System

Abstract

An object of a system according to an embodiment is to enable a parent to effectively support learning and practice of a child at home.SOLUTION: A system includes a lesson teaching material providing part, a curriculum preparation part, a real time support part, an instructor support part, a teaching material preparation support part, and a visualization part. The lesson material providing section provides a lesson material. A curriculum preparation part prepares a curriculum matched with the pace of each house. The real-time support unit supports speaking to, listening to, and watching the child in real time. The instructor support unit supports home learning and practice for an instructor. A teaching material preparation support part prepares a dedicated lesson teaching material for parents. The visualization unit visualizes the amount of learning and practice at home and the weak points of children.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for parents to effectively support their children's learning and practice at home.

[0005] The system according to the embodiment aims to enable parents to effectively support their children's learning and practice at home. [Means for solving the problem]

[0006] The system according to the embodiment includes a lesson material providing unit, a curriculum creating unit, a real-time support unit, an instructor support unit, a teaching material creation support unit, and a visualization unit. The lesson material providing unit provides lesson materials. The curriculum creating unit creates a curriculum tailored to the pace of each family. The real-time support unit provides real-time support for talking to, listening to, and watching over children. The instructor support unit supports instructors in home study and practice. The teaching material creation support unit creates dedicated lesson materials for parents. The visualization unit visualizes the amount of study and practice at home and children's weaknesses. [Effects of the Invention]

[0007] The system according to the embodiment can enable parents to effectively support their children's learning and practice at home. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A coaching support system according to an embodiment of the present invention is a system that allows parents to effectively support their children's learning and practice at home. This system is provided in a BtoBtoC format through businesses that provide cram schools, extracurricular activities, etc., and from the parents' perspective, it can be used as part of the services of the cram school, extracurricular activities, etc. In this way, the coaching support system allows parents to effectively support their children's learning and practice at home.

[0029] A coaching support system according to an embodiment includes a lesson material providing unit, a curriculum creating unit, a real-time support unit, an instructor support unit, a teaching material creation support unit, and a visualization unit. The lesson material providing unit provides lesson materials for parents. For example, the generation AI provides customized lesson materials based on the needs of parents and the characteristics of their children. The generation AI can also provide advice for specific situations. For example, the generation AI can provide advice such as, "How should you encourage your child if they don't want to do their homework?" The curriculum creating unit creates a curriculum for parents tailored to the pace of each family. For example, the generation AI can suggest an optimal curriculum based on the family's situation and the child's learning progress. The generation AI can also generate a curriculum based on the family's schedule and the child's learning progress. The real-time support unit provides real-time support for encouraging, listening to, and monitoring children. For example, the generation AI can provide real-time advice based on the situation the parent is facing. The generation AI can also provide specific advice via audio. The instructor support unit supports instructors in home learning and practice. For example, the generation AI suggests methods for managing the progress of learning and practice at home. The generation AI can also generate support methods based on information and goals provided by the instructor. The teaching material creation support unit supports the creation of specialized lesson materials for parents. For example, the generation AI suggests methods for creating lesson materials tailored to the needs of a specific classroom or team. The generation AI can also generate lesson materials based on information and goals provided by the instructor. The visualization unit visualizes the amount of learning and practice at home and the child's weaknesses. For example, the generation AI analyzes data on learning and practice at home and reports it to parents and instructors. The generation AI can also analyze which areas the child feels weak in. This allows the coaching support system according to the embodiment to effectively support parents in their children's learning and practice at home. For example, by parents giving appropriate encouragement to their children, the child's motivation to learn increases, making their learning and practice at home more effective. Furthermore, by instructors being able to grasp the progress of learning and practice at home, the quality of their instruction improves.

[0030] The lesson material providing unit can analyze the parent's tone of voice and speaking style and suggest how to speak to the child. For example, the generation AI in the lesson material providing unit analyzes the parent's tone of voice and speaking style and suggests the optimal way to speak to the child. For example, if the parent speaks to the child in a harsh tone, the generation AI will advise them to change to a gentler tone. This allows the parent to speak to the child in the optimal way.

