System

Abstract

An object of a system according to an embodiment is to provide an optimal training environment according to a training situation of a user.SOLUTION: A system according to an embodiment includes a data input unit, an environment generation unit, a procedure generation unit, a guidance unit, and an evaluation unit. The data input unit inputs a training situation of a user. The environment generation unit generates a training environment based on the data input by the data input unit. The procedure generation unit generates a training procedure on the basis of the training environment generated by the environment generation unit. The instructor instructs training on the basis of the training procedure generated by the procedure generator. The evaluation unit evaluates a result of the training guided by the guidance unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to provide an optimal training environment that suits the user's training situation.

[0005] The system according to the embodiment aims to provide an optimal training environment according to the user's training situation. [Means for solving the problem]

[0006] The system according to the embodiment includes a data input unit, an environment generation unit, a procedure generation unit, an instruction unit, and an evaluation unit. The data input unit inputs the user's training situation. The environment generation unit generates a training environment based on the data input by the data input unit. The procedure generation unit generates a training procedure based on the training environment generated by the environment generation unit. The instruction unit provides training based on the training procedure generated by the procedure generation unit. The evaluation unit evaluates the results of the training provided by the instruction unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal training environment according to the user's training situation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In a VR training system according to an embodiment of the present invention, a user inputs the situation in which they want to train as data into a generation AI, which then generates a training environment based on that data and provides the training environment including the tools and procedures necessary for training. This allows the VR training system to provide a VR training environment based on the user's training situation and provide training guidance and evaluation.

[0029] The VR training system according to the embodiment includes a data input unit, an environment generation unit, a procedure generation unit, an instruction unit, and an evaluation unit. The data input unit inputs a user's training situation. For example, the user inputs the situation he or she wants to train in text format. The data input unit can also use voice recognition technology to enable the user to specify a training scenario in natural language. The environment generation unit generates a training environment based on the data input by the data input unit. For example, in a fire evacuation drill, a burning building and an evacuation route are generated. The environment generation unit can also track the user's movements in real time and dynamically change the environment according to the movements. The procedure generation unit generates a training procedure based on the training environment generated by the environment generation unit. For example, in a fire evacuation drill, a procedure is generated that includes confirmation of an evacuation route and important points to note during evacuation. The procedure generation unit can also customize the training procedure according to the user's learning style. The instruction unit provides training guidance based on the training procedure generated by the procedure generation unit. For example, in a fire evacuation drill, specific instructions such as "Open this door next" are given. The instructor can also track the user's movements in real time during training and provide immediate feedback based on the movements. The evaluation unit evaluates the results of the training provided by the instructor. For example, in a fire evacuation training, the evaluation unit evaluates whether the evacuation route was followed correctly. The evaluation unit can also compare the training results with the user's past training data and evaluate the progress in detail. As a result, the VR training system according to the embodiment can provide a VR training environment based on the user's training status and provide training instruction and evaluation.

[0030] The data input unit can refer to the user's past training data and generate a customized training scenario based on the individual training history. The data input unit, for example, collects the user's past training data and analyzes the individual training history based on that data. For example, the data input unit refers to the results of past evacuation drills and generates a scenario that strengthens the user's weak points. The data input unit also evaluates the user's progress based on the training history and customizes the next training scenario. For example, it generates a scenario that focuses on practicing procedures in cooking training that the user previously failed at. The data input unit also suggests training scenarios that meet the user's individual needs based on the user's training history. For example, it generates a training scenario to improve a specific skill and provides it to the user. This makes it possible to provide a customized training scenario based on the user's past training data.

[0031] The data input unit can collect the user's biometric information in real time and dynamically adjust the training scenario based on that information. The data input unit, for example, monitors the user's heart rate and stress level in real time and adjusts the training scenario based on that data. For example, if the stress level is high, the difficulty level of the training is lowered. The data input unit also provides a training scenario based on the user's physical condition and mental state based on the biometric information. For example, if the heart rate increases, a scenario that includes breaks is generated. The data input unit also collects the user's biometric information in real time and dynamically changes the training scenario based on that data. For example, if the stress level is low, the difficulty level of the training is increased. This allows the training scenario to be dynamically adjusted based on the user's biometric information.

[0032] The data input unit can use voice recognition technology to enable a user to specify a training scenario in natural language. The data input unit, for example, uses voice recognition technology to build a system that allows a user to specify a training scenario in natural language. For example, a training scenario is generated simply by speaking, "I want to conduct a fire evacuation drill." The data input unit also allows a user to input a training scenario by voice, and the generation AI analyzes the voice data to generate a training scenario. For example, a kitchen environment is generated simply by speaking, "I want to practice cooking." The data input unit also uses voice recognition technology to provide an interface that allows a user to specify a training scenario in natural language. For example, an evacuation drill scenario is generated simply by speaking, "I want to check the evacuation route." This allows a user to specify a training scenario in natural language.

[0033] The data input unit can add a function to share training situation data input with other users and generate a joint training scenario. The data input unit, for example, builds a system for sharing training situation data input with other users and jointly generating training scenarios. For example, a scenario is generated in which multiple users simultaneously conduct evacuation drills. The data input unit also provides a function for sharing training situation data with other users and jointly creating training scenarios. For example, a scenario is generated in which a team conducts cooking training. The data input unit also develops a system for sharing training situation data and jointly generating training scenarios with other users. For example, a scenario is generated in which multiple users cooperate to confirm evacuation routes. This allows training scenarios to be jointly generated with other users.

[0034] The environment generation unit can track the user's movements in real time and dynamically change the environment in response to the movements. The environment generation unit, for example, builds a system that tracks the user's movements in real time and dynamically changes the training environment in response to the movements. For example, if the user changes their evacuation route, an environment corresponding to that route is generated. The environment generation unit also dynamically adjusts the training environment in response to the user's movements using movement tracking technology. For example, if the user changes their cooking utensils, an environment corresponding to that movement is generated. The environment generation unit also develops a system that monitors the user's movements in real time and dynamically changes the training environment in response to that movement. For example, if the user changes their evacuation route, an environment corresponding to that route is generated. This makes it possible to dynamically change the training environment in response to the user's movements.

[0035] The environment generation unit can reflect the user's past training data and generate an environment that corresponds to the individual progress. The environment generation unit, for example, builds a system that generates a training environment that corresponds to the individual progress based on the user's past training data. For example, it reflects the results of past evacuation drills and generates an environment that strengthens the user's weak points. The environment generation unit also provides a training environment that corresponds to the user's progress based on the training data. For example, it generates an environment that focuses on practicing procedures that the user failed in cooking training in the past. The environment generation unit also develops a system that reflects the user's past training data and generates a training environment that corresponds to the individual needs. For example, it generates a training environment to improve a specific skill. In this way, it is possible to generate an environment that corresponds to the individual progress based on the user's past training data.

[0036] The environment generation unit can make the training environment available on different devices, making it multi-platform compatible. For example, the environment generation unit builds a system that makes the training environment available on smartphones and tablets, achieving multi-platform compatibility. For example, it makes it possible to run a training scenario created on a PC on a smartphone. The environment generation unit also develops a system that makes the training environment available on different devices, allowing users to train on any device. For example, it runs a training scenario on a tablet and checks the results on a smartphone. The environment generation unit also makes the training environment multi-platform compatible, providing a system that allows seamless training on different devices. For example, training can be started on a PC and continued on a smartphone. This allows the training environment to be used on different devices.

[0037] The environment generation unit can add a collaborative training mode to the training environment that can be shared with other users in real time. The environment generation unit, for example, adds a collaborative training mode to the training environment that can be shared with other users in real time, and builds a system in which multiple users can train simultaneously. For example, multiple users can cooperate to conduct evacuation drills. The environment generation unit also adds the collaborative training mode and develops a system in which a training environment can be shared with other users in real time. For example, multiple users can practice cooking simultaneously. The environment generation unit also adds the collaborative training mode to the training environment, and provides a system in which a training environment can be trained with other users in real time. For example, multiple users can cooperate to check evacuation routes. This allows the training environment to be shared with other users in real time.

[0038] The procedure generation unit can customize the training procedure according to the user's learning style. For example, the procedure generation unit analyzes the user's learning style and builds a system that customizes the training procedure according to the user's visual, auditory, and tactile senses. For example, a visual guide is provided to a user with a visual learning style. The procedure generation unit also customizes the training procedure according to the learning style. For example, an audio guide is provided to a user with an auditory learning style. The procedure generation unit also monitors the user's learning style in real time and develops a system that customizes the training procedure based on that data. For example, tactile feedback is provided to a user with a tactile learning style. This makes it possible to customize the training procedure according to the user's learning style.

[0039] The procedure generation unit can reflect the user's past training data and generate procedures according to individual progress. The procedure generation unit, for example, builds a system that generates training procedures according to individual progress based on the user's past training data. For example, it reflects the results of past evacuation drills and generates procedures to strengthen areas in which the user is weak. The procedure generation unit also provides training procedures according to the user's progress based on the training data. For example, it generates a procedure to focus on practicing procedures that the user failed in cooking training in the past. The procedure generation unit also develops a system that reflects the user's past training data and generates training procedures according to individual needs. For example, it generates a training procedure to improve a specific skill. In this way, it is possible to generate procedures according to individual progress based on the user's past training data.

