system

Abstract

The system according to this embodiment aims to provide an efficient and effective training program in supporting the treatment of autism spectrum disorder and Asperger's syndrome. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a monitoring unit, an adjustment unit, and an evaluation unit. The reception unit receives input to start training. The generation unit generates a training program based on the information received by the reception unit. The monitoring unit monitors the user's response and progress based on the training program generated by the generation unit. The adjustment unit adjusts the training content based on the information monitored by the monitoring unit. The evaluation unit records the user's training results and evaluates the progress based on the training content adjusted by the adjustment unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to provide an efficient and effective training program in the treatment support of autism spectrum disorder and Asperger's syndrome.

[0005] The system according to the embodiment aims to provide an efficient and effective training program in the treatment support of autism spectrum disorder and Asperger's syndrome.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a monitoring unit, an adjustment unit, and an evaluation unit. The reception unit receives input to start training. The generation unit generates a training program based on the information received by the reception unit. The monitoring unit monitors the user's response and progress based on the training program generated by the generation unit. The adjustment unit adjusts the training content based on the information monitored by the monitoring unit. The evaluation unit records the user's training results and evaluates the progress based on the training content adjusted by the adjustment unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an efficient and effective training program in supporting the treatment of autism spectrum disorder and Asperger's syndrome. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The training system according to an embodiment of the present invention is a training system for supporting the treatment of autism spectrum disorder and Asperger's syndrome. This training system can replace human-assisted autism support and training by utilizing generative AI. First, the user provides input to begin training. For example, if the user wishes to receive training to improve a specific social skill, they input the details. This information is input to the generative AI. Next, the generative AI analyzes the input information and generates an optimal training program for the user. The generative AI creates an individually customized training program based on past training data and the user's characteristics. For example, this may include simulations of conversations in specific social situations or training in emotion recognition. Based on the generated training program, the user begins training. The generative AI monitors the user's responses and progress in real time and adjusts the training content as needed. For example, if the user is having difficulty with a particular task, the generative AI breaks down the task and provides it in simpler steps. Furthermore, the generative AI records the user's training results and evaluates their progress. This allows the user to see their own progress and receive feedback to move on to the next step. For example, the generative AI can evaluate the user's improvement in conversational skills and suggest the next training program. This system will enable more efficient and effective training to support the treatment of autism spectrum disorder and Asperger's syndrome. Users can progress through training at their own pace and improve specific skills through individually customized training programs. In addition, the generating AI provides real-time feedback, so users always receive appropriate support. As a result, the training system can efficiently and effectively support training to support the treatment of autism spectrum disorder and Asperger's syndrome.

[0029] The training system according to this embodiment comprises a reception unit, a generation unit, a monitoring unit, an adjustment unit, and an evaluation unit. The reception unit receives input from the user to initiate training. This input may include, but is not limited to, text input, voice input, or multiple-choice input. For example, the reception unit can receive input from the user if they wish to receive training to improve a specific social skill. The generation unit uses a generation AI to generate a training program based on the information received by the reception unit. The generation AI creates a customized training program based on past training data and the user's characteristics. For example, the generation AI generates a training program that includes simulations of conversations in specific social situations or training in emotion recognition. The monitoring unit monitors the user's reactions and progress based on the training program generated by the generation unit. The monitoring unit can measure, for example, the user's reaction speed, accuracy rate, and progress. The adjustment unit adjusts the training content based on the information monitored by the monitoring unit. For example, if the user is having difficulty with a particular task, the adjustment unit can break down the task and provide it in simpler steps. The evaluation unit records the user's training results and evaluates their progress based on the training content adjusted by the adjustment unit. For example, the evaluation unit can evaluate the user's improvement in conversational skills and propose the next training program. This enables the training system according to the embodiment to efficiently initiate, generate, monitor, adjust, and evaluate the user's training.

[0030] The reception unit can analyze the user's past training history and select an input method. For example, if the user has preferred using voice input in the past, the reception unit will prioritize suggesting voice input. Alternatively, if the user has frequently used text input in the past, the reception unit can set text input as the default input method. Furthermore, if the user has previously trained during a specific time period, the reception unit can suggest starting training during that time period. This allows for the selection of the most user-friendly input method based on the user's past training history. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's past training history data into a generating AI and have the generating AI select the optimal input method.

[0031] The reception unit can filter training content based on the user's lifestyle and areas of interest at the start of training. For example, if the user is experiencing stress in their current lifestyle, the reception unit can suggest training content that promotes relaxation. The reception unit can also prioritize suggesting training content related to a specific area of ​​interest if the user is interested in that area. Furthermore, if the user has limited time in their current lifestyle, the reception unit can suggest short, effective training content. This allows for the provision of more appropriate training content by filtering it based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0032] The reception desk can prioritize accepting training content that is highly relevant to the user based on their geographical location at the start of training. For example, if the user is in a specific region, the reception desk will prioritize suggesting training content related to that region. Similarly, if the user is traveling, the reception desk can prioritize suggesting training content related to their travel destination. Furthermore, if the user is at home, the reception desk can prioritize suggesting training content that can be done at home. This allows for the provision of more appropriate training content by providing highly relevant training based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant training content.