[0031] The lesson material provision unit can learn the parent's past history of speaking to their child and provide customized advice based on examples of success and failure. For example, the generation AI can learn the parent's past history of speaking to their child and provide customized advice based on examples of success and failure. For example, it can suggest ways of speaking to their child that have previously elicited positive responses. This allows parents to use their past experience to provide optimal speaking to their child.

[0032] The curriculum creation unit can automatically analyze the family schedule and suggest study times. For example, the generation AI in the curriculum creation unit automatically analyzes the family schedule and suggests optimal study times. For example, it sets optimal study times taking into account parents' work hours and children's school hours. This makes it possible to suggest optimal study times based on the family schedule.

[0033] The curriculum creation unit can monitor a child's learning progress in real time and dynamically adjust the curriculum. For example, the curriculum creation unit monitors a child's learning progress in real time and dynamically adjusts the curriculum as needed. For example, if a child is struggling with a particular subject, the curriculum creation unit suggests a curriculum that focuses on that subject. This allows the curriculum to be dynamically adjusted according to the child's learning progress.

[0034] The real-time support unit analyzes a child's facial expressions and tone of voice and can suggest how to talk to them in real time. For example, the generation AI analyzes a child's facial expressions and tone of voice and suggests the optimal way to talk to them in real time. For example, if a child is tired, the generation AI will suggest words of encouragement. This makes it possible to suggest the optimal way to talk to a child in real time based on their facial expressions and tone of voice.

[0035] The real-time support unit learns the parent's past history of how they interact with their child and can provide customized advice in real time based on examples of success and failure. For example, the real-time support unit uses a generation AI to learn the parent's past history of how they interact with their child and can provide customized advice in real time based on examples of success and failure. For example, it can suggest ways of interacting with their child that have previously elicited positive responses. This makes it possible to suggest optimal ways of interacting with their child in real time based on the parent's past experience.

[0036] The instructor support unit can analyze the instructor's teaching style and propose a home study support method based on that. For example, the generation AI analyzes the instructor's teaching style and proposes a home study support method based on that. For example, if the instructor has a strict style, it will suggest that the support method at home be more flexible. This makes it possible to propose a home study support method based on the instructor's teaching style.

[0037] The instructor support unit can monitor the learning progress at home in real time and provide feedback based on the information provided by the instructor. The instructor support unit can, for example, monitor the learning progress at home in real time and provide feedback based on the information provided by the instructor. For example, it can provide advice in real time according to the child's learning progress. This makes it possible to monitor the learning progress at home in real time and provide feedback based on the information provided by the instructor.

[0038] The teaching materials creation support unit can analyze the needs of a specific classroom or team and propose customized teaching materials based on that. For example, the generative AI can analyze the needs of a specific classroom or team and propose customized teaching materials based on that. For example, it can create teaching materials tailored to the learning goals of a specific classroom. This makes it possible to propose customized teaching materials based on the needs of a specific classroom or team.

[0039] The teaching material creation support unit can learn from the past data of classrooms and teams and provide customized teaching materials based on examples of success and failure. For example, the generative AI can learn from the past data of classrooms and teams and provide customized teaching materials based on examples of success and failure. For example, it can create teaching materials that incorporate learning methods that have been successful in the past. This makes it possible to provide customized teaching materials based on the past data of classrooms and teams.

[0040] The visualization unit can analyze learning data at home and visualize a child's learning patterns. For example, the visualization unit uses a generation AI to analyze learning data at home and visualize a child's learning patterns. For example, it can show the time of day when a child can most concentrate on studying. This makes it possible to visualize a child's learning patterns based on learning data at home.

[0041] The visualization unit can monitor a child's learning progress in real time and identify weak areas. For example, if a child is falling behind in a particular subject, the visualization unit can suggest support focused on that subject. This makes it possible to identify weak areas based on the child's learning progress.

[0042] The visualization unit can visualize the learning data and provide a dashboard that parents can intuitively understand. The visualization unit, for example, visualizes the learning data and provides a dashboard that parents can intuitively understand. For example, the child's learning progress and weak areas are displayed in graphs and charts. This makes it possible to visualize the learning data and provide a dashboard that parents can intuitively understand.