[0040] The procedure generation unit can automatically translate training procedures into different languages ​​to accommodate international users. The procedure generation unit, for example, builds a system that automatically translates training procedures into different languages ​​to accommodate international users. For example, it translates into multiple languages ​​such as English, French, and Chinese. The procedure generation unit also uses an automatic translation function to make training procedures multilingual and develop a system that can be used by international users. For example, it translates training procedures in real time and provides them to users. The procedure generation unit also builds a system that automatically translates training procedures into different languages ​​to obtain feedback from an international perspective. For example, it collects opinions from international users based on the translated procedures. This allows automatic translation into different languages ​​to accommodate international users.

[0041] The procedure generation unit can share a training procedure with other users and generate a procedure for conducting training jointly. The procedure generation unit, for example, builds a system for sharing a training procedure with other users and generating a procedure for conducting training jointly. For example, it generates a procedure for multiple users to conduct evacuation drills simultaneously. The procedure generation unit also generates a joint training procedure and develops a system that can conduct training in real time with other users. For example, it generates a procedure for multiple users to conduct cooking training simultaneously. The procedure generation unit also provides a system for sharing a training procedure and generating a procedure for conducting training jointly with other users. For example, it generates a procedure for multiple users to cooperate in checking evacuation routes. In this way, it is possible to generate a procedure for conducting training jointly with other users.

[0042] The training department can track the user's movements in real time during training and provide instant feedback according to the movements. For example, the training department builds a system that tracks the user's movements in real time during training and provides instant feedback according to the movements. For example, during an evacuation drill, it evaluates in real time whether the correct evacuation route is being followed. The training department also uses movement tracking technology to provide instant feedback according to the user's movements. For example, during cooking training, it evaluates in real time whether the correct cooking procedures are being performed. The training department also develops a system that monitors the user's movements in real time and provides instant feedback according to the movements. For example, during an evacuation drill, it evaluates in real time whether the correct evacuation route is being followed. This makes it possible to provide instant feedback according to the user's movements.

[0043] The instructor monitors the user's biometric information in real time during training and can provide instruction according to the stress level. The instructor, for example, builds a system that monitors the user's biometric information in real time during training and provides instruction according to the stress level based on that data. For example, if the stress level is high, the difficulty of the training is lowered. The instructor also provides instruction according to the user's stress level based on the biometric information. For example, if the heart rate increases, the instructor provides instruction to take a break. The instructor also develops a system that monitors the user's biometric information in real time and provides instruction according to the stress level based on that data. For example, if the stress level is low, the difficulty of the training is increased. This makes it possible to provide instruction according to the user's stress level.

[0044] The instructor can make it possible for training instruction to be received on different devices. For example, the instructor can build a system that allows training instruction to be received on smart glasses or wearable devices, thereby achieving multi-platform compatibility. For example, evacuation training instruction is received using smart glasses. The instructor can also develop a system that allows training instruction to be received on different devices, allowing users to train on any device. For example, cooking training instruction is received using a wearable device. The instructor can also make training instruction multi-platform compatible, providing a system that allows training to be conducted seamlessly on different devices. For example, training can be started on smart glasses and continued on a wearable device. This allows training instruction to be received on different devices.

[0045] The instructor can share training instruction with other users in real time and provide instruction for conducting joint training. The instructor, for example, builds a system for sharing training instruction with other users in real time and providing instruction for conducting joint training. For example, instruction is provided for multiple users to conduct evacuation training simultaneously. The instructor also develops a system for providing joint training instruction and enabling training with other users in real time. For example, instruction is provided for multiple users to conduct cooking training simultaneously. The instructor also provides a system for sharing training instruction and providing instruction for conducting joint training with other users. For example, instruction is provided for multiple users to cooperate in checking evacuation routes. This allows training instruction to be shared with other users in real time.

[0046] The evaluation unit can compare the training results with the user's past training data and evaluate the progress in detail. The evaluation unit, for example, compares the training results with the user's past training data and builds a system that evaluates the progress in detail. For example, the user's progress is evaluated by comparing with the results of past evacuation drills. The evaluation unit also evaluates the user's progress in detail based on the past training data. For example, it evaluates whether a procedure that previously failed in cooking training has been improved. The evaluation unit also develops a system that compares the training results with the user's past training data and evaluates the progress in detail. For example, the user's progress is evaluated by comparing with the results of past evacuation drills. This makes it possible to evaluate the progress in detail by comparing with the user's past training data.

[0047] The evaluation unit can compare the training results with the user's biometric information and reflect the stress level and concentration level in the evaluation. The evaluation unit, for example, builds a system that compares the training results with the user's biometric information and reflects the stress level and concentration level in the evaluation. For example, the evaluation unit evaluates the training results based on the heart rate and stress level. The evaluation unit also reflects the user's stress level and concentration level in the evaluation based on the biometric information. For example, if the heart rate increases, the difficulty of the training is reduced. The evaluation unit also develops a system that compares the training results with the user's biometric information and reflects the stress level and concentration level in the evaluation. For example, the training results are evaluated based on the heart rate and stress level. This makes it possible to reflect the stress level and concentration level in the evaluation based on the user's biometric information.

[0048] The evaluation unit can make the training results available on different devices, making it multi-platform compatible. For example, the evaluation unit builds a system that allows training results to be viewed on smartphones and tablets, achieving multi-platform compatibility. For example, it makes it possible to view the results of training conducted on a PC on a smartphone. The evaluation unit also develops a system that allows training results to be viewed on different devices, allowing users to view training results on any device. For example, the training results can be viewed on a tablet and the details can be viewed on a smartphone. The evaluation unit also makes the training results available on multiple platforms, providing a system that allows training results to be viewed seamlessly on different devices. For example, training can be conducted on a PC and the results can be viewed on a smartphone. This allows training results to be viewed on different devices.

[0049] The evaluation unit can add a function for sharing training results with other users and conducting collaborative evaluations. The evaluation unit, for example, builds a system that adds a function for sharing training results with other users and conducting collaborative evaluations. For example, multiple users simultaneously evaluate the results of evacuation training. The evaluation unit also adds a collaborative evaluation function and develops a system that allows training results to be shared with other users in real time. For example, multiple users simultaneously evaluate the results of cooking training. The evaluation unit also provides a system that provides a function for sharing training results and conducting collaborative evaluations with other users. For example, multiple users cooperatively evaluate the results of evacuation route confirmation. This allows training results to be evaluated collaboratively with other users.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The data input unit inputs the user's training situation. For example, the user inputs the situation he or she wants to train in text format. The data input unit can also use voice recognition technology to allow the user to specify a training scenario in natural language. The environment generation unit generates a training environment based on the data input by the data input unit. For example, in the case of a fire evacuation drill, a burning building and an evacuation route are generated. The environment generation unit can also track the user's movements in real time and dynamically change the environment according to the movements. The procedure generation unit generates a training procedure based on the training environment generated by the environment generation unit. For example, in a fire evacuation drill, a procedure is generated that includes checking the evacuation route and points to note when evacuating. The procedure generation unit can also customize the training procedure according to the user's learning style. The instructor provides training guidance based on the training procedure generated by the procedure generation unit. For example, in a fire evacuation drill, specific instructions such as "Open this door next" are given. The instructor can also track the user's movements in real time during the training and provide immediate feedback according to the movements. The evaluation unit evaluates the training results provided by the instructor. For example, in a fire evacuation drill, the evaluation unit evaluates whether the user followed the correct evacuation route. The evaluation unit can also compare the training results with the user's past training data to evaluate the user's progress in detail. This allows the VR training system according to the embodiment to provide a VR training environment based on the user's training status and provide training guidance and evaluation.

[0052] The data input unit can refer to the user's past training data and generate a customized training scenario based on the user's individual training history. For example, it can refer to the results of past evacuation drills and generate a scenario that strengthens the user's weak points. The data input unit also evaluates the user's progress based on the training history and customizes the next training scenario. For example, it can generate a scenario that focuses on practicing procedures in cooking training that the user previously failed at. The data input unit also suggests training scenarios that meet the user's individual needs based on the user's training history. For example, it can generate a training scenario to improve a specific skill and provide it to the user. This makes it possible to provide a customized training scenario based on the user's past training data.

[0053] The data input unit can collect the user's biometric information in real time and dynamically adjust the training scenario based on that information. For example, the user's heart rate and stress level can be monitored in real time, and the training scenario can be adjusted based on that data. For example, if the stress level is high, the difficulty level of the training can be lowered. The data input unit also provides a training scenario based on the user's physical condition and mental state based on the biometric information. For example, if the heart rate increases, a scenario that includes breaks can be generated. The data input unit also collects the user's biometric information in real time and dynamically changes the training scenario based on that data. For example, if the stress level is low, the difficulty level of the training can be increased. This allows the training scenario to be dynamically adjusted based on the user's biometric information.

[0054] The data input unit can use voice recognition technology to enable the user to specify a training scenario in natural language. For example, a system can be built using voice recognition technology to enable the user to specify a training scenario in natural language. For example, a training scenario can be generated simply by speaking, "I would like to conduct a fire evacuation drill." The data input unit also enables the user to input a training scenario by voice, and the generation AI analyzes the voice data to generate a training scenario. For example, a kitchen environment can be generated simply by speaking, "I would like to practice cooking." The data input unit also uses voice recognition technology to provide an interface that enables the user to specify a training scenario in natural language. For example, an evacuation drill scenario can be generated simply by speaking, "I would like to check the evacuation route." This allows the user to specify a training scenario in natural language.

