this embodiment allows users to receive real-time feedback in a VR environment, be provided with training scenarios tailored to their needs, and receive individualized instruction.

[0055] The feedback unit provides real-time feedback when users operate in a VR environment. Specifically, when a user operates using a VR headset or controller, that operation data is immediately sent to the feedback unit. The feedback unit uses AI algorithms to analyze the user's operations and provides appropriate advice and corrections. For example, in a surgical simulation, if the user's hand movements holding the scalpel are inaccurate, the feedback unit will detect the movement and display correction instructions on the screen. It can also communicate specific areas for improvement to the user through voice feedback. In a construction site simulation, it evaluates the safety of the user's operation of heavy machinery and immediately issues a warning if dangerous operations occur. Furthermore, in safety training, it evaluates the user's response to emergencies such as fires and earthquakes and provides real-time feedback if the response is inappropriate. The feedback unit can compare the accuracy and efficiency of the user's operations with past operation data and benchmark data. This allows users to understand how accurate and efficient their operations are and clearly identify areas for improvement. The feedback unit can also accumulate the user's operation history and build a database to support long-term skill improvement. This allows users to refer to past feedback and perform self-assessments. The feedback unit can provide not only immediate responses to user actions but also feedback to support long-term skill improvement.

[0056] The scenario provider offers training scenarios tailored to user needs. Specifically, it selects appropriate scenarios based on the user's skill level and goals, and executes them in a VR environment. The scenario provider can refer to the user's training history and provide scenarios that match their progress. For example, in the medical field, it offers a variety of scenarios, from basic surgical procedures for beginners to complex procedures for advanced users. Once a user has mastered a particular surgical procedure, it provides a more difficult scenario as the next step. In the construction field, it offers scenarios that allow users to learn step-by-step, from basic work procedures to applied work procedures used on actual construction sites. In safety training, it provides scenarios that simulate emergencies such as fires and earthquakes, allowing users to develop their ability to respond to emergencies. The scenario provider can use AI to analyze user training data and automatically select the most suitable scenario for each individual user. This allows users to efficiently train according to their skill level and goals. The scenario provider can improve the content of scenarios based on user feedback to provide more effective training. For example, if a user feels that a particular scenario is too difficult, the difficulty level of the scenario can be adjusted based on that feedback. This allows the scenario provider to offer flexible training scenarios tailored to user needs, thereby supporting users in improving their skills.

[0057] The training department analyzes user behavior and provides individualized instruction. Specifically, it analyzes the user's operation history in detail and provides guidance on specific areas for improvement. The training department uses AI to analyze user operation data and evaluate the accuracy and efficiency of operations. For example, in the medical field, it provides specific instruction on surgical procedures to support the improvement of doctors' skills. In surgical simulations, if a user makes a mistake in a particular procedure, it points out the error and shows the correct procedure. It also analyzes what kinds of mistakes users are likely to make during surgery and provides specific advice to prevent them. In the construction field, it provides specific instruction on safe work procedures to improve workers' safety awareness. For example, when operating heavy machinery, if a user does not meet safety standards, it points out this and shows safe operating methods. In safety training, it provides specific instruction on emergency response capabilities to improve users' response capabilities. For example, in fire simulations, if a user fails to select an appropriate evacuation route, it points this out and shows the correct evacuation route. The training department can provide appropriate instruction according to the user's skill level and goals. For example, it carefully instructs beginners on basic operation methods and provides more advanced techniques and knowledge to advanced users. The training department can improve its instruction based on user feedback, thereby providing more effective instruction. This allows the training department to support users' skill development and maximize the effectiveness of the training.

[0058] The feedback unit can estimate the user's emotions and adjust the content and timing of the feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit can make the content of the feedback concise and delay the timing. If the user is relaxed, the feedback unit can also provide detailed feedback and speed up the timing. If the user is focused, the feedback unit can also make the content of the feedback specific and adjust the timing appropriately. In this way, by adjusting the content and timing of the feedback according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0059] The feedback unit can provide optimal feedback by referring to the user's past operation history when providing feedback. For example, the feedback unit can provide specific areas for improvement for operations where the user has made mistakes in the past. The feedback unit can also highlight operations where the user has made successes in the past and provide positive feedback. The feedback unit can also analyze the user's operation history and provide focused feedback on areas where mistakes are frequently made. In this way, optimal feedback can be provided by referring to the user's past operation history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's operation history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0060] The feedback unit can adjust the level of detail in the feedback based on the frequency and type of user errors. For example, if a user makes frequent errors, the feedback unit can provide detailed feedback and specific suggestions for improvement. If a user makes rare errors, the feedback unit can provide concise feedback and points to note. The feedback unit can also customize the content of the feedback and provide appropriate advice depending on the type of user error. This allows for the provision of appropriate feedback by adjusting the level of detail based on the frequency and type of user errors. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user error data into a generating AI and have the generating AI adjust the level of detail in the feedback.