[0043] The visualization unit can provide a platform where parents can share learning data with other parents and exchange advice with each other. The visualization unit can, for example, provide an online forum or chat function. This can provide a platform where parents can share learning data with other parents and exchange advice with each other.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The coaching support system can further include a health monitoring unit that monitors the parent's health condition. The health monitoring unit analyzes the parent's health data and suggests learning support methods according to the parent's health condition. For example, if the parent is tired, it can suggest light exercise or stretching. It can also suggest setting up study time with the child during a time when the parent is in good health. This allows the parent to support their child's learning while remaining healthy.

[0046] The coaching support system can further include a learning style analysis unit that analyzes the parent's learning style. The learning style analysis unit analyzes the parent's learning style and suggests a learning support method based on that analysis. For example, if the parent prefers visual learning, it can suggest that visual learning materials be used more frequently. Also, if the parent prefers auditory learning, it can suggest that audio learning materials be used more frequently. In this way, it is possible to suggest the optimal learning support method based on the parent's learning style.

[0047] The coaching support system can further include a communication analysis unit that analyzes the parent's communication style. The communication analysis unit analyzes the parent's communication style and suggests a learning support method based on that analysis. For example, if the parent prefers interactive communication, it can suggest that the parent emphasize dialogue with the child. Also, if the parent prefers one-sided communication, it can suggest that the parent make instructions clearer. This makes it possible to suggest the optimal learning support method based on the parent's communication style.

[0048] The coaching support system can further include a learning style analysis unit that analyzes the parent's learning style. The learning style analysis unit analyzes the parent's learning style and suggests a learning support method based on that analysis. For example, if the parent prefers visual learning, it can suggest that visual learning materials be used more frequently. Also, if the parent prefers auditory learning, it can suggest that audio learning materials be used more frequently. In this way, it is possible to suggest the optimal learning support method based on the parent's learning style.

[0049] The coaching support system can further include a communication analysis unit that analyzes the parent's communication style. The communication analysis unit analyzes the parent's communication style and suggests a learning support method based on that analysis. For example, if the parent prefers interactive communication, it can suggest that the parent emphasize dialogue with the child. Also, if the parent prefers one-sided communication, it can suggest that the parent make instructions clearer. This makes it possible to suggest the optimal learning support method based on the parent's communication style.

[0050] The coaching support system can further include a health monitoring unit that monitors the parent's health condition. The health monitoring unit analyzes the parent's health data and suggests learning support methods according to the parent's health condition. For example, if the parent is tired, it can suggest light exercise or stretching. It can also suggest setting up study time with the child during a time when the parent is in good health. This allows the parent to support their child's learning while remaining healthy.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The lesson material provider provides lesson materials for parents. For example, the generation AI provides customized lesson materials based on the needs of the parents and the characteristics of the child. The generation AI can also provide advice for specific situations. For example, the generation AI can provide advice such as "How should you encourage your child if they don't want to do their homework?" Step 2: The curriculum creation department creates a curriculum for parents tailored to each family's pace. For example, the generation AI suggests the optimal curriculum based on the family's situation and the child's learning progress. The generation AI can also generate a curriculum based on the family's schedule and the child's learning progress. Step 3: The real-time support section provides real-time support for talking to, listening to, and watching over children. For example, the generation AI can provide real-time advice based on the situation the parent is facing. The generation AI can also provide specific advice via voice. Step 4: The instructor support unit provides support for home learning and practice for instructors. For example, the generative AI suggests ways to manage the progress of home learning and practice. The generative AI can also generate support methods based on the information and goals provided by the instructor. Step 5: The Material Creation Support Unit helps create customized lesson materials for parents. For example, the Generative AI suggests ways to create lesson materials tailored to the needs of a specific classroom or team. The Generative AI can also generate lesson materials based on information and goals provided by instructors. Step 6: The visualization unit visualizes the amount of study and practice at home and the child's weaknesses. For example, the generation AI analyzes data on study and practice at home and reports it to parents and instructors. The generation AI can also analyze which areas the child feels weak in.