[0055] The data input unit can add a function to share training situation data input with other users and generate a joint training scenario. For example, a system is constructed in which training situation data input is shared with other users and training scenarios are jointly generated. For example, a scenario is generated in which multiple users simultaneously conduct evacuation drills. The data input unit also provides a function to share training situation data with other users and jointly create training scenarios. For example, a scenario is generated in which a team conducts cooking training. The data input unit also develops a system in which training situation data is shared and training scenarios are jointly generated with other users. For example, a scenario is generated in which multiple users cooperate to confirm evacuation routes. This allows training scenarios to be jointly generated with other users.

[0056] The environment generation unit can track the user's movements in real time and dynamically change the environment in response to the movements. For example, a system is constructed that tracks the user's movements in real time and dynamically changes the training environment in response to the movements. For example, if the user changes their evacuation route, an environment is generated that corresponds to that route. The environment generation unit also uses movement tracking technology to dynamically adjust the training environment in response to the user's movements. For example, if the user changes their evacuation route, an environment is generated that corresponds to that movement. The environment generation unit also monitors the user's movements in real time and develops a system that dynamically changes the training environment in response to that movement. For example, if the user changes their evacuation route, an environment is generated that corresponds to that route. This makes it possible to dynamically change the training environment in response to the user's movements.

[0057] The environment generation unit can reflect the user's past training data and generate an environment that corresponds to the individual progress. For example, a system is constructed that generates a training environment that corresponds to the individual progress based on the user's past training data. For example, an environment is generated that reflects the results of past evacuation drills and strengthens the user's weak points. The environment generation unit also provides a training environment that corresponds to the user's progress based on the training data. For example, an environment is generated that focuses on practicing procedures that the user failed in the past in cooking training. The environment generation unit also develops a system that reflects the user's past training data and generates a training environment that corresponds to the individual needs. For example, a training environment is generated to improve a specific skill. In this way, an environment can be generated that corresponds to the individual progress based on the user's past training data.

[0058] The environment generation unit can make the training environment available on different devices, making it multi-platform compatible. For example, a system can be built that makes the training environment available on smartphones and tablets, achieving multi-platform compatibility. For example, a training scenario created on a PC can be executed on a smartphone. The environment generation unit also develops a system that makes the training environment available on different devices, allowing users to train on any device. For example, a training scenario can be executed on a tablet and the results can be checked on a smartphone. The environment generation unit also makes the training environment multi-platform compatible, providing a system that allows seamless training on different devices. For example, training can be started on a PC and continued on a smartphone. This allows the training environment to be used on different devices.

[0059] The environment generation unit can add a collaborative training mode to the training environment, which can be shared with other users in real time. For example, a collaborative training mode can be added to the training environment, which can be shared with other users in real time, to build a system in which multiple users can train simultaneously. For example, multiple users can cooperate to conduct evacuation drills. The environment generation unit can also add a collaborative training mode to develop a system in which a training environment can be shared with other users in real time. For example, multiple users can practice cooking simultaneously. The environment generation unit can also add a collaborative training mode to the training environment, to provide a system in which a training environment can be trained with other users in real time. For example, multiple users can cooperate to check evacuation routes. This allows the training environment to be shared with other users in real time.

[0060] The procedure generation unit can customize the training procedure according to the user's learning style. For example, a system is constructed that analyzes the user's learning style and customizes the training procedure according to their visual, auditory, and tactile senses. For example, a visual guide is provided to a user with a visual learning style. The procedure generation unit also customizes the training procedure according to the learning style. For example, an audio guide is provided to a user with an auditory learning style. The procedure generation unit also monitors the user's learning style in real time and develops a system that customizes the training procedure based on that data. For example, tactile feedback is provided to a user with a tactile learning style. This makes it possible to customize the training procedure according to the user's learning style.

[0061] The procedure generation unit can reflect the user's past training data and generate procedures according to individual progress. For example, a system is constructed that generates training procedures according to individual progress based on the user's past training data. For example, a procedure is generated that reflects the results of past evacuation drills and strengthens areas in which the user is weak. The procedure generation unit also provides training procedures according to the user's progress based on the training data. For example, a procedure is generated that focuses on practicing procedures that the user failed in cooking training in the past. The procedure generation unit also develops a system that reflects the user's past training data and generates training procedures according to individual needs. For example, a training procedure is generated to improve a specific skill. In this way, procedures can be generated according to individual progress based on the user's past training data.

[0062] The procedure generation unit can automatically translate training procedures into different languages ​​to accommodate international users. For example, a system is built that automatically translates training procedures into different languages ​​to accommodate international users. For example, it translates into multiple languages ​​such as English, French, and Chinese. The procedure generation unit also uses an automatic translation function to make training procedures multilingual and develop a system that can be used by international users. For example, it translates training procedures in real time and provides them to users. The procedure generation unit also builds a system that automatically translates training procedures into different languages ​​to obtain feedback from an international perspective. For example, it collects opinions from international users based on the translated procedures. This allows automatic translation into different languages ​​to accommodate international users.

[0063] The procedure generation unit can share a training procedure with other users and generate a procedure for conducting training jointly. For example, a system is constructed that generates a procedure for sharing a training procedure with other users and conducting training jointly. For example, a procedure is generated for multiple users to conduct evacuation training simultaneously. The procedure generation unit also generates a joint training procedure and develops a system that can conduct training in real time with other users. For example, a procedure is generated for multiple users to conduct cooking training simultaneously. The procedure generation unit also provides a system that shares a training procedure and generates a procedure for conducting training jointly with other users. For example, a procedure is generated for multiple users to cooperate in checking evacuation routes. This makes it possible to generate a procedure for conducting training jointly with other users.

[0064] The instructor can track a user's movements in real time during training and provide instant feedback based on the movements. For example, a system is constructed that tracks a user's movements in real time during training and provides instant feedback based on those movements. For example, during an evacuation drill, it is evaluated in real time whether the correct evacuation route is being followed. The instructor can also use movement tracking technology to provide instant feedback based on the user's movements. For example, during cooking training, it is evaluated in real time whether the correct cooking procedures are being performed. The instructor can also develop a system that monitors a user's movements in real time and provides instant feedback based on those movements. For example, during an evacuation drill, it is evaluated in real time whether the correct evacuation route is being followed. This makes it possible to provide instant feedback based on the user's movements.

[0065] The instructor can monitor the user's biometric information in real time during training and provide instruction according to the stress level. For example, a system is constructed that monitors the user's biometric information in real time during training and provides instruction according to the stress level based on that data. For example, if the stress level is high, the difficulty of the training is lowered. The instructor also provides instruction according to the user's stress level based on the biometric information. For example, if the heart rate increases, the instructor can provide instruction to take a break. The instructor also develops a system that monitors the user's biometric information in real time and provides instruction according to the stress level based on that data. For example, if the stress level is low, the difficulty of the training is increased. This makes it possible to provide instruction according to the user's stress level.

[0066] The instructor can make it possible for training instruction to be received on different devices. For example, a system can be built that allows training instruction to be received on smart glasses or wearable devices, achieving multi-platform compatibility. For example, evacuation drill instruction can be received using smart glasses. The instructor can also develop a system that allows training instruction to be received on different devices, allowing users to train on any device. For example, cooking training instruction can be received using a wearable device. The instructor can also make training instruction multi-platform compatible, providing a system that allows training to be conducted seamlessly on different devices. For example, training can be started on smart glasses and continued on a wearable device. This allows training instruction to be received on different devices.

[0067] The instructor can share training instruction with other users in real time and provide instruction for conducting joint training. For example, a system is constructed that shares training instruction with other users in real time and provides instruction for conducting joint training. For example, instruction is provided for multiple users to conduct evacuation training simultaneously. The instructor also develops a system that provides joint training instruction and allows training with other users in real time. For example, instruction is provided for multiple users to conduct cooking training simultaneously. The instructor also provides a system that shares training instruction and provides instruction for conducting joint training with other users. For example, instruction is provided for multiple users to cooperate in checking evacuation routes. This allows training instruction to be shared with other users in real time.

[0068] The evaluation unit can compare the training results with the user's past training data and evaluate the progress in detail. For example, a system is constructed that compares the training results with the user's past training data and evaluates the progress in detail. For example, the user's progress is evaluated by comparing with the results of past evacuation drills. The evaluation unit also evaluates the user's progress in detail based on the past training data. For example, it evaluates whether a procedure that previously failed in cooking training has been improved. The evaluation unit also develops a system that compares the training results with the user's past training data and evaluates the progress in detail. For example, the user's progress is evaluated by comparing with the results of past evacuation drills. This makes it possible to evaluate the progress in detail by comparing with the user's past training data.

[0069] The evaluation unit can compare the training results with the user's biometric information and reflect the stress level and concentration level in the evaluation. For example, a system is constructed that compares the training results with the user's biometric information and reflects the stress level and concentration level in the evaluation. For example, the training results are evaluated based on heart rate and stress level. The evaluation unit also reflects the user's stress level and concentration level in the evaluation based on the biometric information. For example, if the heart rate increases, the difficulty of the training is reduced. The evaluation unit also develops a system that compares the training results with the user's biometric information and reflects the stress level and concentration level in the evaluation. For example, the training results are evaluated based on heart rate and stress level. This makes it possible to reflect the stress level and concentration level in the evaluation based on the user's biometric information.

[0070] The evaluation unit can make the training results available on different devices, making it multi-platform compatible. For example, a system can be built that allows training results to be viewed on smartphones and tablets, achieving multi-platform compatibility. For example, the results of training conducted on a PC can be viewed on a smartphone. The evaluation unit also develops a system that allows training results to be viewed on different devices, allowing users to view training results on any device. For example, the training results can be viewed on a tablet and the details can be viewed on a smartphone. The evaluation unit also makes the training results available on multiple platforms, providing a system that allows training results to be viewed seamlessly on different devices. For example, training can be conducted on a PC and the results can be viewed on a smartphone. This allows training results to be viewed on different devices.