[0033] The reception desk can analyze the user's social media activity at the start of training and accept relevant training content. For example, if the user shows interest in a particular topic on social media, the reception desk can suggest training content related to that topic. It can also suggest relaxing training content if the user is experiencing stress on social media. Furthermore, if the user indicates on social media that they want to learn a new skill, the reception desk can suggest training content related to that skill. This allows for more appropriate training content to be provided based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI select relevant training content.

[0034] The generation unit can adjust the program details based on the importance of the training when generating the training program. For example, the generation unit may include detailed explanations and step-by-step guides for high-importance training programs. Alternatively, it may include concise explanations and key points for low-importance training programs. Furthermore, it may include a moderate level of detail and specific examples for training programs of moderate importance. This allows for the provision of more appropriate training programs by adjusting the program detail based on the importance of the training. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input training importance data into a generation AI and have the generation AI perform the adjustment of program detail.

[0035] The generation unit can apply a generation algorithm according to the training category when generating a training program. For example, the generation unit can apply a dialogue simulation generation algorithm to a social skills training program. It can also apply a facial expression analysis generation algorithm to an emotion recognition training program. Furthermore, it can apply a scenario-based generation algorithm to a problem-solving skills training program. By applying different generation algorithms depending on the training category, a more appropriate training program can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input training category data into a generation AI and have the generation AI perform the application of the generation algorithm.

[0036] The generation unit can determine the priority of training programs based on the training submission timing when generating training programs. For example, the generation unit can prioritize the generation of training programs with high urgency. It can also prioritize the generation of training programs with approaching submission deadlines. Furthermore, it can postpone the generation of training programs with distant submission deadlines. This allows for the provision of more appropriate training programs by prioritizing programs based on the training submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input training submission timing data into a generation AI and have the generation AI perform the determination of program priorities.

[0037] The generation unit can adjust the order of training programs based on their relevance during program generation. For example, the generation unit can prioritize generating highly relevant training programs. It can also postpone the generation of less relevant training programs. Furthermore, it can generate training programs of moderate relevance in an appropriate order. By adjusting the program order based on training relevance, it is possible to provide more appropriate training programs. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input training relevance data into a generation AI and have the generation AI perform the program order adjustment.

[0038] The monitoring unit can improve the accuracy of monitoring based on the interrelationships of the training during monitoring. For example, the monitoring unit can analyze the interrelationships of the training and monitor related training content together. The monitoring unit can also adjust the timing of monitoring by considering the interrelationships of the training. Furthermore, the monitoring unit can set monitoring criteria based on the interrelationships of the training. This improves the accuracy of monitoring by considering the interrelationships of the training. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the interrelationship data of the training into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0039] The monitoring unit can perform monitoring based on the attribute information of the training submitter during monitoring. For example, the monitoring unit can set monitoring criteria by considering the age and gender of the training submitter. The monitoring unit can also improve the accuracy of monitoring by referring to the training submitter's past training history. Furthermore, the monitoring unit can customize the monitoring method based on the characteristics of the training submitter. This makes it possible to perform more appropriate monitoring by considering the attribute information of the training submitter. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the attribute information data of the training submitter into a generating AI and have the generating AI perform the setting of monitoring criteria.

[0040] The monitoring unit can perform monitoring based on the geographical distribution of training during monitoring. For example, the monitoring unit can analyze the geographical distribution of training and set monitoring criteria based on the characteristics of each region. The monitoring unit can also adjust the timing of monitoring, for example, by considering the geographical distribution of training. Furthermore, the monitoring unit can customize the monitoring method, for example, based on the geographical distribution of training. This makes it possible to perform more appropriate monitoring by considering the geographical distribution of training. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input geographical distribution data of training into a generating AI and have the generating AI perform the setting of monitoring criteria.

[0041] The monitoring unit can improve the accuracy of monitoring based on relevant training literature during monitoring. For example, the monitoring unit can refer to relevant training literature and set monitoring criteria based on the latest research findings. The monitoring unit can also improve the monitoring method by considering relevant training literature. Furthermore, the monitoring unit can improve the accuracy of monitoring based on relevant training literature. As a result, the accuracy of monitoring is improved by referring to relevant training literature. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant training literature data into a generating AI and have the generating AI perform the setting of monitoring criteria.

[0042] The adjustment unit can select an adjustment method based on the user's past training history when adjusting the training content. For example, the adjustment unit can adjust the training content by referring to training methods that the user has succeeded with in the past. The adjustment unit can also adjust the training content to avoid training content that the user has found difficult in the past. Furthermore, the adjustment unit can select and adjust the optimal training content from the user's past training history. This makes it possible to adjust the training content more appropriately by referring to the user's past training history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's past training history data into a generating AI and have the generating AI perform the selection of an adjustment method.

