system

Abstract

The system according to this embodiment aims to provide training at the optimal timing and with the optimal content according to the child's learning progress. [Solution] The system according to the embodiment comprises an analysis unit, a provision unit, and an analysis unit. The analysis unit analyzes the child's learning progress using multimodal AI. The provision unit provides training at an appropriate timing and with appropriate content based on the information analyzed by the analysis unit. The analysis unit analyzes at least one of audio and images.

Description

Technical Field

[0006] , , ,

[0005] , ,

[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

[0007] The system according to this embodiment can provide training at the optimal timing and with the optimal content according to the child's learning progress. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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 absolute pitch training agent system according to an embodiment of the present invention is a system for training preschool children in absolute pitch. This system analyzes the child's learning progress using multimodal AI and provides training at the optimal timing and with the optimal content. The multimodal AI analyzes both audio and images to understand the child's responses in detail and provide effective training. For example, the agent asks, "What note is this?" and when the child answers, "Do-Mi-So," it provides feedback, "Correct!" in an interactive training format. Furthermore, this agent is intended to be provided as an app and sold worldwide. The online music education market is rapidly expanding, and this agent has great potential in that market. It is expected to solve the problem of parents being too busy to have time to train their children and to be a tool that enriches children's lives in the long term. As a result, the absolute pitch training agent system can provide optimal training according to the child's learning progress.

[0029] The absolute pitch training agent system according to this embodiment comprises an analysis unit, a provision unit, and an analysis unit. The analysis unit analyzes the child's learning progress using multimodal AI. The multimodal AI is realized by combining technologies such as speech recognition, image recognition, and natural language processing. The analysis unit analyzes both speech and images to understand the child's learning progress in detail. For example, the analysis unit analyzes the child's vocalization patterns and facial expression changes to evaluate the learning progress. The provision unit provides training at the optimal timing and with the optimal content based on the information analyzed by the analysis unit. For example, the provision unit adjusts the timing and content of the training based on the child's learning progress and reactions. For example, the provision unit provides training during times when the child is concentrating. The provision unit can also provide effective training in a short amount of time if the child is tired. The analysis unit analyzes both speech and images. For example, the analysis unit uses speech feature quantities and image analysis algorithms to understand the child's reactions in detail. For example, the analysis unit uses speech recognition technology to analyze the child's vocalization patterns. Furthermore, the analysis unit can also analyze changes in a child's facial expressions using image recognition technology. This allows the absolute pitch training agent system according to the embodiment to provide optimal training tailored to the child's learning progress.

[0030] The analysis department uses multimodal AI to analyze children's learning progress. This multimodal AI is implemented by combining technologies such as speech recognition, image recognition, and natural language processing. Specifically, speech recognition technology is used to analyze children's vocal patterns and evaluate the accuracy of their pitch and rhythm. For example, speech recognition technology is used to analyze whether a child can accurately reproduce a piano sound. Image recognition technology is also used to analyze changes in children's facial expressions to understand their level of concentration and emotional changes during training. For example, by analyzing how often a child smiles or frowns during training, the difficulty level of the training can be evaluated. Furthermore, natural language processing technology is used to analyze the words and reactions children make during training and evaluate their understanding of the training content. As a result, the analysis department can integrate information obtained from each modality of speech, image, and language to gain a detailed understanding of the child's learning progress. This allows the analysis department to provide basic data for offering optimal training tailored to each child's learning progress.

[0031] The service provider delivers training at the optimal timing and with the most relevant content, based on information analyzed by the analysis department. Specifically, they adjust the timing and content of training based on the child's learning progress and responses. For example, to provide training during times when the child is most focused, they analyze past training data to identify the times when the child is most attentive. Also, if the child is tired, they adjust the training content to provide effective training in a short amount of time. For example, when a child is tired, they provide simple training with short breaks in between. Furthermore, the service provider can adjust the training content in real time according to the child's responses. For example, if the child finds the training difficult, they can lower the difficulty level or try a different approach. Also, if the child shows a positive response to the training, they can increase the difficulty level or extend the training time. In this way, the service provider can provide optimal training tailored to the child's learning progress and maximize the child's learning effectiveness.

[0032] The analysis unit analyzes both audio and images. Specifically, it uses audio feature data and image analysis algorithms to understand the child's reactions in detail. For example, it uses speech recognition technology to analyze the child's vocal patterns and evaluate the accuracy of pitch and rhythm. Speech recognition technology can analyze the child's vocalizations in real time and detect accurate pitch and rhythm. It also uses image recognition technology to analyze the child's facial expressions and understand their level of concentration and emotional changes during training. Image recognition technology can analyze the child's facial expressions in real time and detect changes such as smiles and frown lines. Furthermore, the analysis unit can integrate this information to comprehensively evaluate the child's response to training. For example, by combining speech recognition and image recognition technology, it can evaluate how much the child is concentrating and enjoying the training. This allows the analysis unit to understand the child's reactions in detail and provide information to maximize the effectiveness of the training.

[0033] The agent employs an interactive training method where it asks, "What sound is this?" and provides feedback like "C-E-G" when the child answers, "Correct!". For example, if the child answers correctly, the agent immediately provides feedback like "Correct!". The agent can also immediately provide feedback like "Try again" if the child makes a mistake. Furthermore, if the child does not respond, the agent can provide encouraging feedback. This interactive training can increase the child's motivation to learn.

[0034] The agent will be offered as an app and aims to be sold in multiple regions. For example, the agent will provide an interface that supports the language of each region. It can also provide training content tailored to the culture of each region. Furthermore, it can offer features that are compatible with the education system of each region. This will allow the training to reach many children worldwide through global sales.

[0035] The analysis unit analyzes both audio and images. For example, the analysis unit extracts audio features and analyzes them using speech recognition technology. The analysis unit can also analyze images using image analysis algorithms. For example, the analysis unit can analyze a child's vocalization patterns and evaluate accurate pitch. The analysis unit can also analyze changes in a child's facial expressions and evaluate the enjoyment of the training. By analyzing both audio and images, the child's reactions can be understood in detail. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input audio data and image data into a generative AI and have the generative AI perform the analysis.