[0071] The evaluation unit can add a function for sharing training results with other users and conducting collaborative evaluations. For example, a system is constructed that adds a function for sharing training results with other users and conducting collaborative evaluations. For example, multiple users simultaneously evaluate the results of evacuation training. The evaluation unit also adds a collaborative evaluation function and develops a system that allows training results to be shared with other users in real time. For example, multiple users simultaneously evaluate the results of cooking training. The evaluation unit also provides a system that provides a function for sharing training results and conducting collaborative evaluations with other users. For example, multiple users cooperatively evaluate the results of evacuation route confirmation. This allows training results to be evaluated collaboratively with other users.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The data input unit inputs the user's training situation. For example, the user inputs the situation he or she wants to train in text format. The data input unit can also use voice recognition technology to allow the user to specify a training scenario in natural language. Step 2: The environment generation unit generates a training environment based on the data input by the data input unit. For example, in the case of a fire evacuation drill, a burning building and an evacuation route are generated. The environment generation unit can also track the user's movements in real time and dynamically change the environment according to the movements. Step 3: The procedure generation unit generates a training procedure based on the training environment generated by the environment generation unit. For example, in a fire evacuation drill, the procedure generated includes confirmation of evacuation routes and points to note when evacuating. The procedure generation unit can also customize the training procedure according to the user's learning style. Step 4: The instructor provides training based on the training procedure generated by the procedure generator. For example, in a fire evacuation drill, specific instructions such as "Open this door next" are given. The instructor can also track the user's movements in real time during the training and provide immediate feedback based on the movements. Step 5: The evaluator evaluates the training results provided by the instructor. For example, in a fire evacuation drill, the evaluator evaluates whether the evacuation route was followed correctly. The evaluator can also compare the training results with the user's past training data to evaluate progress in detail.

[0074] (Example 2) In a VR training system according to an embodiment of the present invention, a user inputs the situation in which they want to train as data into a generation AI, which then generates a training environment based on that data and provides the training environment including the tools and procedures necessary for training. This allows the VR training system to provide a VR training environment based on the user's training situation and provide training guidance and evaluation.

[0075] The VR training system according to the embodiment includes a data input unit, an environment generation unit, a procedure generation unit, an instruction unit, and an evaluation unit. The data input unit inputs a user's training situation. For example, the user inputs the situation he or she wants to train in text format. The data input unit can also use voice recognition technology to enable the user to specify a training scenario in natural language. The environment generation unit generates a training environment based on the data input by the data input unit. For example, in a fire evacuation drill, a burning building and an evacuation route are generated. The environment generation unit can also track the user's movements in real time and dynamically change the environment according to the movements. The procedure generation unit generates a training procedure based on the training environment generated by the environment generation unit. For example, in a fire evacuation drill, a procedure is generated that includes confirmation of an evacuation route and important points to note during evacuation. The procedure generation unit can also customize the training procedure according to the user's learning style. The instruction unit provides training guidance based on the training procedure generated by the procedure generation unit. For example, in a fire evacuation drill, specific instructions such as "Open this door next" are given. The instructor can also track the user's movements in real time during training and provide immediate feedback based on the movements. The evaluation unit evaluates the results of the training provided by the instructor. For example, in a fire evacuation training, the evaluation unit evaluates whether the evacuation route was followed correctly. The evaluation unit can also compare the training results with the user's past training data and evaluate the progress in detail. As a result, the VR training system according to the embodiment can provide a VR training environment based on the user's training status and provide training instruction and evaluation.

[0076] The data input unit can refer to the user's past training data and generate a customized training scenario based on the individual training history. The data input unit, for example, collects the user's past training data and analyzes the individual training history based on that data. For example, the data input unit refers to the results of past evacuation drills and generates a scenario that strengthens the user's weak points. The data input unit also evaluates the user's progress based on the training history and customizes the next training scenario. For example, it generates a scenario that focuses on practicing procedures in cooking training that the user previously failed at. The data input unit also suggests training scenarios that meet the user's individual needs based on the user's training history. For example, it generates a training scenario to improve a specific skill and provides it to the user. This makes it possible to provide a customized training scenario based on the user's past training data.

[0077] The data input unit can collect the user's biometric information in real time and dynamically adjust the training scenario based on that information. The data input unit, for example, monitors the user's heart rate and stress level in real time and adjusts the training scenario based on that data. For example, if the stress level is high, the difficulty level of the training is lowered. The data input unit also provides a training scenario based on the user's physical condition and mental state based on the biometric information. For example, if the heart rate increases, a scenario that includes breaks is generated. The data input unit also collects the user's biometric information in real time and dynamically changes the training scenario based on that data. For example, if the stress level is low, the difficulty level of the training is increased. This allows the training scenario to be dynamically adjusted based on the user's biometric information.

[0078] The data input unit can use the emotion estimation function to analyze the user's current emotional state and propose an optimal training scenario. For example, the data input unit uses the emotion estimation function to analyze the user's facial expressions and voice to identify the user's current emotional state. For example, if the user is nervous, the data input unit proposes a training scenario that will help the user relax. The data input unit also monitors the user's emotional state in real time and adjusts the training scenario based on that data. For example, if the user is enjoying themselves, the data input unit increases the difficulty of the training. The data input unit also uses the emotion estimation function to generate a training scenario that corresponds to the user's emotional state. For example, if the user is tired, the data input unit proposes a short and effective training scenario. This makes it possible to propose an optimal training scenario based on the user's emotional state.

[0079] The data input unit can use voice recognition technology to enable a user to specify a training scenario in natural language. The data input unit, for example, uses voice recognition technology to build a system that allows a user to specify a training scenario in natural language. For example, a training scenario is generated simply by speaking, "I want to conduct a fire evacuation drill." The data input unit also allows a user to input a training scenario by voice, and the generation AI analyzes the voice data to generate a training scenario. For example, a kitchen environment is generated simply by speaking, "I want to practice cooking." The data input unit also uses voice recognition technology to provide an interface that allows a user to specify a training scenario in natural language. For example, an evacuation drill scenario is generated simply by speaking, "I want to check the evacuation route." This allows a user to specify a training scenario in natural language.

[0080] The data input unit can add a function to share training situation data input with other users and generate a joint training scenario. The data input unit, for example, builds a system for sharing training situation data input with other users and jointly generating training scenarios. For example, a scenario is generated in which multiple users simultaneously conduct evacuation drills. The data input unit also provides a function for sharing training situation data with other users and jointly creating training scenarios. For example, a scenario is generated in which a team conducts cooking training. The data input unit also develops a system for sharing training situation data and jointly generating training scenarios with other users. For example, a scenario is generated in which multiple users cooperate to confirm evacuation routes. This allows training scenarios to be jointly generated with other users.

[0081] The data input unit can use the emotion estimation function to analyze the emotion of the user when entering data in real time and provide input support that draws out positive emotions. The data input unit, for example, uses the emotion estimation function to analyze the emotion of the user when entering data in real time and provide input support that draws out positive emotions. For example, if the user is nervous, a message that helps the user to relax is displayed. The data input unit also monitors the user's emotions in real time and provides input support that draws out positive emotions. For example, if the user is having fun, an encouraging message is displayed. The data input unit also uses the emotion estimation function to analyze the emotion of the user when entering data and provide an interface that draws out positive emotions. For example, if the user is tired, an encouraging message is displayed. This makes it possible to draw out positive emotions when the user enters data.

[0082] The environment generation unit can track the user's movements in real time and dynamically change the environment in response to the movements. The environment generation unit, for example, builds a system that tracks the user's movements in real time and dynamically changes the training environment in response to the movements. For example, if the user changes their evacuation route, an environment corresponding to that route is generated. The environment generation unit also dynamically adjusts the training environment in response to the user's movements using movement tracking technology. For example, if the user changes their cooking utensils, an environment corresponding to that movement is generated. The environment generation unit also develops a system that monitors the user's movements in real time and dynamically changes the training environment in response to that movement. For example, if the user changes their evacuation route, an environment corresponding to that route is generated. This makes it possible to dynamically change the training environment in response to the user's movements.

[0083] The environment generation unit can reflect the user's past training data and generate an environment that corresponds to the individual progress. The environment generation unit, for example, builds a system that generates a training environment that corresponds to the individual progress based on the user's past training data. For example, it reflects the results of past evacuation drills and generates an environment that strengthens the user's weak points. The environment generation unit also provides a training environment that corresponds to the user's progress based on the training data. For example, it generates an environment that focuses on practicing procedures that the user failed in cooking training in the past. The environment generation unit also develops a system that reflects the user's past training data and generates a training environment that corresponds to the individual needs. For example, it generates a training environment to improve a specific skill. In this way, it is possible to generate an environment that corresponds to the individual progress based on the user's past training data.

[0084] The environment generation unit can use the emotion estimation function to adjust the color and sound effects of the environment according to the emotional state of the user. For example, the environment generation unit uses the emotion estimation function to build a system that adjusts the color and sound effects of the environment according to the emotional state of the user. For example, if the user is relaxed, calm colors and music are provided. The environment generation unit also monitors the user's emotional state in real time and adjusts the color and sound effects of the environment based on the data. For example, if the user is tense, relaxing colors and music are provided. The environment generation unit also uses the emotion estimation function to develop a system that dynamically adjusts the color and sound effects of the environment according to the emotional state of the user. For example, if the user is having fun, bright colors and music are provided. In this way, the color and sound effects of the environment can be adjusted according to the emotional state of the user.