[0043] The adjustment unit can customize the means of adjustment based on the user's current living situation when adjusting the training content. For example, if the user is feeling stressed in their current living situation, the adjustment unit can adjust the training content to help them relax. The adjustment unit can also adjust the training content to be effective in a short amount of time if the user has limited time in their current living situation. Furthermore, if the user has an interest in a particular area of ​​interest in their current living situation, the adjustment unit can adjust the training content to be related to that area. This allows for the provision of more appropriate training content by customizing it based on the user's current living situation. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the adjustment means.

[0044] The adjustment unit can select an adjustment method based on the user's geographical location information when adjusting training content. For example, if the user is in a specific region, the adjustment unit will adjust the training content to be relevant to that region. It can also adjust the training content to be relevant to the user's travel destination if the user is traveling. Furthermore, if the user is at home, the adjustment unit can adjust the training content to be suitable for use at home. This allows for the provision of more appropriate training content by adjusting it based on the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, or without AI. For example, the adjustment unit can input the user's geographical location data into a generating AI and have the generating AI select the adjustment method.

[0045] The adjustment unit can analyze the user's social media activity and propose adjustment methods when adjusting training content. For example, if the user shows interest in a particular topic on social media, the adjustment unit can adjust the training content to be related to that topic. Also, if the user is feeling stressed on social media, the adjustment unit can adjust the training content to be relaxing. Furthermore, if the user indicates on social media that they want to learn a new skill, the adjustment unit can adjust the training content to be related to that skill. In this way, by adjusting the training content based on the user's social media activity, more appropriate training content can be provided. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media activity data into a generating AI and have the generating AI execute suggestions for adjustment methods.

[0046] The evaluation unit can adjust the evaluation algorithm based on past evaluation data when evaluating training results. For example, the evaluation unit can analyze past evaluation data and select the optimal evaluation algorithm. The evaluation unit can also adjust the evaluation criteria based on past evaluation data. Furthermore, the evaluation unit can improve the accuracy of the evaluation by referring to past evaluation data. This improves the accuracy of the evaluation algorithm by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past evaluation data into a generating AI and have the generating AI perform the adjustment of the evaluation algorithm.

[0047] The evaluation unit can customize the evaluation methods based on the user's characteristics when evaluating training results. For example, the evaluation unit can set evaluation criteria considering the user's age and gender. The evaluation unit can also improve the accuracy of the evaluation by referring to the user's past training history. Furthermore, the evaluation unit can customize the evaluation method based on the user's characteristics. By customizing the evaluation methods based on the user's characteristics, a more appropriate evaluation becomes possible. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user characteristic data into a generating AI and have the generating AI perform the customization of the evaluation methods.

[0048] The evaluation unit can select an evaluation method based on the user's geographical location information when evaluating training results. For example, if the user is in a specific region, the evaluation unit can select an evaluation method relevant to that region. It can also select an evaluation method relevant to the user's travel destination if the user is traveling. Furthermore, if the user is at home, it can select an evaluation method that can be performed at home. By selecting an evaluation method based on the user's geographical location information, a more appropriate evaluation becomes possible. Some or all of the above processing in the evaluation unit may be performed using AI, or without AI. For example, the evaluation unit can input the user's geographical location data into a generating AI and have the generating AI select the evaluation method.

[0049] The evaluation unit can analyze the user's social media activity and propose evaluation methods when evaluating training results. For example, if the user shows interest in a particular topic on social media, the evaluation unit can propose an evaluation method related to that topic. Furthermore, if the user is experiencing stress on social media, the evaluation unit can propose a relaxing evaluation method. Also, if the user indicates a desire to learn a new skill on social media, the evaluation unit can propose an evaluation method related to that skill. This allows for more appropriate evaluation by proposing evaluation methods based on the user's social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input the user's social media activity data into a generating AI and have the generating AI propose evaluation methods.

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

[0051] The reception desk can acquire the user's biometric information and assess the user's health status before training begins. For example, the reception desk can measure the user's heart rate and blood pressure, and temporarily suspend training if abnormalities are detected. It can also analyze the user's sleep data and suggest lighter training content if the user hasn't had enough rest. Furthermore, the reception desk can refer to the user's dietary data and display a message encouraging nutritional supplementation if their nutritional status is insufficient. This allows for safer and more effective training by adjusting the training content based on the user's health status.

[0052] The monitoring unit can detect the user's posture during training and prompt correction if it is poor. For example, if the user is hunched over, the monitoring unit can instruct them to adopt the correct posture. It can also prompt the user to take a break if they are maintaining the same posture for an extended period. Furthermore, the monitoring unit can record the user's posture data and provide feedback later. This allows for improved training effectiveness by properly managing the user's posture.

[0053] The evaluation unit can compare a user's training results with those of other users and provide a relative evaluation. For example, the evaluation unit can evaluate a user's score by comparing it to the average score of other users who have taken the same training program. It can also evaluate a user's performance by comparing it to users of the same age group and gender. Furthermore, the evaluation unit can assess progress by comparing it to the user's past training results. This allows users to evaluate their training results from multiple perspectives and increase their motivation.

[0054] The generation unit can adjust the format of a user's training program based on their learning style. For example, it can generate a training program that heavily utilizes diagrams and graphs for visual learners. It can also generate training programs in audio guide or podcast format for auditory learners. Furthermore, it can generate training programs that include interactive simulations and practical tasks for experiential learners. By adjusting the format of the training program according to the user's learning style, it can provide more effective learning.