[0036] The analysis unit analyzes the child's past training history and selects the optimal analysis algorithm. For example, the analysis unit selects a similar method based on the child's past successful training methods. It can also avoid training methods the child has struggled with in the past and select a different method. Furthermore, the analysis unit can identify the most effective time of day from the child's past training history and select an analysis algorithm suited to that time. This enhances the effectiveness of training by selecting the optimal analysis algorithm based on past training history. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the child's past training data into a generative AI and have the generative AI select the optimal analysis algorithm.

[0037] The analysis unit applies different analysis methods depending on the child's age and developmental stage. For example, the analysis unit applies an analysis method that includes many visual stimuli to toddlers. It can also apply an analysis method that promotes logical thinking to elementary school children. Furthermore, the analysis unit can apply an analysis method that adjusts the accuracy of speech recognition according to the developmental stage. This allows for more appropriate training by applying an analysis method appropriate to age and developmental stage. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the child's age and developmental stage into a generative AI, and have the generative AI select an appropriate analysis method.

[0038] The analysis unit performs analyses based on the child's living environment and home circumstances. For example, if the child is training in a quiet environment, the analysis unit will perform analyses to improve concentration. It can also perform effective analyses in a short time if the child is training in a noisy environment. Furthermore, the analysis unit can adjust the frequency and content of training according to the child's home circumstances. This allows for the provision of more appropriate training by considering the living environment and home circumstances. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the child's living environment and home circumstances into a generative AI and have the generative AI perform the analysis.

[0039] The analysis unit customizes its analysis methods to reflect the child's learning style and interests. For example, if a child prefers visual learning, the analysis unit applies an analysis method that includes many visual elements. Similarly, if a child prefers auditory learning, the analysis unit can apply an analysis method that includes many audio elements. Furthermore, the analysis unit can incorporate training content related to the child's interests into its analysis methods. This allows for more effective training by applying an analysis method tailored to the child's learning style and interests. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input data on the child's learning style and interests into a generative AI, which can then perform the customization of the analysis method.

[0040] The service provider adjusts the timing of training based on the child's concentration level and fatigue level. For example, the service provider provides training during times when the child is most focused. It can also provide training with breaks in between if the child is tired. Furthermore, if the child's concentration level is low, the service provider can provide short, effective training sessions. By adjusting the timing of training according to concentration and fatigue levels, more effective training can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without one. For example, the service provider can input data on the child's concentration level and fatigue level into a generative AI, which can then adjust the timing of training.

[0041] The service provider customizes the training content according to the child's learning progress when providing training. For example, if the child has mastered a particular sound, the service provider provides training that moves on to the next sound. The service provider can also provide training that allows the child to repeatedly practice sounds that the child finds difficult. Furthermore, the service provider can adjust the difficulty level of the training according to the child's learning progress. This allows for more effective training by providing training content that matches the child's learning progress. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the child's learning progress data into a generative AI and have the generative AI perform the customization of the training content.

[0042] The service provider adds interactive elements to the training to capture the child's interest and attention. For example, the service provider may provide training using characters that the child likes. It may also provide training related to themes that the child is interested in. Furthermore, the service provider may incorporate game elements that the child can enjoy into the training. By adding interactive elements, the service provider captures the child's interest and enhances the effectiveness of the training. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider may input data on the child's interests into a generative AI, which can then perform the addition of interactive elements.

[0043] The service provider optimizes the training schedule to match the child's daily rhythm when providing training. For example, if the child is a morning person, the service provider will provide training in the morning. If the child is a night owl, the service provider can also provide training in the evening. Furthermore, the service provider can adjust the frequency and duration of training to match the child's daily rhythm. By providing a training schedule that matches the child's daily rhythm, more effective training can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input data on the child's daily rhythm into a generative AI and have the generative AI perform the optimization of the training schedule.

[0044] The analysis unit evaluates the training effect by analyzing the child's vocal patterns and facial expressions in detail during the analysis. For example, the analysis unit analyzes the child's vocal patterns and evaluates the accuracy of the pitch. The analysis unit can also analyze the child's facial expressions and evaluate the enjoyment of the training. Furthermore, the analysis unit can combine the child's vocal patterns and facial expressions to comprehensively evaluate the training effect. This allows for an accurate evaluation of the training effect by analyzing vocal patterns and facial expressions in detail. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the child's vocal patterns and facial expressions into a generative AI, and have the generative AI perform the evaluation of the training effect.

[0045] The analysis unit optimizes the analysis algorithm based on the child's reaction speed and accuracy during the analysis. For example, the analysis unit can analyze the child's reaction speed and adjust the timing of training. It can also analyze the child's accuracy and adjust the difficulty level of training. Furthermore, the analysis unit can optimize the analysis algorithm by combining the child's reaction speed and accuracy. This allows for more accurate analysis by applying an analysis algorithm that takes reaction speed and accuracy into consideration. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the child's reaction speed and accuracy into a generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0046] The analysis unit improves the accuracy of the analysis based on the child's home environment and background information during the analysis. For example, the analysis unit adjusts the accuracy of the analysis according to the child's home environment. The analysis unit can also improve the accuracy of the analysis by considering the child's background information. Furthermore, the analysis unit can optimize the accuracy of the analysis by combining the child's home environment and background information. In this way, the accuracy of the analysis can be improved by considering the home environment and background information. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input data on the child's home environment and background information into a generating AI, and have the generating AI perform the improvement of the analysis accuracy.