[0061] The feedback unit can estimate the user's emotions and select the format of the feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit can provide audio feedback to help them relax. If the user is relaxed, the feedback unit can also provide text feedback to convey more detailed information. If the user is focused, the feedback unit can also provide visual feedback to make it easier to understand visually. This allows for more effective feedback by selecting the format of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI select the format of the feedback.

[0062] The feedback unit can provide highly relevant feedback by considering the user's geographical location information. For example, if the user is operating in a specific area, the feedback unit can provide information relevant to that area. If the user is on the move, the feedback unit can also provide information relevant to their destination. If the user is operating within a specific facility, the feedback unit can also provide information relevant to that facility. This allows the feedback unit to provide highly relevant feedback by considering the user's geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information data into a generating AI and have the generating AI perform the task of providing highly relevant feedback.

[0063] The feedback unit can analyze the user's social media activity and provide relevant feedback when providing feedback. For example, the feedback unit can provide relevant feedback based on information the user has shared on social media. The feedback unit can also provide feedback related to accounts the user follows on social media. The feedback unit can also analyze the user's social media activity and provide feedback based on their interests. This allows the feedback unit to provide relevant feedback by analyzing the user's social media activity. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant feedback.

[0064] The scenario provider can estimate the user's emotions and adjust the difficulty and content of the scenario based on the estimated emotions. For example, if the user is stressed, the scenario provider can lower the difficulty and simplify the content. If the user is relaxed, the scenario provider can also increase the difficulty and detail the content. If the user is focused, the scenario provider can appropriately adjust the difficulty and enrich the content. In this way, by adjusting the difficulty and content of the scenario according to the user's emotions, an appropriate training scenario can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scenario provider may be performed using AI or not using AI. For example, the scenario provider can input user emotion data into the generative AI and have the generative AI adjust the difficulty and content of the scenario.

[0065] The scenario provider can provide the optimal scenario by referring to the user's past training history when providing a scenario. For example, the scenario provider can provide the optimal scenario based on the training content the user has previously performed. The scenario provider can also analyze the user's training history and provide a scenario that matches their progress. The scenario provider can also provide a scenario that includes areas for improvement, based on the user's past training results. In this way, the optimal scenario can be provided by referring to the user's past training history. Some or all of the above processing in the scenario provider may be performed using AI, for example, or without using AI. For example, the scenario provider can input the user's training history data into a generating AI and have the generating AI execute the provision of the optimal scenario.

[0066] The scenario provider can provide different scenarios depending on the user's skill level. For example, the scenario provider can provide a scenario including basic operations to a beginner user. The scenario provider can also provide a scenario including advanced operations to an intermediate user. The scenario provider can also provide a scenario including complex operations to an advanced user. This allows for appropriate training by providing different scenarios according to the user's skill level. Some or all of the above processing in the scenario provider can be performed using AI, for example, or without AI. For example, the scenario provider can input user skill level data into a generating AI and have the generating AI perform the task of providing different scenarios.

[0067] The scenario provider can estimate the user's emotions and adjust the scenario length and pace based on the estimated emotions. For example, if the user is stressed, the scenario provider can shorten the scenario length and slow down the pace. If the user is relaxed, the scenario provider can also lengthen the scenario length and speed up the pace. If the user is focused, the scenario provider can appropriately adjust the scenario length and pace. This allows for the provision of appropriate training scenarios by adjusting the scenario length and pace according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scenario provider may be performed using AI or not. For example, the scenario provider can input user emotion data into the generative AI and have the generative AI adjust the scenario length and pace.

[0068] The scenario provider can provide highly relevant scenarios by considering the user's geographical location information when providing scenarios. For example, if the user is performing an operation in a specific region, the scenario provider can provide a scenario related to that region. If the user is on the move, the scenario provider can also provide a scenario related to the user's destination. If the user is performing an operation within a specific facility, the scenario provider can also provide a scenario related to that facility. In this way, highly relevant scenarios can be provided by considering the user's geographical location information. Some or all of the above processing in the scenario provider may be performed using AI, for example, or without using AI. For example, the scenario provider can input the user's geographical location information data into a generating AI and have the generating AI perform the task of providing highly relevant scenarios.