[0055] The adjustment unit can customize the training content based on the user's hobbies and interests when adjusting the user's training. For example, if the user is interested in sports, the training content can be provided using sports-related scenarios. If the user is interested in music, the training content can be provided that includes music-related tasks. Furthermore, if the user is interested in art, the training content can be provided that is related to art. By customizing the training content based on the user's hobbies and interests, motivation can be increased.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives input from the user to begin training. This input may include, for example, text input, voice input, or multiple-choice input. The reception desk can also input details if the user wishes to receive training to improve specific social skills. Step 2: The generation unit uses a generation AI to generate a training program based on the information received by the reception unit. The generation AI creates individually customized training programs based on past training data and user characteristics. For example, it generates training programs that include simulations of conversations in specific social situations or training in emotion recognition. Step 3: The monitoring unit monitors the user's reactions and progress based on the training program generated by the generation unit. The monitoring unit can measure the user's reaction speed, accuracy rate, progress, etc. Step 4: The adjustment unit adjusts the training content based on the information monitored by the monitoring unit. If the user is having difficulty with a particular task, the adjustment unit can break down the task and provide it in simpler steps. Step 5: The evaluation unit records the user's training results and evaluates their progress based on the training content adjusted by the adjustment unit. The evaluation unit can assess the user's improvement in conversational skills and propose the next training program.

[0058] (Example of form 2) The training system according to an embodiment of the present invention is a training system for supporting the treatment of autism spectrum disorder and Asperger's syndrome. This training system can replace human-assisted autism support and training by utilizing generative AI. First, the user provides input to begin training. For example, if the user wishes to receive training to improve a specific social skill, they input the details. This information is input to the generative AI. Next, the generative AI analyzes the input information and generates an optimal training program for the user. The generative AI creates an individually customized training program based on past training data and the user's characteristics. For example, this may include simulations of conversations in specific social situations or training in emotion recognition. Based on the generated training program, the user begins training. The generative AI monitors the user's responses and progress in real time and adjusts the training content as needed. For example, if the user is having difficulty with a particular task, the generative AI breaks down the task and provides it in simpler steps. Furthermore, the generative AI records the user's training results and evaluates their progress. This allows the user to see their own progress and receive feedback to move on to the next step. For example, the generative AI can evaluate the user's improvement in conversational skills and suggest the next training program. This system will enable more efficient and effective training to support the treatment of autism spectrum disorder and Asperger's syndrome. Users can progress through training at their own pace and improve specific skills through individually customized training programs. In addition, the generating AI provides real-time feedback, so users always receive appropriate support. As a result, the training system can efficiently and effectively support training to support the treatment of autism spectrum disorder and Asperger's syndrome.

[0059] The training system according to this embodiment comprises a reception unit, a generation unit, a monitoring unit, an adjustment unit, and an evaluation unit. The reception unit receives input from the user to initiate training. This input may include, but is not limited to, text input, voice input, or multiple-choice input. For example, the reception unit can receive input from the user if they wish to receive training to improve a specific social skill. The generation unit uses a generation AI to generate a training program based on the information received by the reception unit. The generation AI creates a customized training program based on past training data and the user's characteristics. For example, the generation AI generates a training program that includes simulations of conversations in specific social situations or training in emotion recognition. The monitoring unit monitors the user's reactions and progress based on the training program generated by the generation unit. The monitoring unit can measure, for example, the user's reaction speed, accuracy rate, and progress. The adjustment unit adjusts the training content based on the information monitored by the monitoring unit. For example, if the user is having difficulty with a particular task, the adjustment unit can break down the task and provide it in simpler steps. The evaluation unit records the user's training results and evaluates their progress based on the training content adjusted by the adjustment unit. For example, the evaluation unit can evaluate the user's improvement in conversational skills and propose the next training program. This enables the training system according to the embodiment to efficiently initiate, generate, monitor, adjust, and evaluate the user's training.

[0060] The reception unit can estimate the user's emotions and adjust the timing of the training start based on the estimated emotions. For example, if the user is tense, the reception unit can suggest a short break to relax before starting the training. Alternatively, if the user is excited, the reception unit can start the training immediately and perform a simple warm-up to improve concentration. Furthermore, if the user is tired, the reception unit can encourage them to rest and start the training after their energy has recovered. This allows for training to begin at a more appropriate time by adjusting the timing of the training start according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0061] The reception unit can analyze the user's past training history and select an input method. For example, if the user has preferred using voice input in the past, the reception unit will prioritize suggesting voice input. Alternatively, if the user has frequently used text input in the past, the reception unit can set text input as the default input method. Furthermore, if the user has previously trained during a specific time period, the reception unit can suggest starting training during that time period. This allows for the selection of the most user-friendly input method based on the user's past training history. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's past training history data into a generating AI and have the generating AI select the optimal input method.