[0047] The analysis unit customizes the analysis method by referring to the child's learning history and performance data during the analysis. For example, the analysis unit customizes the analysis method by referring to the child's learning history. The analysis unit can also optimize the analysis method by referring to the child's performance data. Furthermore, the analysis unit can customize the analysis method by combining the child's learning history and performance data. This allows the analysis method to be optimized by referring to the learning history and performance data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the child's learning history and performance data into a generative AI and have the generative AI perform the customization of the analysis method.

[0048] During interactive training, immediate feedback is provided in response to the child's reactions. For example, if the child answers correctly, the feedback "Correct!" is given immediately. If the child makes a mistake, the feedback "Try again" can also be given immediately. Furthermore, if the child does not respond, encouraging words can be given as feedback. By providing immediate feedback, the child's motivation to learn can be increased. Some or all of the above processing during interactive training may be performed using, for example, a generative AI, or without a generative AI. For example, during interactive training, the child's reaction data can be input into a generative AI, and the generative AI can be used to provide immediate feedback.

[0049] During interactive training, game elements can be added to engage children's interest. For example, game elements using characters that children like can be added during interactive training. Game elements related to themes that children are interested in can also be added during interactive training. Furthermore, game elements incorporating a point system that children can enjoy can be added during interactive training. By adding game elements, children's interest is stimulated and the effectiveness of the training is enhanced. Some or all of the above processes during interactive training may be performed using, for example, a generative AI, or not using a generative AI. For example, during interactive training, data on the child's interests can be input into a generative AI, and the generative AI can be used to add game elements.

[0050] During interactive training, the format of the dialogue is customized to suit the child's learning style. For example, if a child prefers visual learning, the dialogue will include many visual elements. Similarly, if a child prefers auditory learning, the dialogue can include many sounds. Furthermore, if a child prefers experiential learning, the dialogue can include experiences that involve actually making sounds. By applying a dialogue format tailored to the learning style, more effective training can be provided. Some or all of the above processing during interactive training may be performed using, for example, generative AI, or not. For example, during interactive training, the child's learning style data can be input into a generative AI, which can then perform the customization of the dialogue format.

[0051] When an app is released, user feedback is collected to continuously improve the app's functionality. For example, the app can improve its interface based on user feedback. It can also add new features based on user feedback. Furthermore, the app can optimize existing features based on user feedback. By improving the app's functionality based on user feedback, a more user-friendly app can be provided. Some or all of the above processes in the app may be performed using, for example, generative AI, or not. For example, the app can input user feedback data into a generative AI and have the generative AI perform the function improvements.

[0052] When providing the app, its content is localized according to the culture and language of each region. For example, the app provides an interface that supports the language of each region. The app can also provide training content tailored to the culture of each region. Furthermore, the app can provide functions that are tailored to the education system of each region. By localizing the app according to the culture and language of each region, it can be used to serve a wider range of users. Some or all of the above processes in the app may be performed using, for example, generative AI, or not. For example, the app can input regional culture and language data into a generative AI and have the generative AI perform the localization.

[0053] When the app is released, support features for parents and educators will be added. For example, the app will provide parents with a function to check their child's learning progress. The app may also provide educators with a function to customize the training content. Furthermore, the app may provide parents and educators with a function to evaluate the effectiveness of the training. By providing support features for parents and educators, the effectiveness of the training can be enhanced. Some or all of the above processes in the app may be performed using, for example, generative AI, or not. For example, the app can input feedback data from parents and educators into a generative AI, which can then perform the addition of support features.

[0054] When providing the app, it will be integrated with other educational apps and devices to maximize learning effectiveness. For example, the app can integrate with other music education apps to expand training content. The app can also integrate with other devices to enhance training effectiveness. Furthermore, the app can integrate with other educational apps to provide overall learning effectiveness. In this way, learning effectiveness can be maximized by integrating with other educational apps and devices. Some or all of the above processes in the app may be performed using, for example, generative AI, or not using generative AI. For example, the app can input data from other educational apps and devices into a generative AI, which can then perform optimization of the integration.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The perfect pitch training agent system can monitor a child's learning progress in real time and dynamically change the training content according to their progress. For example, if a child can accurately recognize a particular sound, it can provide training to move on to sounds of the next difficulty level. It can also add supplementary training for sounds that a child is struggling with. Furthermore, it can adjust the frequency and duration of training based on the child's learning progress. This allows for the provision of optimal training tailored to the child's learning progress.

[0057] The perfect pitch training agent system can customize training methods according to a child's learning style. For example, children who prefer visual learning can be provided with training that includes many visual elements. Children who prefer auditory learning can be provided with training that includes many sounds. Furthermore, children who prefer experiential learning can be provided with training that includes hands-on sound production. This allows for the provision of optimal training tailored to each child's learning style.

[0058] The perfect pitch training agent system can optimize the training schedule to match a child's daily rhythm. For example, it can provide training in the morning for morning-oriented children and in the evening for night-owl children. It can also adjust the frequency and duration of training to suit the child's rhythm. This allows for the provision of an optimal training schedule tailored to each child's lifestyle.

[0059] The perfect pitch training agent system can customize training content based on a child's home environment and background information. For example, it can provide training to improve concentration for children training in a quiet environment, and provide short, effective training for children training in a noisy environment. It can also provide training content tailored to the family's culture and language. This allows for the provision of optimal training tailored to each child's home environment and background information.

[0060] The perfect pitch training agent system can customize training content by referencing a child's learning history and performance data. For example, it can provide similar training methods based on past successes and avoid methods that the child has struggled with. It can also identify the most effective time of day and provide training tailored to that time. This allows for the provision of optimal training based on past learning history and performance data.