[0053] (Example 2) A coaching support system according to an embodiment of the present invention is a system that allows parents to effectively support their children's learning and practice at home. This system is provided in a BtoBtoC format through businesses that provide cram schools, extracurricular activities, etc., and from the parents' perspective, it can be used as part of the services of the cram school, extracurricular activities, etc. In this way, the coaching support system allows parents to effectively support their children's learning and practice at home.

[0054] A coaching support system according to an embodiment includes a lesson material providing unit, a curriculum creating unit, a real-time support unit, an instructor support unit, a teaching material creation support unit, and a visualization unit. The lesson material providing unit provides lesson materials for parents. For example, the generation AI provides customized lesson materials based on the needs of parents and the characteristics of their children. The generation AI can also provide advice for specific situations. For example, the generation AI can provide advice such as, "How should you encourage your child if they don't want to do their homework?" The curriculum creating unit creates a curriculum for parents tailored to the pace of each family. For example, the generation AI can suggest an optimal curriculum based on the family's situation and the child's learning progress. The generation AI can also generate a curriculum based on the family's schedule and the child's learning progress. The real-time support unit provides real-time support for encouraging, listening to, and monitoring children. For example, the generation AI can provide real-time advice based on the situation the parent is facing. The generation AI can also provide specific advice via audio. The instructor support unit supports instructors in home learning and practice. For example, the generation AI suggests methods for managing the progress of learning and practice at home. The generation AI can also generate support methods based on information and goals provided by the instructor. The teaching material creation support unit supports the creation of specialized lesson materials for parents. For example, the generation AI suggests methods for creating lesson materials tailored to the needs of a specific classroom or team. The generation AI can also generate lesson materials based on information and goals provided by the instructor. The visualization unit visualizes the amount of learning and practice at home and the child's weaknesses. For example, the generation AI analyzes data on learning and practice at home and reports it to parents and instructors. The generation AI can also analyze which areas the child feels weak in. This allows the coaching support system according to the embodiment to effectively support parents in their children's learning and practice at home. For example, by parents giving appropriate encouragement to their children, the child's motivation to learn increases, making their learning and practice at home more effective. Furthermore, by instructors being able to grasp the progress of learning and practice at home, the quality of their instruction improves.

[0055] The lesson material providing unit can analyze the parent's tone of voice and speaking style and suggest how to speak to the child. For example, the generation AI in the lesson material providing unit analyzes the parent's tone of voice and speaking style and suggests the optimal way to speak to the child. For example, if the parent speaks to the child in a harsh tone, the generation AI will advise them to change to a gentler tone. This allows the parent to speak to the child in the optimal way.

[0056] The lesson material provision unit can learn the parent's past history of speaking to their child and provide customized advice based on examples of success and failure. For example, the generation AI can learn the parent's past history of speaking to their child and provide customized advice based on examples of success and failure. For example, it can suggest ways of speaking to their child that have previously elicited positive responses. This allows parents to use their past experience to provide optimal speaking to their child.

[0057] The lesson material providing unit can use the emotion estimation function to analyze the parent's emotional state in real time and suggest how to speak to the child. The lesson material providing unit can, for example, use the emotion estimation function to analyze the parent's emotional state in real time and suggest an appropriate way to speak to the child. For example, if the parent is feeling stressed, the generation AI will advise them to speak to the child in a relaxed state. This makes it possible to provide the most appropriate way to speak to the parent according to their emotional state.

[0058] The curriculum creation unit can automatically analyze the family schedule and suggest study times. For example, the generation AI in the curriculum creation unit automatically analyzes the family schedule and suggests optimal study times. For example, it sets optimal study times taking into account parents' work hours and children's school hours. This makes it possible to suggest optimal study times based on the family schedule.

[0059] The curriculum creation unit can monitor a child's learning progress in real time and dynamically adjust the curriculum. For example, the curriculum creation unit monitors a child's learning progress in real time and dynamically adjusts the curriculum as needed. For example, if a child is struggling with a particular subject, the curriculum creation unit suggests a curriculum that focuses on that subject. This allows the curriculum to be dynamically adjusted according to the child's learning progress.

[0060] The curriculum creation unit can use the emotion estimation function to analyze a child's motivation to learn and propose a curriculum that will increase their motivation. For example, the curriculum creation unit can use the emotion estimation function to analyze a child's motivation to learn and propose a curriculum that will increase their motivation. For example, the curriculum creation unit can create a curriculum that incorporates themes that interest the child. This makes it possible to propose a curriculum that will increase their motivation based on the child's motivation to learn.