[0069] The scenario provider can analyze a user's social media activity and provide relevant scenarios when providing them. For example, the scenario provider can provide relevant scenarios based on information shared by the user on social media. The scenario provider can also provide scenarios related to accounts followed by the user on social media. The scenario provider can also analyze a user's social media activity and provide scenarios based on their interests. In this way, relevant scenarios can be provided by analyzing a user's social media activity. Some or all of the above processing in the scenario provider may be performed using AI, for example, or without AI. For example, the scenario provider can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant scenarios.

[0070] The instruction unit can estimate the user's emotions and adjust the content and method of instruction based on the estimated emotions. For example, if the user is stressed, the instruction unit can make the content of the instruction simpler and the method gentler. If the user is relaxed, the instruction unit can make the content of the instruction more detailed and the method more assertive. If the user is focused, the instruction unit can make the content of the instruction more specific and adjust the method appropriately. In this way, appropriate instruction can be provided by adjusting the content and method of instruction according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not using AI. For example, the instruction unit can input user emotion data into a generative AI and have the generative AI adjust the content and method of instruction.

[0071] The instruction department can provide optimal guidance by referring to the user's past behavior history during instruction. For example, the instruction department can provide specific areas for improvement in operations the user has performed in the past. The instruction department can also provide positive guidance by highlighting operations the user has successfully performed in the past. The instruction department can also analyze the user's behavior history and provide focused guidance on areas where mistakes are frequently made. In this way, optimal guidance can be provided by referring to the user's past behavior history. Some or all of the above processes in the instruction department may be performed using AI, for example, or not using AI. For example, the instruction department can input user behavior history data into a generating AI and have the generating AI perform the task of providing optimal guidance.

[0072] The instruction unit can apply different teaching methods depending on the user's skill level during instruction. For example, the instruction unit may provide instruction including basic operations to beginner users. The instruction unit may also provide instruction including advanced operations to intermediate users. The instruction unit may also provide instruction including complex operations to advanced users. In this way, appropriate instruction can be provided by applying different teaching methods according to the user's skill level. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not using AI. For example, the instruction unit can input user skill level data into a generating AI and have the generating AI execute the application of different teaching methods.

[0073] The instruction unit can estimate the user's emotions and select the format of instruction based on the estimated emotions. For example, if the user is stressed, the instruction unit can provide voice instruction to help them relax. If the user is relaxed, the instruction unit can also provide text instruction to convey detailed information. If the user is focused, the instruction unit can also provide visual instruction to make it easier to understand visually. This allows for more effective instruction by selecting the format of instruction according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI or not. For example, the instruction unit can input user emotion data into a generative AI and have the generative AI select the format of instruction.

[0074] The instruction unit can provide highly relevant instruction by considering the user's geographical location during instruction. For example, if the user is operating in a specific region, the instruction unit can provide instruction relevant to that region. If the user is on the move, the instruction unit can also provide instruction relevant to the user's destination. If the user is operating within a specific facility, the instruction unit can also provide instruction relevant to that facility. In this way, highly relevant instruction can be provided by considering the user's geographical location. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing highly relevant instruction.

[0075] The guidance department can analyze the user's social media activity and provide relevant guidance during the guidance process. For example, the guidance department can provide relevant guidance based on information the user has shared on social media. The guidance department can also provide guidance related to accounts the user follows on social media. The guidance department can also analyze the user's social media activity and provide guidance based on their interests. This allows the guidance department to provide relevant guidance by analyzing the user's social media activity. Some or all of the above processes in the guidance department may be performed using AI, for example, or not using AI. For example, the guidance department can input the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant guidance.

[0076] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0077] The feedback unit can analyze the user's operation speed and provide appropriate feedback. For example, if the user is in a hurry, the feedback unit will provide concise feedback to avoid disrupting the flow of operation. Conversely, if the user is operating slowly, the feedback unit will provide detailed feedback to deepen their understanding of the operation. The feedback unit can also adjust the timing of feedback according to the user's operation speed. This allows for the provision of optimal feedback tailored to the user's operating speed.

[0078] The feedback unit can evaluate the accuracy of the user's actions and provide appropriate feedback. For example, if the user performs an action correctly, the feedback unit will provide positive feedback to increase the user's motivation. Conversely, if the user makes an error, the feedback unit will provide feedback indicating specific areas for improvement. Furthermore, the feedback unit can customize the content of the feedback according to the accuracy of the user's actions. This allows for the provision of optimal feedback tailored to the accuracy of the user's actions.