[0061] The perfect pitch training agent system can customize training content to reflect a child's learning style and interests. For example, it can provide training with many visual elements for children who prefer visual learning, and training with many auditory elements for children who prefer auditory learning. It can also provide training content related to themes that the child is interested in. This allows for the provision of optimal training tailored to each child's learning style and interests.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The analysis unit analyzes the child's learning progress using multimodal AI. The multimodal AI is implemented by combining technologies such as speech recognition, image recognition, and natural language processing. The analysis unit analyzes the child's vocal patterns and facial expression changes to evaluate their learning progress. Step 2: The service provider delivers training at the optimal time and with the most relevant content, based on the information analyzed by the analysis department. The service provider adjusts the timing and content of the training based on the child's learning progress and responses. For example, they might provide training during times when the child is focused, or offer short, effective training sessions when the child is tired. Step 3: The analysis unit analyzes both audio and images. The analysis unit uses audio feature data and image analysis algorithms to understand the child's reactions in detail. For example, it uses speech recognition technology to analyze the child's vocal patterns and image recognition technology to analyze changes in the child's facial expressions.

[0064] (Example of form 2) The absolute pitch training agent system according to an embodiment of the present invention is a system for training preschool children in absolute pitch. This system analyzes the child's learning progress using multimodal AI and provides training at the optimal timing and with the optimal content. The multimodal AI analyzes both audio and images to understand the child's responses in detail and provide effective training. For example, the agent asks, "What note is this?" and when the child answers, "Do-Mi-So," it provides feedback, "Correct!" in an interactive training format. Furthermore, this agent is intended to be provided as an app and sold worldwide. The online music education market is rapidly expanding, and this agent has great potential in that market. It is expected to solve the problem of parents being too busy to have time to train their children and to be a tool that enriches children's lives in the long term. As a result, the absolute pitch training agent system can provide optimal training according to the child's learning progress.

[0065] The absolute pitch training agent system according to this embodiment comprises an analysis unit, a provision unit, and an analysis unit. The analysis unit analyzes the child's learning progress using multimodal AI. The multimodal AI is realized by combining technologies such as speech recognition, image recognition, and natural language processing. The analysis unit analyzes both speech and images to understand the child's learning progress in detail. For example, the analysis unit analyzes the child's vocalization patterns and facial expression changes to evaluate the learning progress. The provision unit provides training at the optimal timing and with the optimal content based on the information analyzed by the analysis unit. For example, the provision unit adjusts the timing and content of the training based on the child's learning progress and reactions. For example, the provision unit provides training during times when the child is concentrating. The provision unit can also provide effective training in a short amount of time if the child is tired. The analysis unit analyzes both speech and images. For example, the analysis unit uses speech feature quantities and image analysis algorithms to understand the child's reactions in detail. For example, the analysis unit uses speech recognition technology to analyze the child's vocalization patterns. Furthermore, the analysis unit can also analyze changes in a child's facial expressions using image recognition technology. This allows the absolute pitch training agent system according to the embodiment to provide optimal training tailored to the child's learning progress.

[0066] The analysis department uses multimodal AI to analyze children's learning progress. This multimodal AI is implemented by combining technologies such as speech recognition, image recognition, and natural language processing. Specifically, speech recognition technology is used to analyze children's vocal patterns and evaluate the accuracy of their pitch and rhythm. For example, speech recognition technology is used to analyze whether a child can accurately reproduce a piano sound. Image recognition technology is also used to analyze changes in children's facial expressions to understand their level of concentration and emotional changes during training. For example, by analyzing how often a child smiles or frowns during training, the difficulty level of the training can be evaluated. Furthermore, natural language processing technology is used to analyze the words and reactions children make during training and evaluate their understanding of the training content. As a result, the analysis department can integrate information obtained from each modality of speech, image, and language to gain a detailed understanding of the child's learning progress. This allows the analysis department to provide basic data for offering optimal training tailored to each child's learning progress.

[0067] The service provider delivers training at the optimal timing and with the most relevant content, based on information analyzed by the analysis department. Specifically, they adjust the timing and content of training based on the child's learning progress and responses. For example, to provide training during times when the child is most focused, they analyze past training data to identify the times when the child is most attentive. Also, if the child is tired, they adjust the training content to provide effective training in a short amount of time. For example, when a child is tired, they provide simple training with short breaks in between. Furthermore, the service provider can adjust the training content in real time according to the child's responses. For example, if the child finds the training difficult, they can lower the difficulty level or try a different approach. Also, if the child shows a positive response to the training, they can increase the difficulty level or extend the training time. In this way, the service provider can provide optimal training tailored to the child's learning progress and maximize the child's learning effectiveness.

[0068] The analysis unit analyzes both audio and images. Specifically, it uses audio feature data and image analysis algorithms to understand the child's reactions in detail. For example, it uses speech recognition technology to analyze the child's vocal patterns and evaluate the accuracy of pitch and rhythm. Speech recognition technology can analyze the child's vocalizations in real time and detect accurate pitch and rhythm. It also uses image recognition technology to analyze the child's facial expressions and understand their level of concentration and emotional changes during training. Image recognition technology can analyze the child's facial expressions in real time and detect changes such as smiles and frown lines. Furthermore, the analysis unit can integrate this information to comprehensively evaluate the child's response to training. For example, by combining speech recognition and image recognition technology, it can evaluate how much the child is concentrating and enjoying the training. This allows the analysis unit to understand the child's reactions in detail and provide information to maximize the effectiveness of the training.

[0069] The agent employs an interactive training method where it asks, "What sound is this?" and provides feedback like "C-E-G" when the child answers, "Correct!". For example, if the child answers correctly, the agent immediately provides feedback like "Correct!". The agent can also immediately provide feedback like "Try again" if the child makes a mistake. Furthermore, if the child does not respond, the agent can provide encouraging feedback. This interactive training can increase the child's motivation to learn.

[0070] The agent will be offered as an app and aims to be sold in multiple regions. For example, the agent will provide an interface that supports the language of each region. It can also provide training content tailored to the culture of each region. Furthermore, it can offer features that are compatible with the education system of each region. This will allow the training to reach many children worldwide through global sales.