[0085] The environment generation unit can make the training environment available on different devices, making it multi-platform compatible. For example, the environment generation unit builds a system that makes the training environment available on smartphones and tablets, achieving multi-platform compatibility. For example, it makes it possible to run a training scenario created on a PC on a smartphone. The environment generation unit also develops a system that makes the training environment available on different devices, allowing users to train on any device. For example, it runs a training scenario on a tablet and checks the results on a smartphone. The environment generation unit also makes the training environment multi-platform compatible, providing a system that allows seamless training on different devices. For example, training can be started on a PC and continued on a smartphone. This allows the training environment to be used on different devices.

[0086] The environment generation unit can add a collaborative training mode to the training environment that can be shared with other users in real time. The environment generation unit, for example, adds a collaborative training mode to the training environment that can be shared with other users in real time, and builds a system in which multiple users can train simultaneously. For example, multiple users can cooperate to conduct evacuation drills. The environment generation unit also adds the collaborative training mode and develops a system in which a training environment can be shared with other users in real time. For example, multiple users can practice cooking simultaneously. The environment generation unit also adds the collaborative training mode to the training environment, and provides a system in which a training environment can be trained with other users in real time. For example, multiple users can cooperate to check evacuation routes. This allows the training environment to be shared with other users in real time.

[0087] The environment generation unit can use the emotion estimation function to analyze the user's emotional reactions in the training environment and propose optimal environment settings. For example, the environment generation unit uses the emotion estimation function to analyze the user's emotional reactions in the training environment in real time and propose optimal environment settings based on the data. For example, if the user is nervous, the environment generation unit proposes an environment setting that allows the user to relax. The environment generation unit also builds a system that monitors the user's emotional reactions in real time and dynamically adjusts the training environment settings based on the data. For example, if the user is enjoying themselves, the difficulty of training is increased. The environment generation unit also uses the emotion estimation function to develop a system that analyzes the user's emotional reactions in the training environment and proposes optimal environment settings. For example, if the user is tired, the environment generation unit proposes an environment setting that includes breaks. This makes it possible to propose optimal environment settings based on the user's emotional reactions.

[0088] The procedure generation unit can customize the training procedure according to the user's learning style. For example, the procedure generation unit analyzes the user's learning style and builds a system that customizes the training procedure according to the user's visual, auditory, and tactile senses. For example, a visual guide is provided to a user with a visual learning style. The procedure generation unit also customizes the training procedure according to the learning style. For example, an audio guide is provided to a user with an auditory learning style. The procedure generation unit also monitors the user's learning style in real time and develops a system that customizes the training procedure based on that data. For example, tactile feedback is provided to a user with a tactile learning style. This makes it possible to customize the training procedure according to the user's learning style.

[0089] The procedure generation unit can reflect the user's past training data and generate procedures according to individual progress. The procedure generation unit, for example, builds a system that generates training procedures according to individual progress based on the user's past training data. For example, it reflects the results of past evacuation drills and generates procedures to strengthen areas in which the user is weak. The procedure generation unit also provides training procedures according to the user's progress based on the training data. For example, it generates a procedure to focus on practicing procedures that the user failed in cooking training in the past. The procedure generation unit also develops a system that reflects the user's past training data and generates training procedures according to individual needs. For example, it generates a training procedure to improve a specific skill. In this way, it is possible to generate procedures according to individual progress based on the user's past training data.

[0090] The procedure generation unit can use the emotion estimation function to dynamically adjust the difficulty of a procedure according to the emotional state of the user. The procedure generation unit, for example, uses the emotion estimation function to build a system that dynamically adjusts the difficulty of a procedure according to the emotional state of the user. For example, if the user is nervous, the difficulty of the procedure is lowered. The procedure generation unit also monitors the user's emotional state in real time and adjusts the difficulty of the procedure based on the data. For example, if the user is enjoying themselves, the difficulty of the procedure is increased. The procedure generation unit also uses the emotion estimation function to develop a system that dynamically adjusts the difficulty of a procedure according to the emotional state of the user. For example, if the user is tired, the difficulty of the procedure is lowered. This makes it possible to dynamically adjust the difficulty of a procedure according to the emotional state of the user.

[0091] The procedure generation unit can automatically translate training procedures into different languages ​​to accommodate international users. The procedure generation unit, for example, builds a system that automatically translates training procedures into different languages ​​to accommodate international users. For example, it translates into multiple languages ​​such as English, French, and Chinese. The procedure generation unit also uses an automatic translation function to make training procedures multilingual and develop a system that can be used by international users. For example, it translates training procedures in real time and provides them to users. The procedure generation unit also builds a system that automatically translates training procedures into different languages ​​to obtain feedback from an international perspective. For example, it collects opinions from international users based on the translated procedures. This allows automatic translation into different languages ​​to accommodate international users.

[0092] The procedure generation unit can share a training procedure with other users and generate a procedure for conducting training jointly. The procedure generation unit, for example, builds a system for sharing a training procedure with other users and generating a procedure for conducting training jointly. For example, it generates a procedure for multiple users to conduct evacuation drills simultaneously. The procedure generation unit also generates a joint training procedure and develops a system that can conduct training in real time with other users. For example, it generates a procedure for multiple users to conduct cooking training simultaneously. The procedure generation unit also provides a system for sharing a training procedure and generating a procedure for conducting training jointly with other users. For example, it generates a procedure for multiple users to cooperate in checking evacuation routes. In this way, it is possible to generate a procedure for conducting training jointly with other users.

[0093] The procedure generation unit can use the emotion estimation function to identify a procedure that will evoke the most positive emotion in the user and provide that procedure preferentially. The procedure generation unit, for example, uses the emotion estimation function to identify a procedure that will evoke the most positive emotion in the user and builds a system that provides that procedure preferentially. For example, a procedure that the user enjoys is provided preferentially. The procedure generation unit also monitors the user's emotional response in real time and provides a procedure that elicits positive emotion based on that data. For example, a procedure that makes the user feel relaxed is provided preferentially. The procedure generation unit also uses the emotion estimation function to identify a procedure that will evoke the most positive emotion in the user and develops a system that provides that procedure preferentially. For example, a procedure that the user enjoys is provided preferentially. This makes it possible to provide a procedure that will evoke the most positive emotion in the user preferentially.

[0094] The training department can track the user's movements in real time during training and provide instant feedback according to the movements. For example, the training department builds a system that tracks the user's movements in real time during training and provides instant feedback according to the movements. For example, during an evacuation drill, it evaluates in real time whether the correct evacuation route is being followed. The training department also uses movement tracking technology to provide instant feedback according to the user's movements. For example, during cooking training, it evaluates in real time whether the correct cooking procedures are being performed. The training department also develops a system that monitors the user's movements in real time and provides instant feedback according to the movements. For example, during an evacuation drill, it evaluates in real time whether the correct evacuation route is being followed. This makes it possible to provide instant feedback according to the user's movements.

[0095] The instructor monitors the user's biometric information in real time during training and can provide instruction according to the stress level. The instructor, for example, builds a system that monitors the user's biometric information in real time during training and provides instruction according to the stress level based on that data. For example, if the stress level is high, the difficulty of the training is lowered. The instructor also provides instruction according to the user's stress level based on the biometric information. For example, if the heart rate increases, the instructor provides instruction to take a break. The instructor also develops a system that monitors the user's biometric information in real time and provides instruction according to the stress level based on that data. For example, if the stress level is low, the difficulty of the training is increased. This makes it possible to provide instruction according to the user's stress level.

[0096] The instructor can use the emotion estimation function to dynamically adjust the instruction method according to the user's emotional state. The instructor, for example, uses the emotion estimation function to build a system that dynamically adjusts the instruction method according to the user's emotional state. For example, if the user is nervous, it provides an instruction method that helps the user relax. The instructor also monitors the user's emotional state in real time and adjusts the instruction method based on the data. For example, if the user is enjoying themselves, it increases the difficulty of the training. The instructor also uses the emotion estimation function to develop a system that dynamically adjusts the instruction method according to the user's emotional state. For example, if the user is tired, it provides an instruction method that includes breaks. This makes it possible to dynamically adjust the instruction method according to the user's emotional state.

[0097] The instructor can make it possible for training instruction to be received on different devices. For example, the instructor can build a system that allows training instruction to be received on smart glasses or wearable devices, thereby achieving multi-platform compatibility. For example, evacuation training instruction is received using smart glasses. The instructor can also develop a system that allows training instruction to be received on different devices, allowing users to train on any device. For example, cooking training instruction is received using a wearable device. The instructor can also make training instruction multi-platform compatible, providing a system that allows training to be conducted seamlessly on different devices. For example, training can be started on smart glasses and continued on a wearable device. This allows training instruction to be received on different devices.

[0098] The instructor can share training instruction with other users in real time and provide instruction for conducting joint training. The instructor, for example, builds a system for sharing training instruction with other users in real time and providing instruction for conducting joint training. For example, instruction is provided for multiple users to conduct evacuation training simultaneously. The instructor also develops a system for providing joint training instruction and enabling training with other users in real time. For example, instruction is provided for multiple users to conduct cooking training simultaneously. The instructor also provides a system for sharing training instruction and providing instruction for conducting joint training with other users. For example, instruction is provided for multiple users to cooperate in checking evacuation routes. This allows training instruction to be shared with other users in real time.