[0062] The reception unit can filter training content based on the user's lifestyle and areas of interest at the start of training. For example, if the user is experiencing stress in their current lifestyle, the reception unit can suggest training content that promotes relaxation. The reception unit can also prioritize suggesting training content related to a specific area of ​​interest if the user is interested in that area. Furthermore, if the user has limited time in their current lifestyle, the reception unit can suggest short, effective training content. This allows for the provision of more appropriate training content by filtering it based on the user's current lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0063] The reception unit can estimate the user's emotions and prioritize input content based on the estimated emotions. For example, if the user is nervous, the reception unit will prioritize suggesting simple training content to help them relax. Similarly, if the user is excited, the reception unit can prioritize suggesting training content to improve their concentration. Furthermore, if the user is tired, the reception unit can prioritize suggesting training content to restore their energy. This allows for the provision of more appropriate training content by prioritizing input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0064] The reception desk can prioritize accepting training content that is highly relevant to the user based on their geographical location at the start of training. For example, if the user is in a specific region, the reception desk will prioritize suggesting training content related to that region. Similarly, if the user is traveling, the reception desk can prioritize suggesting training content related to their travel destination. Furthermore, if the user is at home, the reception desk can prioritize suggesting training content that can be done at home. This allows for the provision of more appropriate training content by providing highly relevant training based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI select highly relevant training content.

[0065] The reception desk can analyze the user's social media activity at the start of training and accept relevant training content. For example, if the user shows interest in a particular topic on social media, the reception desk can suggest training content related to that topic. It can also suggest relaxing training content if the user is experiencing stress on social media. Furthermore, if the user indicates on social media that they want to learn a new skill, the reception desk can suggest training content related to that skill. This allows for more appropriate training content to be provided based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI select relevant training content.

[0066] The generation unit can estimate the user's emotions and adjust the presentation of the training program based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a training program that proceeds at a relaxed pace. Alternatively, if the user is in a hurry, the generation unit can generate a training program that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a training program with visually stimulating effects. This allows for the provision of a more appropriate training program by adjusting its presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the training program.

[0067] The generation unit can adjust the program details based on the importance of the training when generating the training program. For example, the generation unit may include detailed explanations and step-by-step guides for high-importance training programs. Alternatively, it may include concise explanations and key points for low-importance training programs. Furthermore, it may include a moderate level of detail and specific examples for training programs of moderate importance. This allows for the provision of more appropriate training programs by adjusting the program detail based on the importance of the training. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input training importance data into a generation AI and have the generation AI perform the adjustment of program detail.

[0068] The generation unit can apply a generation algorithm according to the training category when generating a training program. For example, the generation unit can apply a dialogue simulation generation algorithm to a social skills training program. It can also apply a facial expression analysis generation algorithm to an emotion recognition training program. Furthermore, it can apply a scenario-based generation algorithm to a problem-solving skills training program. By applying different generation algorithms depending on the training category, a more appropriate training program can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input training category data into a generation AI and have the generation AI perform the application of the generation algorithm.

[0069] The generation unit can estimate the user's emotions and adjust the length of the training program based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise training program. Alternatively, if the user is relaxed, the generation unit can generate a longer training program with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a training program with visually stimulating effects. This allows for the provision of a more appropriate training program by adjusting the length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the length of the training program.

[0070] The generation unit can determine the priority of training programs based on the training submission timing when generating training programs. For example, the generation unit can prioritize the generation of training programs with high urgency. It can also prioritize the generation of training programs with approaching submission deadlines. Furthermore, it can postpone the generation of training programs with distant submission deadlines. This allows for the provision of more appropriate training programs by prioritizing programs based on the training submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input training submission timing data into a generation AI and have the generation AI perform the determination of program priorities.

[0071] The generation unit can adjust the order of training programs based on their relevance during program generation. For example, the generation unit can prioritize generating highly relevant training programs. It can also postpone the generation of less relevant training programs. Furthermore, it can generate training programs of moderate relevance in an appropriate order. By adjusting the program order based on training relevance, it is possible to provide more appropriate training programs. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input training relevance data into a generation AI and have the generation AI perform the program order adjustment.

[0072] The monitoring unit can estimate the user's emotions and adjust the monitoring criteria based on the estimated emotions. For example, if the user is tense, the monitoring unit can reduce the frequency of monitoring and provide a relaxing environment. Conversely, if the user is relaxed, the monitoring unit can increase the frequency of monitoring and provide more detailed feedback. Furthermore, if the user is excited, the monitoring unit can tighten the monitoring criteria and provide support to enhance concentration. This allows for more appropriate monitoring by adjusting the monitoring criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into the generative AI and have the generative AI adjust the monitoring criteria.

[0073] The monitoring unit can improve the accuracy of monitoring based on the interrelationships of the training during monitoring. For example, the monitoring unit can analyze the interrelationships of the training and monitor related training content together. The monitoring unit can also adjust the timing of monitoring by considering the interrelationships of the training. Furthermore, the monitoring unit can set monitoring criteria based on the interrelationships of the training. This improves the accuracy of monitoring by considering the interrelationships of the training. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the interrelationship data of the training into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0074] The monitoring unit can perform monitoring based on the attribute information of the training submitter during monitoring. For example, the monitoring unit can set monitoring criteria by considering the age and gender of the training submitter. The monitoring unit can also improve the accuracy of monitoring by referring to the training submitter's past training history. Furthermore, the monitoring unit can customize the monitoring method based on the characteristics of the training submitter. This makes it possible to perform more appropriate monitoring by considering the attribute information of the training submitter. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the attribute information data of the training submitter into a generating AI and have the generating AI perform the setting of monitoring criteria.