[0071] The analysis unit analyzes both audio and images. For example, the analysis unit extracts audio features and analyzes them using speech recognition technology. The analysis unit can also analyze images using image analysis algorithms. For example, the analysis unit can analyze a child's vocalization patterns and evaluate accurate pitch. The analysis unit can also analyze changes in a child's facial expressions and evaluate the enjoyment of the training. By analyzing both audio and images, the child's reactions can be understood in detail. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input audio data and image data into a generative AI and have the generative AI perform the analysis.

[0072] The analysis unit estimates the child's emotions and adjusts the analysis method of learning progress based on the estimated emotions. For example, if the child is enjoying themselves, the analysis unit adjusts the analysis method to provide more challenging tasks. It can also adjust the analysis method to provide easier tasks if the child is tired. Furthermore, if the child is focused, the analysis unit can adjust the analysis method to provide longer training sessions. This allows for more effective training by adjusting the analysis method according to the child'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 analysis unit may be performed using, for example, a generative AI, or not. For example, the analysis unit can input the child's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The analysis unit analyzes the child's past training history and selects the optimal analysis algorithm. For example, the analysis unit selects a similar method based on the child's past successful training methods. It can also avoid training methods the child has struggled with in the past and select a different method. Furthermore, the analysis unit can identify the most effective time of day from the child's past training history and select an analysis algorithm suited to that time. This enhances the effectiveness of training by selecting the optimal analysis algorithm based on past training history. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the child's past training data into a generative AI and have the generative AI select the optimal analysis algorithm.

[0074] The analysis unit applies different analysis methods depending on the child's age and developmental stage. For example, the analysis unit applies an analysis method that includes many visual stimuli to toddlers. It can also apply an analysis method that promotes logical thinking to elementary school children. Furthermore, the analysis unit can apply an analysis method that adjusts the accuracy of speech recognition according to the developmental stage. This allows for more appropriate training by applying an analysis method appropriate to age and developmental stage. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the child's age and developmental stage into a generative AI, and have the generative AI select an appropriate analysis method.

[0075] The analysis unit estimates the child's emotions and prioritizes the analysis results based on the estimated emotions. For example, if the child is enjoying themselves, the analysis unit may prioritize providing more difficult tasks. It may also prioritize providing easier tasks if the child is tired. Furthermore, if the child is focused, the analysis unit may prioritize providing longer training sessions. This allows for more effective training by prioritizing the analysis results according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, 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 analysis unit may be performed using, for example, a generative AI, or not. For example, the analysis unit can input the child's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The analysis unit performs analyses based on the child's living environment and home circumstances. For example, if the child is training in a quiet environment, the analysis unit will perform analyses to improve concentration. It can also perform effective analyses in a short time if the child is training in a noisy environment. Furthermore, the analysis unit can adjust the frequency and content of training according to the child's home circumstances. This allows for the provision of more appropriate training by considering the living environment and home circumstances. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the child's living environment and home circumstances into a generative AI and have the generative AI perform the analysis.

[0077] The analysis unit customizes its analysis methods to reflect the child's learning style and interests. For example, if a child prefers visual learning, the analysis unit applies an analysis method that includes many visual elements. Similarly, if a child prefers auditory learning, the analysis unit can apply an analysis method that includes many audio elements. Furthermore, the analysis unit can incorporate training content related to the child's interests into its analysis methods. This allows for more effective training by applying an analysis method tailored to the child's learning style and interests. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input data on the child's learning style and interests into a generative AI, which can then perform the customization of the analysis method.

[0078] The service provider estimates the child's emotions and adjusts the training delivery method based on the estimated emotions. For example, if the child is enjoying themselves, the service provider will provide training that incorporates game elements. If the child is tired, the service provider can also provide short, effective training. Furthermore, if the child is focused, the service provider can provide longer training sessions. By adjusting the training delivery method according to the child's emotions, more effective training can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input the child's emotion data into a generative AI and have the generative AI adjust the training delivery method.

[0079] The service provider adjusts the timing of training based on the child's concentration level and fatigue level. For example, the service provider provides training during times when the child is most focused. It can also provide training with breaks in between if the child is tired. Furthermore, if the child's concentration level is low, the service provider can provide short, effective training sessions. By adjusting the timing of training according to concentration and fatigue levels, more effective training can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without one. For example, the service provider can input data on the child's concentration level and fatigue level into a generative AI, which can then adjust the timing of training.

[0080] The service provider customizes the training content according to the child's learning progress when providing training. For example, if the child has mastered a particular sound, the service provider provides training that moves on to the next sound. The service provider can also provide training that allows the child to repeatedly practice sounds that the child finds difficult. Furthermore, the service provider can adjust the difficulty level of the training according to the child's learning progress. This allows for more effective training by providing training content that matches the child's learning progress. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the child's learning progress data into a generative AI and have the generative AI perform the customization of the training content.

[0081] The service provider estimates the child's emotions and adjusts the difficulty level of the training based on the estimated emotions. For example, if the child is enjoying themselves, the service provider may provide a more difficult training session. It may also provide a less difficult training session if the child is tired. Furthermore, if the child is focused, the service provider may provide training that gradually increases in difficulty. This allows for more effective training by adjusting the difficulty level according to the child'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 service provider may be performed using a generative AI, or not. For example, the service provider can input the child's emotion data into a generative AI and have the generative AI adjust the training difficulty level.

[0082] The service provider adds interactive elements to the training to capture the child's interest and attention. For example, the service provider may provide training using characters that the child likes. It may also provide training related to themes that the child is interested in. Furthermore, the service provider may incorporate game elements that the child can enjoy into the training. By adding interactive elements, the service provider captures the child's interest and enhances the effectiveness of the training. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider may input data on the child's interests into a generative AI, which can then perform the addition of interactive elements.

[0083] The service provider optimizes the training schedule to match the child's daily rhythm when providing training. For example, if the child is a morning person, the service provider will provide training in the morning. If the child is a night owl, the service provider can also provide training in the evening. Furthermore, the service provider can adjust the frequency and duration of training to match the child's daily rhythm. By providing a training schedule that matches the child's daily rhythm, more effective training can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input data on the child's daily rhythm into a generative AI and have the generative AI perform the optimization of the training schedule.