[0099] The teaching unit can use the emotion estimation function to identify the teaching method that will evoke the most positive emotions in the user and provide that method preferentially. The teaching unit, for example, uses the emotion estimation function to build a system that identifies the teaching method that will evoke the most positive emotions in the user and provides that method preferentially. For example, a teaching method that the user enjoys is provided preferentially. The teaching unit also monitors the user's emotional reactions in real time and provides a teaching method that elicits positive emotions based on that data. For example, a teaching method that makes the user relaxed is provided preferentially. The teaching unit also uses the emotion estimation function to develop a system that identifies the teaching method that will evoke the most positive emotions in the user and provides that method preferentially. For example, a teaching method that the user enjoys is provided preferentially. This makes it possible to provide a teaching method that will evoke the most positive emotions in the user preferentially.

[0100] The evaluation unit can compare the training results with the user's past training data and evaluate the progress in detail. The evaluation unit, for example, compares the training results with the user's past training data and builds a system that evaluates the progress in detail. For example, the user's progress is evaluated by comparing with the results of past evacuation drills. The evaluation unit also evaluates the user's progress in detail based on the past training data. For example, it evaluates whether a procedure that previously failed in cooking training has been improved. The evaluation unit also develops a system that compares the training results with the user's past training data and evaluates the progress in detail. For example, the user's progress is evaluated by comparing with the results of past evacuation drills. This makes it possible to evaluate the progress in detail by comparing with the user's past training data.

[0101] The evaluation unit can compare the training results with the user's biometric information and reflect the stress level and concentration level in the evaluation. The evaluation unit, for example, builds a system that compares the training results with the user's biometric information and reflects the stress level and concentration level in the evaluation. For example, the evaluation unit evaluates the training results based on the heart rate and stress level. The evaluation unit also reflects the user's stress level and concentration level in the evaluation based on the biometric information. For example, if the heart rate increases, the difficulty of the training is reduced. The evaluation unit also develops a system that compares the training results with the user's biometric information and reflects the stress level and concentration level in the evaluation. For example, the training results are evaluated based on the heart rate and stress level. This makes it possible to reflect the stress level and concentration level in the evaluation based on the user's biometric information.

[0102] The evaluation unit can use the emotion estimation function to make an evaluation based on the user's emotional state and provide positive feedback. The evaluation unit, for example, uses the emotion estimation function to make an evaluation based on the user's emotional state and build a system that provides positive feedback. For example, if the user is enjoying themselves, positive feedback is provided. The evaluation unit also monitors the user's emotional state in real time and makes an evaluation based on that data. For example, if the user is relaxed, positive feedback is provided. The evaluation unit also uses the emotion estimation function to make an evaluation based on the user's emotional state and develop a system that provides positive feedback. For example, if the user is enjoying themselves, positive feedback is provided. This makes it possible to provide positive feedback based on the user's emotional state.

[0103] The evaluation unit can make the training results available on different devices, making it multi-platform compatible. For example, the evaluation unit builds a system that allows training results to be viewed on smartphones and tablets, achieving multi-platform compatibility. For example, it makes it possible to view the results of training conducted on a PC on a smartphone. The evaluation unit also develops a system that allows training results to be viewed on different devices, allowing users to view training results on any device. For example, the training results can be viewed on a tablet and the details can be viewed on a smartphone. The evaluation unit also makes the training results available on multiple platforms, providing a system that allows training results to be viewed seamlessly on different devices. For example, training can be conducted on a PC and the results can be viewed on a smartphone. This allows training results to be viewed on different devices.

[0104] The evaluation unit can add a function for sharing training results with other users and conducting collaborative evaluations. The evaluation unit, for example, builds a system that adds a function for sharing training results with other users and conducting collaborative evaluations. For example, multiple users simultaneously evaluate the results of evacuation training. The evaluation unit also adds a collaborative evaluation function and develops a system that allows training results to be shared with other users in real time. For example, multiple users simultaneously evaluate the results of cooking training. The evaluation unit also provides a system that provides a function for sharing training results and conducting collaborative evaluations with other users. For example, multiple users cooperatively evaluate the results of evacuation route confirmation. This allows training results to be evaluated collaboratively with other users.

[0105] The evaluation unit can use the emotion estimation function to identify the evaluation method that evokes the most positive emotion in the user and provide that method preferentially. The evaluation unit, for example, uses the emotion estimation function to build a system that identifies the evaluation method that evokes the most positive emotion in the user and provides that method preferentially. For example, an evaluation method that the user enjoys is provided preferentially. The evaluation unit also monitors the user's emotional reactions in real time and provides an evaluation method that elicits positive emotion based on that data. For example, an evaluation method that makes the user feel relaxed is provided preferentially. The evaluation unit also uses the emotion estimation function to develop a system that identifies the evaluation method that evokes the most positive emotion in the user and provides that method preferentially. For example, an evaluation method that the user enjoys is provided preferentially. This makes it possible to provide the evaluation method that evokes the most positive emotion in the user preferentially.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The data input unit inputs the user's training situation. For example, the user inputs the situation he or she wants to train in text format. The data input unit can also use voice recognition technology to allow the user to specify a training scenario in natural language. The environment generation unit generates a training environment based on the data input by the data input unit. For example, in the case of a fire evacuation drill, a burning building and an evacuation route are generated. The environment generation unit can also track the user's movements in real time and dynamically change the environment according to the movements. The procedure generation unit generates a training procedure based on the training environment generated by the environment generation unit. For example, in a fire evacuation drill, a procedure is generated that includes checking the evacuation route and points to note when evacuating. The procedure generation unit can also customize the training procedure according to the user's learning style. The instructor provides training guidance based on the training procedure generated by the procedure generation unit. For example, in a fire evacuation drill, specific instructions such as "Open this door next" are given. The instructor can also track the user's movements in real time during the training and provide immediate feedback according to the movements. The evaluation unit evaluates the training results provided by the instructor. For example, in a fire evacuation drill, the evaluation unit evaluates whether the user followed the correct evacuation route. The evaluation unit can also compare the training results with the user's past training data to evaluate the user's progress in detail. This allows the VR training system according to the embodiment to provide a VR training environment based on the user's training status and provide training guidance and evaluation.

[0108] The data input unit can refer to the user's past training data and generate a customized training scenario based on the user's individual training history. For example, it can refer to the results of past evacuation drills and generate a scenario that strengthens the user's weak points. The data input unit also evaluates the user's progress based on the training history and customizes the next training scenario. For example, it can generate a scenario that focuses on practicing procedures in cooking training that the user previously failed at. The data input unit also suggests training scenarios that meet the user's individual needs based on the user's training history. For example, it can generate a training scenario to improve a specific skill and provide it to the user. This makes it possible to provide a customized training scenario based on the user's past training data.

[0109] The data input unit can collect the user's biometric information in real time and dynamically adjust the training scenario based on that information. For example, the user's heart rate and stress level can be monitored in real time, and the training scenario can be adjusted based on that data. For example, if the stress level is high, the difficulty level of the training can be lowered. The data input unit also provides a training scenario based on the user's physical condition and mental state based on the biometric information. For example, if the heart rate increases, a scenario that includes breaks can be generated. The data input unit also collects the user's biometric information in real time and dynamically changes the training scenario based on that data. For example, if the stress level is low, the difficulty level of the training can be increased. This allows the training scenario to be dynamically adjusted based on the user's biometric information.

[0110] The data input unit can use the emotion estimation function to analyze the user's current emotional state and propose an optimal training scenario. For example, the emotion estimation function can be used to analyze the user's facial expressions and voice to identify the user's current emotional state. For example, if the user is nervous, a training scenario that will help them relax can be proposed. The data input unit can also monitor the user's emotional state in real time and adjust the training scenario based on that data. For example, if the user is enjoying themselves, the difficulty of the training can be increased. The data input unit can also use the emotion estimation function to generate a training scenario that corresponds to the user's emotional state. For example, if the user is tired, a short and effective training scenario can be proposed. This makes it possible to propose an optimal training scenario based on the user's emotional state.

[0111] The data input unit can use voice recognition technology to enable the user to specify a training scenario in natural language. For example, a system can be built using voice recognition technology to enable the user to specify a training scenario in natural language. For example, a training scenario can be generated simply by speaking, "I would like to conduct a fire evacuation drill." The data input unit also enables the user to input a training scenario by voice, and the generation AI analyzes the voice data to generate a training scenario. For example, a kitchen environment can be generated simply by speaking, "I would like to practice cooking." The data input unit also uses voice recognition technology to provide an interface that enables the user to specify a training scenario in natural language. For example, an evacuation drill scenario can be generated simply by speaking, "I would like to check the evacuation route." This allows the user to specify a training scenario in natural language.

[0112] The data input unit can add a function to share training situation data input with other users and generate a joint training scenario. For example, a system is constructed in which training situation data input is shared with other users and training scenarios are jointly generated. For example, a scenario is generated in which multiple users simultaneously conduct evacuation drills. The data input unit also provides a function to share training situation data with other users and jointly create training scenarios. For example, a scenario is generated in which a team conducts cooking training. The data input unit also develops a system in which training situation data is shared and training scenarios are jointly generated with other users. For example, a scenario is generated in which multiple users cooperate to confirm evacuation routes. This allows training scenarios to be jointly generated with other users.