[0061] The real-time support unit analyzes a child's facial expressions and tone of voice and can suggest how to talk to them in real time. For example, the generation AI analyzes a child's facial expressions and tone of voice and suggests the optimal way to talk to them in real time. For example, if a child is tired, the generation AI will suggest words of encouragement. This makes it possible to suggest the optimal way to talk to a child in real time based on their facial expressions and tone of voice.

[0062] The real-time support unit learns the parent's past history of how they interact with their child and can provide customized advice in real time based on examples of success and failure. For example, the real-time support unit uses a generation AI to learn the parent's past history of how they interact with their child and can provide customized advice in real time based on examples of success and failure. For example, it can suggest ways of interacting with their child that have previously elicited positive responses. This makes it possible to suggest optimal ways of interacting with their child in real time based on the parent's past experience.

[0063] The real-time support unit can use the emotion estimation function to analyze a child's emotional state in real time and suggest how to speak to them. For example, the real-time support unit can use the emotion estimation function to analyze a child's emotional state in real time and suggest an appropriate way to speak to them. For example, if a child is sad, the generation AI can suggest words of comfort. This makes it possible to suggest the most appropriate words to speak to them in real time based on the child's emotional state.

[0064] The instructor support unit can analyze the instructor's teaching style and propose a home study support method based on that. For example, the generation AI analyzes the instructor's teaching style and proposes a home study support method based on that. For example, if the instructor has a strict style, it will suggest that the support method at home be more flexible. This makes it possible to propose a home study support method based on the instructor's teaching style.

[0065] The instructor support unit can monitor the learning progress at home in real time and provide feedback based on the information provided by the instructor. The instructor support unit can, for example, monitor the learning progress at home in real time and provide feedback based on the information provided by the instructor. For example, it can provide advice in real time according to the child's learning progress. This makes it possible to monitor the learning progress at home in real time and provide feedback based on the information provided by the instructor.

[0066] The instructor support unit can use the emotion estimation function to analyze the emotional state of the instructor and propose a support method. For example, the instructor support unit can use the emotion estimation function to analyze the emotional state of the instructor and propose an optimal support method. For example, if the instructor is feeling stressed, the unit can suggest that the instructor provide support in a relaxed state. This makes it possible to propose an optimal support method based on the instructor's emotional state.

[0067] The teaching materials creation support unit can analyze the needs of a specific classroom or team and propose customized teaching materials based on that. For example, the generative AI can analyze the needs of a specific classroom or team and propose customized teaching materials based on that. For example, it can create teaching materials tailored to the learning goals of a specific classroom. This makes it possible to propose customized teaching materials based on the needs of a specific classroom or team.

[0068] The teaching material creation support unit can learn from the past data of classrooms and teams and provide customized teaching materials based on examples of success and failure. For example, the generative AI can learn from the past data of classrooms and teams and provide customized teaching materials based on examples of success and failure. For example, it can create teaching materials that incorporate learning methods that have been successful in the past. This makes it possible to provide customized teaching materials based on the past data of classrooms and teams.

[0069] The teaching material creation support unit can analyze the emotional state of the parent using the emotion estimation function and suggest the most suitable teaching material. The teaching material creation support unit can, for example, analyze the emotional state of the parent using the emotion estimation function and suggest the most suitable teaching material. For example, it can suggest teaching materials to be used during times when the parent is relaxed. In this way, it is possible to suggest the most suitable teaching material based on the emotional state of the parent.

[0070] The visualization unit can analyze learning data at home and visualize a child's learning patterns. For example, the visualization unit uses a generation AI to analyze learning data at home and visualize a child's learning patterns. For example, it can show the time of day when a child can most concentrate on studying. This makes it possible to visualize a child's learning patterns based on learning data at home.

[0071] The visualization unit can monitor a child's learning progress in real time and identify weak areas. For example, if a child is falling behind in a particular subject, the visualization unit can suggest support focused on that subject. This makes it possible to identify weak areas based on the child's learning progress.