[0079] The feedback unit can evaluate the efficiency of the user's operations and provide appropriate feedback. For example, if the user operates efficiently, the feedback unit will provide positive feedback to encourage the user to operate efficiently in the future. Conversely, if the user operates inefficiently, the feedback unit will provide feedback indicating specific areas for improvement. Furthermore, the feedback unit can customize the content of the feedback according to the efficiency of the user's operations. This allows for the provision of optimal feedback tailored to the efficiency of the user's operations.

[0080] The feedback unit can evaluate the consistency of user actions and provide appropriate feedback. For example, if a user consistently performs accurate actions, the feedback unit will provide positive feedback to encourage consistent user actions. Conversely, if a user performs inconsistent actions, the feedback unit will provide feedback indicating specific areas for improvement. Furthermore, the feedback unit can customize the content of the feedback based on the consistency of the user's actions. This allows for the provision of optimal feedback tailored to the consistency of the user's actions.

[0081] The feedback unit can evaluate the user's proficiency and provide appropriate feedback. For example, if a user demonstrates high proficiency, the feedback unit provides positive feedback to encourage further improvement. Conversely, if a user demonstrates low proficiency, the feedback unit provides feedback indicating specific areas for improvement. Furthermore, the feedback unit can customize the content of the feedback according to the user's proficiency. This allows for the provision of optimal feedback tailored to the user's skill level.

[0082] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is stressed, the feedback unit will provide concise feedback to reduce the user's burden. Conversely, if the user is relaxed, the feedback unit will provide detailed feedback to deepen the user's understanding. The feedback unit can also customize the content of the feedback according to the user's emotions. This allows for the provision of optimal feedback tailored to the user's feelings.

[0083] The feedback unit can estimate the user's emotions and adjust the timing of feedback based on those emotions. For example, if the user is stressed, the feedback unit will delay the timing of the feedback to reduce the user's burden. Conversely, if the user is relaxed, the feedback unit will speed up the timing of the feedback to deepen the user's understanding. The feedback unit can also customize the timing of feedback according to the user's emotions. This allows for the provision of optimal feedback tailored to the user's feelings.

[0084] The feedback unit can estimate the user's emotions and select the format of the feedback based on those emotions. For example, if the user is stressed, the feedback unit can provide voice feedback to alleviate the user's burden. Conversely, if the user is relaxed, the feedback unit can provide text feedback to convey detailed information. The feedback unit can also customize the format of the feedback according to the user's emotions. This allows it to provide the most appropriate feedback tailored to the user's feelings.

[0085] The feedback unit can estimate the user's emotions and adjust the content and timing of the feedback based on those estimates. For example, if the user is stressed, the feedback unit can provide concise feedback and delay its delivery. Conversely, if the user is relaxed, the feedback unit can provide detailed feedback and deliver it earlier. The feedback unit can also customize the content and timing of the feedback according to the user's emotions. This allows for the provision of optimal feedback tailored to the user's feelings.

[0086] The feedback unit can estimate the user's emotions and adjust the content and format of the feedback based on those estimates. For example, if the user is stressed, the feedback unit can provide concise voice feedback to reduce the user's burden. Conversely, if the user is relaxed, the feedback unit can provide detailed text feedback to deepen the user's understanding. The feedback unit can also customize the content and format of the feedback according to the user's emotions. This allows for the provision of optimal feedback tailored to the user's feelings.

[0087] The following briefly describes the processing flow for example form 2.

[0088] Step 1: The feedback unit provides real-time feedback as the user operates in the VR environment. For example, it reacts immediately to the user's actions and provides appropriate advice and corrections. The feedback unit can also evaluate the accuracy and efficiency of the user's actions and point out areas for improvement. Specifically, in surgical simulations, construction site simulations, and safety training, it evaluates the accuracy and safety of procedures and the ability to respond to emergencies, and provides real-time feedback. Step 2: The scenario provider provides training scenarios tailored to the user's needs. For example, they select appropriate scenarios based on the user's skill level and goals. The scenario provider can also refer to the user's training history and provide scenarios that match their progress. Specifically, they provide simulation scenarios for surgery in the medical field, simulation scenarios for construction sites in the construction field, and simulation scenarios for emergencies such as fires and earthquakes in safety training. Step 3: The training department analyzes user behavior and provides individualized instruction. For example, they analyze the user's operation history and provide guidance on specific areas for improvement. The training department can also provide appropriate instruction according to the user's skill level and goals. Specifically, this could include specific instruction on surgical procedures in the medical field, specific instruction on safe work procedures in the construction field, and specific instruction on emergency response capabilities in safety training.