[0113] The data input unit can use the emotion estimation function to analyze the emotion of the user when entering data in real time and provide input support that draws out positive emotions. For example, the emotion estimation function can be used to analyze the emotion of the user when entering data in real time and provide input support that draws out positive emotions. For example, if the user is nervous, a message that helps the user to relax can be displayed. The data input unit can also monitor the user's emotions in real time and provide input support that draws out positive emotions. For example, if the user is having fun, an encouraging message can be displayed. The data input unit can also use the emotion estimation function to analyze the emotion of the user when entering data and provide an interface that draws out positive emotions. For example, if the user is tired, an encouraging message can be displayed. This can draw out positive emotions when the user enters data.

[0114] The environment generation unit can track the user's movements in real time and dynamically change the environment in response to the movements. For example, a system is constructed that tracks the user's movements in real time and dynamically changes the training environment in response to the movements. For example, if the user changes their evacuation route, an environment is generated that corresponds to that route. The environment generation unit also uses movement tracking technology to dynamically adjust the training environment in response to the user's movements. For example, if the user changes their evacuation route, an environment is generated that corresponds to that movement. The environment generation unit also monitors the user's movements in real time and develops a system that dynamically changes the training environment in response to that movement. For example, if the user changes their evacuation route, an environment is generated that corresponds to that route. This makes it possible to dynamically change the training environment in response to the user's movements.

[0115] The environment generation unit can reflect the user's past training data and generate an environment that corresponds to the individual progress. For example, a system is constructed that generates a training environment that corresponds to the individual progress based on the user's past training data. For example, an environment is generated that reflects the results of past evacuation drills and strengthens the user's weak points. The environment generation unit also provides a training environment that corresponds to the user's progress based on the training data. For example, an environment is generated that focuses on practicing procedures that the user failed in the past in cooking training. The environment generation unit also develops a system that reflects the user's past training data and generates a training environment that corresponds to the individual needs. For example, a training environment is generated to improve a specific skill. In this way, an environment can be generated that corresponds to the individual progress based on the user's past training data.

[0116] The environment generation unit can use the emotion estimation function to adjust the color and sound effects of the environment according to the emotional state of the user. For example, a system is constructed that uses the emotion estimation function to adjust the color and sound effects of the environment according to the emotional state of the user. For example, if the user is relaxed, calm colors and music are provided. The environment generation unit also monitors the user's emotional state in real time and adjusts the color and sound effects of the environment based on the data. For example, if the user is tense, relaxing colors and music are provided. The environment generation unit also uses the emotion estimation function to develop a system that dynamically adjusts the color and sound effects of the environment according to the emotional state of the user. For example, if the user is having fun, bright colors and music are provided. This makes it possible to adjust the color and sound effects of the environment according to the emotional state of the user.

[0117] The environment generation unit can make the training environment available on different devices, making it multi-platform compatible. For example, a system can be built that makes the training environment available on smartphones and tablets, achieving multi-platform compatibility. For example, a training scenario created on a PC can be executed on a smartphone. The environment generation unit also develops a system that makes the training environment available on different devices, allowing users to train on any device. For example, a training scenario can be executed on a tablet and the results can be checked on a smartphone. The environment generation unit also makes the training environment multi-platform compatible, providing a system that allows seamless training on different devices. For example, training can be started on a PC and continued on a smartphone. This allows the training environment to be used on different devices.

[0118] The environment generation unit can add a collaborative training mode to the training environment, which can be shared with other users in real time. For example, a collaborative training mode can be added to the training environment, which can be shared with other users in real time, to build a system in which multiple users can train simultaneously. For example, multiple users can cooperate to conduct evacuation drills. The environment generation unit can also add a collaborative training mode to develop a system in which a training environment can be shared with other users in real time. For example, multiple users can practice cooking simultaneously. The environment generation unit can also add a collaborative training mode to the training environment, to provide a system in which a training environment can be trained with other users in real time. For example, multiple users can cooperate to check evacuation routes. This allows the training environment to be shared with other users in real time.

[0119] The environment generation unit can use the emotion estimation function to analyze the user's emotional reactions in the training environment and propose optimal environment settings. For example, the emotion estimation function can be used to analyze the user's emotional reactions in the training environment in real time and propose optimal environment settings based on the data. For example, if the user is nervous, the environment generation unit can propose an environment setting that allows the user to relax. The environment generation unit can also build a system that monitors the user's emotional reactions in real time and dynamically adjusts the training environment settings based on the data. For example, if the user is enjoying themselves, the difficulty of the training can be increased. The environment generation unit can also use the emotion estimation function to develop a system that analyzes the user's emotional reactions in the training environment and proposes optimal environment settings. For example, if the user is tired, the environment generation unit can propose an environment setting that includes breaks. This makes it possible to propose optimal environment settings based on the user's emotional reactions.

[0120] The procedure generation unit can customize the training procedure according to the user's learning style. For example, a system is constructed that analyzes the user's learning style and customizes the training procedure according to their visual, auditory, and tactile senses. For example, a visual guide is provided to a user with a visual learning style. The procedure generation unit also customizes the training procedure according to the learning style. For example, an audio guide is provided to a user with an auditory learning style. The procedure generation unit also monitors the user's learning style in real time and develops a system that customizes the training procedure based on that data. For example, tactile feedback is provided to a user with a tactile learning style. This makes it possible to customize the training procedure according to the user's learning style.

[0121] The procedure generation unit can reflect the user's past training data and generate procedures according to individual progress. For example, a system is constructed that generates training procedures according to individual progress based on the user's past training data. For example, a procedure is generated that reflects the results of past evacuation drills and strengthens areas in which the user is weak. The procedure generation unit also provides training procedures according to the user's progress based on the training data. For example, a procedure is generated that focuses on practicing procedures that the user failed in cooking training in the past. The procedure generation unit also develops a system that reflects the user's past training data and generates training procedures according to individual needs. For example, a training procedure is generated to improve a specific skill. In this way, procedures can be generated according to individual progress based on the user's past training data.

[0122] The procedure generation unit can use the emotion estimation function to dynamically adjust the difficulty of a procedure according to the emotional state of the user. For example, a system is constructed that uses the emotion estimation function to dynamically adjust the difficulty of a procedure according to the emotional state of the user. For example, if the user is nervous, the difficulty of the procedure is lowered. The procedure generation unit also monitors the user's emotional state in real time and adjusts the difficulty of the procedure based on the data. For example, if the user is enjoying themselves, the difficulty of the procedure is increased. The procedure generation unit also uses the emotion estimation function to develop a system that dynamically adjusts the difficulty of a procedure according to the emotional state of the user. For example, if the user is tired, the difficulty of the procedure is lowered. This makes it possible to dynamically adjust the difficulty of a procedure according to the emotional state of the user.

[0123] The procedure generation unit can automatically translate training procedures into different languages ​​to accommodate international users. For example, a system is built that automatically translates training procedures into different languages ​​to accommodate international users. For example, it translates into multiple languages ​​such as English, French, and Chinese. The procedure generation unit also uses an automatic translation function to make training procedures multilingual and develop a system that can be used by international users. For example, it translates training procedures in real time and provides them to users. The procedure generation unit also builds a system that automatically translates training procedures into different languages ​​to obtain feedback from an international perspective. For example, it collects opinions from international users based on the translated procedures. This allows automatic translation into different languages ​​to accommodate international users.

[0124] The procedure generation unit can share a training procedure with other users and generate a procedure for conducting training jointly. For example, a system is constructed that generates a procedure for sharing a training procedure with other users and conducting training jointly. For example, a procedure is generated for multiple users to conduct evacuation training simultaneously. The procedure generation unit also generates a joint training procedure and develops a system that can conduct training in real time with other users. For example, a procedure is generated for multiple users to conduct cooking training simultaneously. The procedure generation unit also provides a system that shares a training procedure and generates a procedure for conducting training jointly with other users. For example, a procedure is generated for multiple users to cooperate in checking evacuation routes. This makes it possible to generate a procedure for conducting training jointly with other users.

[0125] The procedure generation unit can use the emotion estimation function to identify a procedure that will evoke the most positive emotion in the user and provide that procedure preferentially. For example, a system can be constructed that uses the emotion estimation function to identify a procedure that will evoke the most positive emotion in the user and provide that procedure preferentially. For example, a procedure that the user enjoys is provided preferentially. The procedure generation unit can also monitor the user's emotional reactions in real time and provide a procedure that will elicit positive emotion based on that data. For example, a procedure that will relax the user is provided preferentially. The procedure generation unit can also use the emotion estimation function to identify a procedure that will evoke the most positive emotion in the user and provide that procedure preferentially. For example, a procedure that the user enjoys is provided preferentially. This allows the procedure that will evoke the most positive emotion in the user to be provided preferentially.

[0126] The instructor can track a user's movements in real time during training and provide instant feedback based on the movements. For example, a system is constructed that tracks a user's movements in real time during training and provides instant feedback based on those movements. For example, during an evacuation drill, it is evaluated in real time whether the correct evacuation route is being followed. The instructor can also use movement tracking technology to provide instant feedback based on the user's movements. For example, during cooking training, it is evaluated in real time whether the correct cooking procedures are being performed. The instructor can also develop a system that monitors a user's movements in real time and provides instant feedback based on those movements. For example, during an evacuation drill, it is evaluated in real time whether the correct evacuation route is being followed. This makes it possible to provide instant feedback based on the user's movements.

[0127] The instructor can monitor the user's biometric information in real time during training and provide instruction according to the stress level. For example, a system is constructed that monitors the user's biometric information in real time during training and provides instruction according to the stress level based on that data. For example, if the stress level is high, the difficulty of the training is lowered. The instructor also provides instruction according to the user's stress level based on the biometric information. For example, if the heart rate increases, the instructor can provide instruction to take a break. The instructor also develops a system that monitors the user's biometric information in real time and provides instruction according to the stress level based on that data. For example, if the stress level is low, the difficulty of the training is increased. This makes it possible to provide instruction according to the user's stress level.