[0075] The monitoring unit can estimate the user's emotions and adjust the order in which monitoring results are displayed based on the estimated emotions. For example, if the user is tense, the monitoring unit may prioritize displaying results that promote relaxation. Similarly, if the user is relaxed, the monitoring unit may prioritize displaying detailed results. Furthermore, if the user is excited, the monitoring unit may prioritize displaying visually stimulating results. This allows for more appropriate feedback by adjusting the order in which monitoring results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the display order of the monitoring results.

[0076] The monitoring unit can perform monitoring based on the geographical distribution of training during monitoring. For example, the monitoring unit can analyze the geographical distribution of training and set monitoring criteria based on the characteristics of each region. The monitoring unit can also adjust the timing of monitoring, for example, by considering the geographical distribution of training. Furthermore, the monitoring unit can customize the monitoring method, for example, based on the geographical distribution of training. This makes it possible to perform more appropriate monitoring by considering the geographical distribution of training. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input geographical distribution data of training into a generating AI and have the generating AI perform the setting of monitoring criteria.

[0077] The monitoring unit can improve the accuracy of monitoring based on relevant training literature during monitoring. For example, the monitoring unit can refer to relevant training literature and set monitoring criteria based on the latest research findings. The monitoring unit can also improve the monitoring method by considering relevant training literature. Furthermore, the monitoring unit can improve the accuracy of monitoring based on relevant training literature. As a result, the accuracy of monitoring is improved by referring to relevant training literature. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant training literature data into a generating AI and have the generating AI perform the setting of monitoring criteria.

[0078] The adjustment unit can estimate the user's emotions and determine how to adjust the training content based on the estimated emotions. For example, if the user is tense, the adjustment unit will adjust the training content to help them relax. It can also adjust the training content to improve concentration if the user is excited, or adjust it to restore energy if the user is tired. This allows for the provision of more appropriate training content by determining how to adjust the training content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI determine how to adjust the training content.

[0079] The adjustment unit can select an adjustment method based on the user's past training history when adjusting the training content. For example, the adjustment unit can adjust the training content by referring to training methods that the user has succeeded with in the past. The adjustment unit can also adjust the training content to avoid training content that the user has found difficult in the past. Furthermore, the adjustment unit can select and adjust the optimal training content from the user's past training history. This makes it possible to adjust the training content more appropriately by referring to the user's past training history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without using AI. For example, the adjustment unit can input the user's past training history data into a generating AI and have the generating AI perform the selection of an adjustment method.

[0080] The adjustment unit can customize the means of adjustment based on the user's current living situation when adjusting the training content. For example, if the user is feeling stressed in their current living situation, the adjustment unit can adjust the training content to help them relax. The adjustment unit can also adjust the training content to be effective in a short amount of time if the user has limited time in their current living situation. Furthermore, if the user has an interest in a particular area of ​​interest in their current living situation, the adjustment unit can adjust the training content to be related to that area. This allows for the provision of more appropriate training content by customizing it based on the user's current living situation. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the adjustment means.

[0081] The adjustment unit can estimate the user's emotions and determine the priority of training content based on the estimated emotions. For example, if the user is tense, the adjustment unit will prioritize suggesting training content that promotes relaxation. Similarly, if the user is excited, the adjustment unit can prioritize suggesting training content that enhances concentration. Furthermore, if the user is tired, the adjustment unit can prioritize suggesting training content that restores energy. This allows for the provision of more appropriate training content by prioritizing training content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI determine the priority of training content.

[0082] The adjustment unit can select an adjustment method based on the user's geographical location information when adjusting training content. For example, if the user is in a specific region, the adjustment unit will adjust the training content to be relevant to that region. It can also adjust the training content to be relevant to the user's travel destination if the user is traveling. Furthermore, if the user is at home, the adjustment unit can adjust the training content to be suitable for use at home. This allows for the provision of more appropriate training content by adjusting it based on the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, or without AI. For example, the adjustment unit can input the user's geographical location data into a generating AI and have the generating AI select the adjustment method.

[0083] The adjustment unit can analyze the user's social media activity and propose adjustment methods when adjusting training content. For example, if the user shows interest in a particular topic on social media, the adjustment unit can adjust the training content to be related to that topic. Also, if the user is feeling stressed on social media, the adjustment unit can adjust the training content to be relaxing. Furthermore, if the user indicates on social media that they want to learn a new skill, the adjustment unit can adjust the training content to be related to that skill. In this way, by adjusting the training content based on the user's social media activity, more appropriate training content can be provided. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media activity data into a generating AI and have the generating AI execute suggestions for adjustment methods.