[0072] The visualization unit can use the emotion estimation function to analyze the child's emotional state and suggest ways to improve weak areas. For example, if a child has negative feelings about a particular subject, the visualization unit can suggest ways to change their approach to that subject. This makes it possible to suggest ways to improve weak areas based on the child's emotional state.

[0073] The visualization unit can visualize the learning data and provide a dashboard that parents can intuitively understand. The visualization unit, for example, visualizes the learning data and provides a dashboard that parents can intuitively understand. For example, the child's learning progress and weak areas are displayed in graphs and charts. This makes it possible to visualize the learning data and provide a dashboard that parents can intuitively understand.

[0074] The visualization unit can provide a platform where parents can share learning data with other parents and exchange advice with each other. The visualization unit can, for example, provide an online forum or chat function. This can provide a platform where parents can share learning data with other parents and exchange advice with each other.

[0075] The visualization unit can use the emotion estimation function to analyze the emotional states of the parent and child and suggest an optimal study method. For example, the visualization unit can use the emotion estimation function to analyze the emotional states of the parent and child and suggest an optimal study method. For example, it can suggest that the parent and child should study at a time when they are relaxed. This makes it possible to suggest an optimal study method based on the emotional states of the parent and child.

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

[0077] The coaching support system can further include a stress monitoring unit that monitors the parent's stress level. The stress monitoring unit analyzes the parent's stress level in real time and suggests relaxation methods if stress is high. For example, if the parent is feeling stressed, it can advise the parent to take deep breaths or a short break. It can also suggest setting up study time with the child during times when stress is low. This allows the parent to support their child's learning in a relaxed state.

[0078] The coaching support system can further include a health monitoring unit that monitors the parent's health condition. The health monitoring unit analyzes the parent's health data and suggests learning support methods according to the parent's health condition. For example, if the parent is tired, it can suggest light exercise or stretching. It can also suggest setting up study time with the child during a time when the parent is in good health. This allows the parent to support their child's learning while remaining healthy.

[0079] The coaching support system can further include a learning style analysis unit that analyzes the parent's learning style. The learning style analysis unit analyzes the parent's learning style and suggests a learning support method based on that analysis. For example, if the parent prefers visual learning, it can suggest that visual learning materials be used more frequently. Also, if the parent prefers auditory learning, it can suggest that audio learning materials be used more frequently. In this way, it is possible to suggest the optimal learning support method based on the parent's learning style.

[0080] The coaching support system can further include an emotion analysis unit that analyzes the parent's emotional state and suggests a learning support method based on the emotion. The emotion analysis unit analyzes the parent's emotional state in real time and suggests a learning support method based on the emotion. For example, if the parent is feeling positive, it can suggest encouraging the child to study in a relaxed state. If the parent is feeling negative, it can also suggest studying in a relaxed state. This makes it possible to suggest the optimal learning support method based on the parent's emotional state.

[0081] The coaching support system can further include a communication analysis unit that analyzes the parent's communication style. The communication analysis unit analyzes the parent's communication style and suggests a learning support method based on that analysis. For example, if the parent prefers interactive communication, it can suggest that the parent emphasize dialogue with the child. Also, if the parent prefers one-sided communication, it can suggest that the parent make instructions clearer. This makes it possible to suggest the optimal learning support method based on the parent's communication style.

[0082] The coaching support system can further include an emotion analysis unit that analyzes the parent's emotional state and suggests a learning support method based on the emotion. The emotion analysis unit analyzes the parent's emotional state in real time and suggests a learning support method based on the emotion. For example, if the parent is feeling positive, it can suggest encouraging the child to study in a relaxed state. If the parent is feeling negative, it can also suggest studying in a relaxed state. This makes it possible to suggest the optimal learning support method based on the parent's emotional state.

[0083] The coaching support system can further include a learning style analysis unit that analyzes the parent's learning style. The learning style analysis unit analyzes the parent's learning style and suggests a learning support method based on that analysis. For example, if the parent prefers visual learning, it can suggest that visual learning materials be used more frequently. Also, if the parent prefers auditory learning, it can suggest that audio learning materials be used more frequently. In this way, it is possible to suggest the optimal learning support method based on the parent's learning style.