[0128] The instructor can use the emotion estimation function to dynamically adjust the instruction method according to the user's emotional state. For example, a system is constructed that uses the emotion estimation function to dynamically adjust the instruction method according to the user's emotional state. For example, if the user is nervous, an instruction method that helps the user relax is provided. The instructor also monitors the user's emotional state in real time and adjusts the instruction method based on that data. For example, if the user is enjoying themselves, the difficulty of the training is increased. The instructor also develops a system that uses the emotion estimation function to dynamically adjust the instruction method according to the user's emotional state. For example, if the user is tired, an instruction method that includes breaks is provided. This makes it possible to dynamically adjust the instruction method according to the user's emotional state.

[0129] The instructor can make it possible for training instruction to be received on different devices. For example, a system can be built that allows training instruction to be received on smart glasses or wearable devices, achieving multi-platform compatibility. For example, evacuation drill instruction can be received using smart glasses. The instructor can also develop a system that allows training instruction to be received on different devices, allowing users to train on any device. For example, cooking training instruction can be received using a wearable device. The instructor can also make training instruction multi-platform compatible, providing a system that allows training to be conducted seamlessly on different devices. For example, training can be started on smart glasses and continued on a wearable device. This allows training instruction to be received on different devices.

[0130] The instructor can share training instruction with other users in real time and provide instruction for conducting joint training. For example, a system is constructed that shares training instruction with other users in real time and provides instruction for conducting joint training. For example, instruction is provided for multiple users to conduct evacuation training simultaneously. The instructor also develops a system that provides joint training instruction and allows training with other users in real time. For example, instruction is provided for multiple users to conduct cooking training simultaneously. The instructor also provides a system that shares training instruction and provides instruction for conducting joint training with other users. For example, instruction is provided for multiple users to cooperate in checking evacuation routes. This allows training instruction to be shared with other users in real time.

[0131] The teaching unit can use the emotion estimation function to identify the teaching method that elicits the most positive emotions from the user and provide that method preferentially. For example, a system can be constructed that uses the emotion estimation function to identify the teaching method that elicits the most positive emotions from the user and provide that method preferentially. For example, a teaching method that the user enjoys can be provided preferentially. The teaching unit can also monitor the user's emotional reactions in real time and provide a teaching method that elicits positive emotions based on that data. For example, a teaching method that makes the user feel relaxed can be provided preferentially. The teaching unit can also use the emotion estimation function to identify the teaching method that elicits the most positive emotions from the user and develop a system that provides that method preferentially. For example, a teaching method that the user enjoys can be provided preferentially. This allows the teaching method that elicits the most positive emotions from the user to be provided preferentially.

[0132] The evaluation unit can compare the training results with the user's past training data and evaluate the progress in detail. For example, a system is constructed that compares the training results with the user's past training data and evaluates the progress in detail. For example, the user's progress is evaluated by comparing with the results of past evacuation drills. The evaluation unit also evaluates the user's progress in detail based on the past training data. For example, it evaluates whether a procedure that previously failed in cooking training has been improved. The evaluation unit also develops a system that compares the training results with the user's past training data and evaluates the progress in detail. For example, the user's progress is evaluated by comparing with the results of past evacuation drills. This makes it possible to evaluate the progress in detail by comparing with the user's past training data.

[0133] The evaluation unit can compare the training results with the user's biometric information and reflect the stress level and concentration level in the evaluation. For example, a system is constructed that compares the training results with the user's biometric information and reflects the stress level and concentration level in the evaluation. For example, the training results are evaluated based on heart rate and stress level. The evaluation unit also reflects the user's stress level and concentration level in the evaluation based on the biometric information. For example, if the heart rate increases, the difficulty of the training is reduced. The evaluation unit also develops a system that compares the training results with the user's biometric information and reflects the stress level and concentration level in the evaluation. For example, the training results are evaluated based on heart rate and stress level. This makes it possible to reflect the stress level and concentration level in the evaluation based on the user's biometric information.

[0134] The evaluation unit can use the emotion estimation function to make an evaluation based on the user's emotional state and provide positive feedback. For example, a system is constructed using the emotion estimation function to make an evaluation based on the user's emotional state and provide positive feedback. For example, if the user is enjoying themselves, positive feedback is provided. The evaluation unit also monitors the user's emotional state in real time and makes an evaluation based on that data. For example, if the user is relaxed, positive feedback is provided. The evaluation unit also uses the emotion estimation function to make an evaluation based on the user's emotional state and develop a system to provide positive feedback. For example, if the user is enjoying themselves, positive feedback is provided. This makes it possible to provide positive feedback based on the user's emotional state.

[0135] The evaluation unit can make the training results available on different devices, making it multi-platform compatible. For example, a system can be built that allows training results to be viewed on smartphones and tablets, achieving multi-platform compatibility. For example, the results of training conducted on a PC can be viewed on a smartphone. The evaluation unit also develops a system that allows training results to be viewed on different devices, allowing users to view training results on any device. For example, the training results can be viewed on a tablet and the details can be viewed on a smartphone. The evaluation unit also makes the training results available on multiple platforms, providing a system that allows training results to be viewed seamlessly on different devices. For example, training can be conducted on a PC and the results can be viewed on a smartphone. This allows training results to be viewed on different devices.

[0136] The evaluation unit can add a function for sharing training results with other users and conducting collaborative evaluations. For example, a system is constructed that adds a function for sharing training results with other users and conducting collaborative evaluations. For example, multiple users simultaneously evaluate the results of evacuation training. The evaluation unit also adds a collaborative evaluation function and develops a system that allows training results to be shared with other users in real time. For example, multiple users simultaneously evaluate the results of cooking training. The evaluation unit also provides a system that provides a function for sharing training results and conducting collaborative evaluations with other users. For example, multiple users cooperatively evaluate the results of evacuation route confirmation. This allows training results to be evaluated collaboratively with other users.

[0137] The evaluation unit can use the emotion estimation function to identify the evaluation method that evokes the most positive emotion in the user and provide that method preferentially. For example, a system can be constructed that uses the emotion estimation function to identify the evaluation method that evokes the most positive emotion in the user and provide that method preferentially. For example, an evaluation method that the user enjoys is provided preferentially. The evaluation unit can also monitor the user's emotional reactions in real time and provide an evaluation method that elicits positive emotion based on that data. For example, an evaluation method that makes the user feel relaxed is provided preferentially. The evaluation unit can also use the emotion estimation function to identify the evaluation method that evokes the most positive emotion in the user and develop a system that provides that method preferentially. For example, an evaluation method that the user enjoys is provided preferentially. This makes it possible to provide the evaluation method that evokes the most positive emotion in the user preferentially.

[0138] The processing flow of the second embodiment will be briefly explained below.

[0139] Step 1: The data input unit inputs the user's training situation. For example, the user inputs the situation he or she wants to train in text format. The data input unit can also use voice recognition technology to allow the user to specify a training scenario in natural language. Step 2: The environment generation unit generates a training environment based on the data input by the data input unit. For example, in the case of a fire evacuation drill, a burning building and an evacuation route are generated. The environment generation unit can also track the user's movements in real time and dynamically change the environment according to the movements. Step 3: The procedure generation unit generates a training procedure based on the training environment generated by the environment generation unit. For example, in a fire evacuation drill, the procedure generated includes confirmation of evacuation routes and points to note when evacuating. The procedure generation unit can also customize the training procedure according to the user's learning style. Step 4: The instructor provides training based on the training procedure generated by the procedure generator. For example, in a fire evacuation drill, specific instructions such as "Open this door next" are given. The instructor can also track the user's movements in real time during the training and provide immediate feedback based on the movements. Step 5: The evaluator evaluates the training results provided by the instructor. For example, in a fire evacuation drill, the evaluator evaluates whether the evacuation route was followed correctly. The evaluator can also compare the training results with the user's past training data to evaluate progress in detail.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0144] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0159] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0160] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0161] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0162] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0164] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0165] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0168] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0170] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0172] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0180] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0181] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0182] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0183] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0184] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0185] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0187] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0189] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0190] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0191] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0192] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0193] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0194] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0195] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0196] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0197] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0198] 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.

[0199] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0200] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0201] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0202] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0203] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0204] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0205] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0206] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data input unit for inputting the user's training status; an environment generation unit that generates a training environment based on the data input by the data input unit; a procedure generation unit that generates a training procedure based on the training environment generated by the environment generation unit; an instruction unit that provides training guidance based on the training procedure generated by the procedure generation unit; an evaluation unit that evaluates the training results instructed by the instruction unit; A system characterized by:

2. The data input unit References the user's past training data and generates customized training scenarios based on individual training history 2. The system of claim 1.

3. The data input unit Collect user biometric information in real time and dynamically adjust training scenarios based on that information 2. The system of claim 1.

4. The data input unit Analyzes the user's current emotional state and suggests optimal training scenarios 2. The system of claim 1.

5. The data input unit Uses voice recognition technology to allow users to specify training scenarios in natural language 2. The system of claim 1.

6. The data input unit Add the ability to share training situation data input with other users and generate joint training scenarios 2. The system of claim 1.

7. The data input unit Analyzes the user's emotions in real time as they type, and provides input support that elicits positive emotions.

2. The system of claim 1.

Images