[0084] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is tense, the evaluation unit can set evaluation criteria that promote relaxation. It can also set evaluation criteria to improve concentration if the user is excited, or to restore energy if the user is tired. This allows for more appropriate evaluation by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI adjust the evaluation criteria.

[0085] The evaluation unit can adjust the evaluation algorithm based on past evaluation data when evaluating training results. For example, the evaluation unit can analyze past evaluation data and select the optimal evaluation algorithm. The evaluation unit can also adjust the evaluation criteria based on past evaluation data. Furthermore, the evaluation unit can improve the accuracy of the evaluation by referring to past evaluation data. This improves the accuracy of the evaluation algorithm by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input past evaluation data into a generating AI and have the generating AI perform the adjustment of the evaluation algorithm.

[0086] The evaluation unit can customize the evaluation methods based on the user's characteristics when evaluating training results. For example, the evaluation unit can set evaluation criteria considering the user's age and gender. The evaluation unit can also improve the accuracy of the evaluation by referring to the user's past training history. Furthermore, the evaluation unit can customize the evaluation method based on the user's characteristics. By customizing the evaluation methods based on the user's characteristics, a more appropriate evaluation becomes possible. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user characteristic data into a generating AI and have the generating AI perform the customization of the evaluation methods.

[0087] The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated emotions. For example, if the user is tense, the evaluation unit will prioritize suggesting evaluations that promote relaxation. Similarly, if the user is excited, the evaluation unit can prioritize suggesting evaluations that enhance concentration. Furthermore, if the user is tired, the evaluation unit can prioritize suggesting evaluations that restore energy. By determining the priority of evaluations according to the user's emotions, a more appropriate evaluation becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI determine the priority of evaluations.

[0088] The evaluation unit can select an evaluation method based on the user's geographical location information when evaluating training results. For example, if the user is in a specific region, the evaluation unit can select an evaluation method relevant to that region. It can also select an evaluation method relevant to the user's travel destination if the user is traveling. Furthermore, if the user is at home, it can select an evaluation method that can be performed at home. By selecting an evaluation method based on the user's geographical location information, a more appropriate evaluation becomes possible. Some or all of the above processing in the evaluation unit may be performed using AI, or without AI. For example, the evaluation unit can input the user's geographical location data into a generating AI and have the generating AI select the evaluation method.

[0089] The evaluation unit can analyze the user's social media activity and propose evaluation methods when evaluating training results. For example, if the user shows interest in a particular topic on social media, the evaluation unit can propose an evaluation method related to that topic. Furthermore, if the user is experiencing stress on social media, the evaluation unit can propose a relaxing evaluation method. Also, if the user indicates a desire to learn a new skill on social media, the evaluation unit can propose an evaluation method related to that skill. This allows for more appropriate evaluation by proposing evaluation methods based on the user's social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input the user's social media activity data into a generating AI and have the generating AI propose evaluation methods. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, generation unit, monitoring unit, adjustment unit, and evaluation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives user input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a training program using a generation AI. The monitoring unit monitors the user's response using the camera 42 and microphone 38B of the smart device 14. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the training content based on the monitoring results. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates the user's training results. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, generation unit, monitoring unit, adjustment unit, and evaluation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives voice input from the user. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a training program using a generation AI. The monitoring unit monitors the user's reactions using, for example, the camera 42 of the smart glasses 214. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and adjusts the training content based on the monitoring results. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and evaluates the user's training results. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, generation unit, monitoring unit, adjustment unit, and evaluation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives voice input from the user. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a training program using a generation AI. The monitoring unit monitors the user's reactions using, for example, the camera 42 of the headset terminal 314. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and adjusts the training content based on the monitoring results. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and evaluates the user's training results. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, generation unit, monitoring unit, adjustment unit, and evaluation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives voice input from the user. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a training program using a generation AI. The monitoring unit monitors the user's reactions using, for example, the camera 42 of the robot 414. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and adjusts the training content based on the monitoring results. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and evaluates the user's training results.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The reception desk can acquire the user's biometric information and assess the user's health status before training begins. For example, the reception desk can measure the user's heart rate and blood pressure, and temporarily suspend training if abnormalities are detected. It can also analyze the user's sleep data and suggest lighter training content if the user hasn't had enough rest. Furthermore, the reception desk can refer to the user's dietary data and display a message encouraging nutritional supplementation if their nutritional status is insufficient. This allows for safer and more effective training by adjusting the training content based on the user's health status.

[0092] The generation unit can estimate the user's emotions and adjust the difficulty level of the training program based on those emotions. For example, if the user is feeling anxious, the generation unit can generate a training program that starts with easy steps. If the user is confident, the generation unit can also generate a training program that includes challenging tasks. Furthermore, if the user is excited, the generation unit can generate a training program designed to improve concentration. By adjusting the difficulty level of the training program according to the user's emotions, more appropriate training can be provided.

[0093] The monitoring unit can detect the user's posture during training and prompt correction if it is poor. For example, if the user is hunched over, the monitoring unit can instruct them to adopt the correct posture. It can also prompt the user to take a break if they are maintaining the same posture for an extended period. Furthermore, the monitoring unit can record the user's posture data and provide feedback later. This allows for improved training effectiveness by properly managing the user's posture.