[0084] The analysis unit estimates the child's emotions and adjusts the audio and image analysis method based on the estimated emotions. For example, if the child is enjoying themselves, the analysis unit performs a detailed analysis of the audio and images. The analysis unit can also perform a simplified analysis if the child is tired. Furthermore, if the child is concentrating, the analysis unit can perform a longer analysis. This allows for more accurate analysis by applying an analysis method appropriate to the child'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 analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input the child's emotion data into a generative AI and have the generative AI adjust the audio and image analysis method.

[0085] The analysis unit evaluates the training effect by analyzing the child's vocal patterns and facial expressions in detail during the analysis. For example, the analysis unit analyzes the child's vocal patterns and evaluates the accuracy of the pitch. The analysis unit can also analyze the child's facial expressions and evaluate the enjoyment of the training. Furthermore, the analysis unit can combine the child's vocal patterns and facial expressions to comprehensively evaluate the training effect. This allows for an accurate evaluation of the training effect by analyzing vocal patterns and facial expressions in detail. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the child's vocal patterns and facial expressions into a generative AI, and have the generative AI perform the evaluation of the training effect.

[0086] The analysis unit optimizes the analysis algorithm based on the child's reaction speed and accuracy during the analysis. For example, the analysis unit can analyze the child's reaction speed and adjust the timing of training. It can also analyze the child's accuracy and adjust the difficulty level of training. Furthermore, the analysis unit can optimize the analysis algorithm by combining the child's reaction speed and accuracy. This allows for more accurate analysis by applying an analysis algorithm that takes reaction speed and accuracy into consideration. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the child's reaction speed and accuracy into a generative AI and have the generative AI perform the optimization of the analysis algorithm.

[0087] The analysis unit estimates the child's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the child is having fun, the analysis unit displays detailed analysis results. The analysis unit can also display simplified analysis results if the child is tired. Furthermore, if the child is concentrating, the analysis unit can display longer analysis results. This allows for the provision of more appropriate analysis results by applying a display method that corresponds to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the child's emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0088] The analysis unit improves the accuracy of the analysis based on the child's home environment and background information during the analysis. For example, the analysis unit adjusts the accuracy of the analysis according to the child's home environment. The analysis unit can also improve the accuracy of the analysis by considering the child's background information. Furthermore, the analysis unit can optimize the accuracy of the analysis by combining the child's home environment and background information. In this way, the accuracy of the analysis can be improved by considering the home environment and background information. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input data on the child's home environment and background information into a generating AI, and have the generating AI perform the improvement of the analysis accuracy.

[0089] The analysis unit customizes the analysis method by referring to the child's learning history and performance data during the analysis. For example, the analysis unit customizes the analysis method by referring to the child's learning history. The analysis unit can also optimize the analysis method by referring to the child's performance data. Furthermore, the analysis unit can customize the analysis method by combining the child's learning history and performance data. This allows the analysis method to be optimized by referring to the learning history and performance data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the child's learning history and performance data into a generative AI and have the generative AI perform the customization of the analysis method.

[0090] Interactive training methods estimate a child's emotions and adjust the content and tone of the dialogue based on those estimates. For example, if a child is having fun, the interactive training method can use a bright tone. If a child is tired, it can use a calm tone. Furthermore, if a child is concentrating, the interactive training method can use detailed content. This allows for more effective training by applying dialogue content and tone appropriate to the child'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 interactive training method may be performed using a generative AI, or not. For example, the interactive training method can input the child's emotion data into a generative AI, which can then adjust the content and tone of the dialogue.

[0091] During interactive training, immediate feedback is provided in response to the child's reactions. For example, if the child answers correctly, the feedback "Correct!" is given immediately. If the child makes a mistake, the feedback "Try again" can also be given immediately. Furthermore, if the child does not respond, encouraging words can be given as feedback. By providing immediate feedback, the child's motivation to learn can be increased. Some or all of the above processing during interactive training may be performed using, for example, a generative AI, or without a generative AI. For example, during interactive training, the child's reaction data can be input into a generative AI, and the generative AI can be used to provide immediate feedback.

[0092] Interactive training methods estimate a child's emotions and adjust the length and frequency of conversations based on those estimates. For example, if a child is enjoying themselves, the interactive training method may use longer conversations. If a child is tired, it may use shorter conversations. Furthermore, if a child is focused, it may use more frequent conversations. By applying conversation length and frequency according to the child's emotions, more effective training can be provided. Emotion estimation is achieved using emotion estimation functions, 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 interactive training method may be performed using, for example, a generative AI, or not using a generative AI. For example, the interactive training method may input child emotion data into a generative AI, which can then adjust the length and frequency of conversations.

[0093] During interactive training, game elements can be added to engage children's interest. For example, game elements using characters that children like can be added during interactive training. Game elements related to themes that children are interested in can also be added during interactive training. Furthermore, game elements incorporating a point system that children can enjoy can be added during interactive training. By adding game elements, children's interest is stimulated and the effectiveness of the training is enhanced. Some or all of the above processes during interactive training may be performed using, for example, a generative AI, or not using a generative AI. For example, during interactive training, data on the child's interests can be input into a generative AI, and the generative AI can be used to add game elements.

[0094] During interactive training, the format of the dialogue is customized to suit the child's learning style. For example, if a child prefers visual learning, the dialogue will include many visual elements. Similarly, if a child prefers auditory learning, the dialogue can include many sounds. Furthermore, if a child prefers experiential learning, the dialogue can include experiences that involve actually making sounds. By applying a dialogue format tailored to the learning style, more effective training can be provided. Some or all of the above processing during interactive training may be performed using, for example, generative AI, or not. For example, during interactive training, the child's learning style data can be input into a generative AI, which can then perform the customization of the dialogue format.