[0089] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0091] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0092] Each of the multiple elements described above, including the feedback unit, scenario provision unit, and instruction unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the feedback unit is implemented by the control unit 46A of the smart device 14 and provides real-time feedback to user operations. The scenario provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides training scenarios tailored to user needs. The instruction unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes user behavior and provides individualized instruction. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0093] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0094] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0096] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0097] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0099] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0100] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0101] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0102] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0103] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0104] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0107] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] Each of the multiple elements described above, including the feedback unit, scenario provision unit, and instruction unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the feedback unit is implemented by the control unit 46A of the smart glasses 214 and provides real-time feedback to the user's operations. The scenario provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides training scenarios tailored to the user's needs. The instruction unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes user behavior and provides individualized instruction. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0109] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0110] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the feedback unit, scenario provision unit, and instruction unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the feedback unit is implemented by the control unit 46A of the headset terminal 314 and provides real-time feedback to the user's operations. The scenario provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides training scenarios tailored to the user's needs. The instruction unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes user behavior and provides individualized instruction. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0125] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0126] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0133] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the feedback unit, scenario provision unit, and instruction unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the feedback unit is implemented by the control unit 46A of the robot 414 and provides real-time feedback to the user's operations. The scenario provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides training scenarios tailored to the user's needs. The instruction unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes user behavior and provides individualized instruction. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0142] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0144] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0145] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0146] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0150] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0151] 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.

[0152] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0153] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0154] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0155] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0157] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0158] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0159] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0160] (Note 1) A feedback unit that provides real-time feedback when the user operates in a VR environment, A scenario provision department that provides training scenarios tailored to user needs, It includes a training department that analyzes user behavior and provides individualized guidance. A system characterized by the following features. (Note 2) The aforementioned feedback unit is It estimates the user's emotions and adjusts the content and timing of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned feedback unit is When providing feedback, we refer to the user's past operation history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is When providing feedback, adjust the level of detail based on the frequency and type of user errors. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is The system estimates the user's emotions and selects the format of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is When providing feedback, we take the user's geographical location into consideration to provide more relevant feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and provide relevant feedback. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned scenario provision unit, The system estimates the user's emotions and adjusts the difficulty and content of the scenario based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned scenario provision unit, When providing scenarios, the system will refer to the user's past training history to provide the most suitable scenario. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned scenario provision unit, When providing scenarios, we offer different scenarios depending on the user's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned scenario provision unit, It estimates the user's emotions and adjusts the length and pace of the scenario based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned scenario provision unit, When providing scenarios, we will consider the user's geographical location to provide highly relevant scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned scenario provision unit, When providing scenarios, we analyze the user's social media activity and provide relevant scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned leadership, The system estimates the user's emotions and adjusts the content and methods of instruction based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned leadership, During instruction, we refer to the user's past behavioral history to provide optimal guidance. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned leadership, During instruction, different teaching methods are applied depending on the user's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned leadership, The system estimates the user's emotions and selects the format of instruction based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned leadership, When providing instruction, consider the user's geographical location to provide more relevant guidance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned leadership, During coaching sessions, we analyze users' social media activity and provide relevant guidance. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A feedback unit that provides real-time feedback when the user operates in a VR environment, A scenario provision department that provides training scenarios tailored to user needs, It includes a training department that analyzes user behavior and provides individualized guidance. A system characterized by the following features.

2. The aforementioned feedback unit is It estimates the user's emotions and adjusts the content and timing of feedback based on those estimated emotions. The system according to feature 1.

3. The aforementioned feedback unit is When providing feedback, we refer to the user's past operation history to provide the most appropriate feedback. The system according to feature 1.

4. The aforementioned feedback unit is When providing feedback, adjust the level of detail based on the frequency and type of user errors. The system according to feature 1.

5. The aforementioned feedback unit is The system estimates the user's emotions and selects the format of feedback based on those estimated emotions. The system according to feature 1.

6. The aforementioned feedback unit is When providing feedback, we take the user's geographical location into consideration to provide more relevant feedback. The system according to feature 1.

7. The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and provide relevant feedback. The system according to feature 1.

8. The aforementioned scenario provision unit, The system estimates the user's emotions and adjusts the difficulty and content of the scenario based on those estimated emotions. The system according to feature 1.

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