[0094] The adjustment unit can estimate the user's emotions and adjust the feedback method of the training content based on the estimated user emotions. For example, if the user is nervous, the adjustment unit can provide feedback in gentle words. If the user is confident, the adjustment unit can also provide feedback that includes specific areas for improvement. Furthermore, if the user is excited, the adjustment unit can provide feedback that includes positive reinforcement. In this way, by adjusting the feedback method according to the user's emotions, more effective training can be provided.

[0095] The evaluation unit can compare a user's training results with those of other users and provide a relative evaluation. For example, the evaluation unit can evaluate a user's score by comparing it to the average score of other users who have taken the same training program. It can also evaluate a user's performance by comparing it to users of the same age group and gender. Furthermore, the evaluation unit can assess progress by comparing it to the user's past training results. This allows users to evaluate their training results from multiple perspectives and increase their motivation.

[0096] The reception desk can estimate the user's emotions and, based on those estimates, provide content to help them relax before the training begins. For example, if the user is feeling tense, the reception desk can provide relaxing music or meditation guidance. If the user is feeling anxious, the reception desk can provide relaxing videos or breathing exercises. Furthermore, if the user is feeling stressed, the reception desk can suggest relaxing aromatherapy. In this way, by providing content to help users relax according to their emotions, a relaxed state can be created before the training begins.

[0097] The generation unit can adjust the format of a user's training program based on their learning style. For example, it can generate a training program that heavily utilizes diagrams and graphs for visual learners. It can also generate training programs in audio guide or podcast format for auditory learners. Furthermore, it can generate training programs that include interactive simulations and practical tasks for experiential learners. By adjusting the format of the training program according to the user's learning style, it can provide more effective learning.

[0098] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on those emotions. For example, if the user is tense, the monitoring frequency can be reduced to provide a more relaxing environment. Conversely, if the user is relaxed, the monitoring frequency can be increased to provide more detailed feedback. Furthermore, if the user is excited, the monitoring criteria can be tightened to provide support for improving concentration. By adjusting the monitoring frequency according to the user's emotions, more appropriate monitoring becomes possible.

[0099] The adjustment unit can customize the training content based on the user's hobbies and interests when adjusting the user's training. For example, if the user is interested in sports, the training content can be provided using sports-related scenarios. If the user is interested in music, the training content can be provided that includes music-related tasks. Furthermore, if the user is interested in art, the training content can be provided that is related to art. By customizing the training content based on the user's hobbies and interests, motivation can be increased.

[0100] The evaluation unit can estimate the user's emotions and adjust the feedback method based on those emotions. For example, if the user is nervous, the evaluation unit can provide feedback in gentle language. If the user is confident, the evaluation unit can provide feedback that includes specific areas for improvement. Furthermore, if the user is excited, the evaluation unit can provide feedback that includes positive reinforcement. By adjusting the feedback method according to the user's emotions, a more effective evaluation can be provided.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception desk receives input from the user to begin training. This input may include, for example, text input, voice input, or multiple-choice input. The reception desk can also input details if the user wishes to receive training to improve specific social skills. Step 2: The generation unit uses a generation AI to generate a training program based on the information received by the reception unit. The generation AI creates individually customized training programs based on past training data and user characteristics. For example, it generates training programs that include simulations of conversations in specific social situations or training in emotion recognition. Step 3: The monitoring unit monitors the user's reactions and progress based on the training program generated by the generation unit. The monitoring unit can measure the user's reaction speed, accuracy rate, progress, etc. Step 4: The adjustment unit adjusts the training content based on the information monitored by the monitoring unit. If the user is having difficulty with a particular task, the adjustment unit can break down the task and provide it in simpler steps. Step 5: The evaluation unit records the user's training results and evaluates their progress based on the training content adjusted by the adjustment unit. The evaluation unit can assess the user's improvement in conversational skills and propose the next training program.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts input to start training, A generation unit generates a training program based on the information received by the aforementioned reception unit, A monitoring unit monitors the user's reactions and progress based on the training program generated by the generation unit, Based on the information monitored by the aforementioned monitoring unit, an adjustment unit adjusts the training content, The system includes an evaluation unit that records the user's training results and evaluates their progress based on the training content adjusted by the adjustment unit. A system characterized by the following features.

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

3. The aforementioned reception unit is Analyze the user's past training history and select an input method. The system according to feature 1.

4. The aforementioned reception unit is At the start of training, filtering is performed based on the user's lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is At the start of training, the system prioritizes accepting training content that is highly relevant to the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is At the start of training, the system analyzes the user's social media activity and accepts relevant training content. The system according to feature 1.

8. The generating unit is It estimates the user's emotions and adjusts the training program's presentation based on the estimated user emotions. The system according to feature 1.

9. The generating unit is When generating a training program, adjust the program details based on the importance of the training. The system according to feature 1.

10. The generating unit is When generating a training program, the generation algorithm is applied according to the training category. The system according to feature 1.

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