[0084] The coaching support system can further include a communication analysis unit that analyzes the parent's communication style. The communication analysis unit analyzes the parent's communication style and suggests a learning support method based on that analysis. For example, if the parent prefers interactive communication, it can suggest that the parent emphasize dialogue with the child. Also, if the parent prefers one-sided communication, it can suggest that the parent make instructions clearer. This makes it possible to suggest the optimal learning support method based on the parent's communication style.

[0085] The coaching support system can further include an emotion analysis unit that analyzes the parent's emotional state and suggests a learning support method based on the emotion. The emotion analysis unit analyzes the parent's emotional state in real time and suggests a learning support method based on the emotion. For example, if the parent is feeling positive, it can suggest encouraging the child to study in a relaxed state. If the parent is feeling negative, it can also suggest studying in a relaxed state. This makes it possible to suggest the optimal learning support method based on the parent's emotional state.

[0086] The coaching support system can further include a health monitoring unit that monitors the parent's health condition. The health monitoring unit analyzes the parent's health data and suggests learning support methods according to the parent's health condition. For example, if the parent is tired, it can suggest light exercise or stretching. It can also suggest setting up study time with the child during a time when the parent is in good health. This allows the parent to support their child's learning while remaining healthy.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The lesson material provider provides lesson materials for parents. For example, the generation AI provides customized lesson materials based on the needs of the parents and the characteristics of the child. The generation AI can also provide advice for specific situations. For example, the generation AI can provide advice such as "How should you encourage your child if they don't want to do their homework?" Step 2: The curriculum creation department creates a curriculum for parents tailored to each family's pace. For example, the generation AI suggests the optimal curriculum based on the family's situation and the child's learning progress. The generation AI can also generate a curriculum based on the family's schedule and the child's learning progress. Step 3: The real-time support section provides real-time support for talking to, listening to, and watching over children. For example, the generation AI can provide real-time advice based on the situation the parent is facing. The generation AI can also provide specific advice via voice. Step 4: The instructor support unit provides support for home learning and practice for instructors. For example, the generative AI suggests ways to manage the progress of home learning and practice. The generative AI can also generate support methods based on the information and goals provided by the instructor. Step 5: The Material Creation Support Unit helps create customized lesson materials for parents. For example, the Generative AI suggests ways to create lesson materials tailored to the needs of a specific classroom or team. The Generative AI can also generate lesson materials based on information and goals provided by instructors. Step 6: The visualization unit visualizes the amount of study and practice at home and the child's weaknesses. For example, the generation AI analyzes data on study and practice at home and reports it to parents and instructors. The generation AI can also analyze which areas the child feels weak in.

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

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0100] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0102] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0110] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, a 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0130] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0139] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0140] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0141] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0142] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0143] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0144] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0145] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0146] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0147] 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.

[0148] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0149] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0150] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0151] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0152] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0153] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0154] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0155] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0156] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a lesson material providing department that provides lesson materials; A curriculum development department creates a curriculum tailored to each family's pace, The real-time support department provides real-time support for talking to, listening to, and watching over children. The instructor support department provides support for instructors in home study and practice, A support team to create dedicated lesson materials for parents, A visualization unit that visualizes the amount of study and practice at home and the child's weaknesses. A system characterized by:

2. The lesson material providing unit Analyzing the emotional state of the parent in real time and suggesting the method of speaking to the parent 2. The system of claim 1.

3. The curriculum creation department Automatically analyze the family schedule and suggest study times 2. The system of claim 1.

4. The real-time support unit Analyzing the child's facial expression and tone of voice, and suggesting the method of speaking to the child in real time.

2. The system of claim 1.

5. The instructor support department: Analyzing instructors' teaching styles and proposing home learning support methods based on those analysis 2. The system of claim 1.

6. The teaching material creation support unit Analyze specific classroom or team needs and suggest customized learning materials based on those needs 2. The system of claim 1.

7. The visualization unit Analyzing the learning data at home and visualizing the child's learning pattern 2. The system of claim 1.

8. The visualization unit Analyze the child's emotional state and suggest ways to improve areas of weakness 2. The system of claim 1.

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