[0095] The app estimates the child's emotions and adjusts the app's interface based on the estimated emotions. For example, if the child is having fun, the app provides a brightly colored interface. It can also provide a calmingly colored interface if the child is tired. Furthermore, if the child is concentrating, the app can provide a simple and highly visible interface. This allows for a more user-friendly app by providing an interface that responds to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the app may be performed using, for example, generative AI, or not. For example, the app can input the child's emotion data into a generative AI, which can then perform the interface adjustments.

[0096] When an app is released, user feedback is collected to continuously improve the app's functionality. For example, the app can improve its interface based on user feedback. It can also add new features based on user feedback. Furthermore, the app can optimize existing features based on user feedback. By improving the app's functionality based on user feedback, a more user-friendly app can be provided. Some or all of the above processes in the app may be performed using, for example, generative AI, or not. For example, the app can input user feedback data into a generative AI and have the generative AI perform the function improvements.

[0097] When providing the app, its content is localized according to the culture and language of each region. For example, the app provides an interface that supports the language of each region. The app can also provide training content tailored to the culture of each region. Furthermore, the app can provide functions that are tailored to the education system of each region. By localizing the app according to the culture and language of each region, it can be used to serve a wider range of users. Some or all of the above processes in the app may be performed using, for example, generative AI, or not. For example, the app can input regional culture and language data into a generative AI and have the generative AI perform the localization.

[0098] The app estimates the child's emotions and adjusts the frequency of app use based on the estimated emotions. For example, the app increases usage frequency when the child is enjoying themselves. It can also decrease usage frequency when the child is tired. Furthermore, it can adjust usage frequency when the child is concentrating. This allows for more effective training by providing usage frequency tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the app may be performed using generative AI, or not. For example, the app can input the child's emotion data into a generative AI and have the generative AI perform the adjustment of usage frequency.

[0099] When the app is released, support features for parents and educators will be added. For example, the app will provide parents with a function to check their child's learning progress. The app may also provide educators with a function to customize the training content. Furthermore, the app may provide parents and educators with a function to evaluate the effectiveness of the training. By providing support features for parents and educators, the effectiveness of the training can be enhanced. Some or all of the above processes in the app may be performed using, for example, generative AI, or not. For example, the app can input feedback data from parents and educators into a generative AI, which can then perform the addition of support features.

[0100] When providing the app, it will be integrated with other educational apps and devices to maximize learning effectiveness. For example, the app can integrate with other music education apps to expand training content. The app can also integrate with other devices to enhance training effectiveness. Furthermore, the app can integrate with other educational apps to provide overall learning effectiveness. In this way, learning effectiveness can be maximized by integrating with other educational apps and devices. Some or all of the above processes in the app may be performed using, for example, generative AI, or not using generative AI. For example, the app can input data from other educational apps and devices into a generative AI, which can then perform optimization of the integration.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The perfect pitch training agent system can monitor a child's learning progress in real time and dynamically change the training content according to their progress. For example, if a child can accurately recognize a particular sound, it can provide training to move on to sounds of the next difficulty level. It can also add supplementary training for sounds that a child is struggling with. Furthermore, it can adjust the frequency and duration of training based on the child's learning progress. This allows for the provision of optimal training tailored to the child's learning progress.

[0103] The perfect pitch training agent system can estimate a child's emotions and adjust the training content and methods based on those estimates. For example, if the child is enjoying themselves, it can provide training that incorporates game elements. If the child is tired, it can provide short, effective training sessions. Furthermore, if the child is focused, it can provide longer training sessions. By providing training tailored to the child's emotions, it can achieve more effective learning.

[0104] The perfect pitch training agent system can customize training methods according to a child's learning style. For example, children who prefer visual learning can be provided with training that includes many visual elements. Children who prefer auditory learning can be provided with training that includes many sounds. Furthermore, children who prefer experiential learning can be provided with training that includes hands-on sound production. This allows for the provision of optimal training tailored to each child's learning style.

[0105] The perfect pitch training agent system can optimize the training schedule to match a child's daily rhythm. For example, it can provide training in the morning for morning-oriented children and in the evening for night-owl children. It can also adjust the frequency and duration of training to suit the child's rhythm. This allows for the provision of an optimal training schedule tailored to each child's lifestyle.

[0106] The perfect pitch training agent system can customize training content based on a child's home environment and background information. For example, it can provide training to improve concentration for children training in a quiet environment, and provide short, effective training for children training in a noisy environment. It can also provide training content tailored to the family's culture and language. This allows for the provision of optimal training tailored to each child's home environment and background information.

[0107] The perfect pitch training agent system can estimate a child's emotions and adjust the difficulty of the training based on those emotions. For example, if the child is enjoying themselves, it will provide more difficult training; if the child is tired, it will provide easier training. It can also provide training that gradually increases in difficulty if the child is concentrating. By adjusting the difficulty of the training according to the child's emotions, it is possible to achieve more effective learning.

[0108] The perfect pitch training agent system can customize training content by referencing a child's learning history and performance data. For example, it can provide similar training methods based on past successes and avoid methods that the child has struggled with. It can also identify the most effective time of day and provide training tailored to that time. This allows for the provision of optimal training based on past learning history and performance data.

[0109] The absolute pitch training agent system can estimate a child's emotions and adjust the training delivery method based on those emotions. For example, if the child is having fun, it can provide training with game elements; if the child is tired, it can provide short, effective training sessions. It can also provide longer training sessions if the child is focused. By adjusting the training delivery method according to the child's emotions, it can achieve more effective learning.

[0110] The perfect pitch training agent system can customize training content to reflect a child's learning style and interests. For example, it can provide training with many visual elements for children who prefer visual learning, and training with many auditory elements for children who prefer auditory learning. It can also provide training content related to themes that the child is interested in. This allows for the provision of optimal training tailored to each child's learning style and interests.

[0111] The absolute pitch training agent system can estimate a child's emotions and adjust the timing of training based on those emotions. For example, if the child is enjoying themselves, the training frequency can be increased; if the child is tired, the frequency can be decreased. Furthermore, if the child is concentrating, the training time can be extended. This allows for more effective learning by adjusting the training timing according to the child's emotions.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The analysis unit analyzes the child's learning progress using multimodal AI. The multimodal AI is implemented by combining technologies such as speech recognition, image recognition, and natural language processing. The analysis unit analyzes the child's vocal patterns and facial expression changes to evaluate their learning progress. Step 2: The service provider delivers training at the optimal time and with the most relevant content, based on the information analyzed by the analysis department. The service provider adjusts the timing and content of the training based on the child's learning progress and responses. For example, they might provide training during times when the child is focused, or offer short, effective training sessions when the child is tired. Step 3: The analysis unit analyzes both audio and images. The analysis unit uses audio feature data and image analysis algorithms to understand the child's reactions in detail. For example, it uses speech recognition technology to analyze the child's vocal patterns and image recognition technology to analyze changes in the child's facial expressions.

[0114] 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.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] 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.

[0117] Each of the multiple elements described above, including the analysis unit, provision unit, and analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes the child's vocal patterns and facial expression changes. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides training at the optimal timing and with the appropriate content. The analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes both voice and images. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] 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.

[0133] Each of the multiple elements described above, including the analysis unit, provision unit, and analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and analyzes the child's vocal patterns and facial expression changes. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides training at the optimal timing and with the appropriate content. The analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes both audio and images. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] 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.

[0149] Each of the multiple elements described above, including the analysis unit, provision unit, and analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and analyzes the child's vocal patterns and facial expression changes. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides training at the optimal timing and with the appropriate content. The analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes both audio and images. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] 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.

[0166] Each of the multiple elements described above, including the analysis unit, provision unit, and analysis unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and analyzes the child's vocal patterns and facial expression changes. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides training at the optimal timing and with the appropriate content. The analysis unit is implemented by the control unit 46A of the robot 414 and analyzes both voice and images. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) The analysis department analyzes children's learning progress using multimodal AI, A provisioning unit provides training at an appropriate time and with appropriate content based on the information analyzed by the aforementioned analysis unit. It comprises an analysis unit that analyzes at least one of audio and images. A system characterized by the following features. (Note 2) The agent asked, "What's that sound?" It features an interactive training method that provides feedback such as "Correct!" when a child answers "Do-Mi-So". The system described in Appendix 1, characterized by the features described herein. (Note 3) It is provided as an app. We aim to sell in multiple regions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Analyze both audio and images. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is We estimate the child's emotions and adjust the method of analyzing their learning progress based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is Analyze the child's past training history and select the optimal analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is Apply different analytical methods depending on the child's age and developmental stage. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is The system estimates the child's emotions and prioritizes the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is The analysis is based on the child's living environment and family circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is Customize the analysis method to reflect the child's learning style and interests. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, The system estimates the child's emotions and adjusts the training delivery method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supply unit is, When providing training, the timing of the training will be adjusted based on the child's concentration level and fatigue level. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, When providing training, customize the training content according to the child's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, The system estimates the child's emotions and adjusts the difficulty of the training based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing training, add interactive elements to engage children's interest and attention. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing training, we optimize the training schedule to match the child's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates the child's emotions and adjusts the audio and image analysis methods based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the child's vocal patterns and facial expressions are analyzed in detail to evaluate the effectiveness of the training. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized based on the child's reaction speed and accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, The system estimates the child's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, improve the accuracy of the analysis based on the child's home environment and background information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, the analysis method is customized by referring to the child's learning history and performance data. The system described in Appendix 1, characterized by the features described herein. (Note 23) Interactive training methods are The system estimates the child's emotions and adjusts the content and tone of the conversation based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 24) During interactive training, Provides immediate feedback based on the child's response. The system described in Appendix 2, characterized by the features described herein. (Note 25) Interactive training methods are The system estimates the child's emotions and adjusts the length and frequency of conversations based on those estimates. The system described in Appendix 2, characterized by the features described herein. (Note 26) During interactive training, Add game elements to capture children's interest. The system described in Appendix 2, characterized by the features described herein. (Note 27) During interactive training, Customize the dialogue format to suit your child's learning style. The system described in Appendix 2, characterized by the features described herein. (Note 28) The app is The app estimates the child's emotions and adjusts the app interface based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 29) When providing the app, We collect user feedback to continuously improve the app's features. The system described in Appendix 3, characterized by the features described herein. (Note 30) When providing the app, Localize the app's content to suit the culture and language of each region. The system described in Appendix 3, characterized by the features described herein. (Note 31) The app is The app estimates the child's emotions and adjusts the frequency of app use based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) When providing the app, Add support features for parents and educators. The system described in Appendix 3, characterized by the features described herein. (Note 33) When providing the app, Maximize learning effectiveness by integrating with other educational apps and devices. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes children's learning progress using multimodal AI, A provisioning unit provides training at an appropriate time and with appropriate content based on the information analyzed by the aforementioned analysis unit. It comprises an analysis unit that analyzes at least one of audio and images. A system characterized by the following features.

2. The goal is to offer it as an app and sell it in multiple regions. The system according to feature 1.

3. The aforementioned analysis unit, Analyze both audio and images. The system according to feature 1.

4. The aforementioned analysis unit is We estimate the child's emotions and adjust the method of analyzing their learning progress based on those estimated emotions. The system according to feature 1.

5. The aforementioned analysis unit is Analyze the child's past training history and select the optimal analysis algorithm. The system according to feature 1.

6. The aforementioned analysis unit is Apply different analytical methods depending on the child's age and developmental stage. The system according to feature 1.

7. The aforementioned analysis unit is The system estimates the child's emotions and prioritizes the analysis results based on the estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit is The analysis is based on the child's living environment and family circumstances. The system according to feature 1.

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