System

Abstract

An object of the system according to the embodiment is to provide individual learning assistance and mental support to students who are not going to school.SOLUTION: A system according to an embodiment includes a generation AI, a platform, a learning support unit, a mental support unit, and a community unit. The generative AI leverages the learning assister and the mental support. A learning support part analyzes the learning progress and degree of understanding of the student by the generation AI and proposes an individual learning plan. The mental support part listens to feelings and worries of the student by the generated AI and gives encouragement and advice. The community unit provides a function for communicating with other students and teachers through the platform.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 does not provide sufficient individual learning support or mental support to students who are not attending school, and there is room for improvement.

[0005] The system according to the embodiment aims to provide individual learning assistance and mental support to students who are not attending school. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation AI, a platform, a learning support unit, a mental support unit, and a community unit. The generation AI utilizes the learning support unit and mental support unit. The learning support unit uses the generation AI to analyze students' learning progress and level of understanding and proposes individual learning plans. The mental support unit uses the generation AI to listen to students' feelings and concerns and provide encouragement and advice. The community unit provides functions for communicating with other students and teachers through the platform. [Effects of the Invention]

[0007] The system according to the embodiment can provide individual learning assistance and mental support to students who are not attending school. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The support system according to an embodiment of the present invention is a generative AI (My Buddy) for supporting students who are not attending school, and a platform (Generative Stage) that utilizes the generative AI. As a result, the support system enables students who are not attending school to study and communicate smoothly.

[0029] The support system according to the embodiment includes a generation AI, a platform, a learning support unit, a mental support unit, and a community unit. The generation AI supports students through the learning support unit, which analyzes students' learning progress and comprehension and proposes individual learning plans. For example, the generation AI analyzes students' past learning data and suggests what they should study next. The generation AI also supports students through the mental support unit, which listens to students' feelings and concerns and offers encouragement and advice. For example, if a student confides in the generation AI that they are afraid of going to school, the generation AI empathizes with their feelings and provides appropriate advice. The platform promotes communication between students and with teachers through a community unit for communicating with other students and teachers. For example, students can share information about their studies and ask questions with other students through forums and chat functions. Individual consultations with teachers are also possible, allowing students to receive advice on their studies and daily life. This allows the support system to facilitate smooth learning and communication for students who are absent from school.

[0030] The learning support unit can predict future learning progress based on a student's learning history and propose long-term learning plans. For example, the learning support unit uses a generation AI to analyze a student's past learning data and predict future learning progress. For example, it suggests what to study next based on past test results and study time. The learning support unit also uses a generation AI to create long-term learning plans based on the student's learning history. For example, it sets annual learning goals and proposes monthly learning plans based on those goals. The learning support unit also uses a generation AI to analyze a student's learning history and develop an algorithm to predict future learning progress. For example, it suggests what to study next and what needs to be reviewed based on past learning patterns. This makes it possible to predict a student's learning progress and provide a long-term learning plan.

[0031] The learning support unit can analyze students' hobbies and interests and suggest learning content based on them. For example, the learning support unit uses a generating AI to analyze students' hobbies and interests and suggest learning content based on them. For example, it could provide learning materials related to a student's favorite sport or music. The learning support unit also uses a generating AI to create customized learning content based on the student's hobbies and interests. For example, it could suggest workbooks or videos related to topics of interest. The learning support unit also develops an algorithm that uses a generating AI to analyze students' hobbies and interests and suggest learning content based on them. For example, it could incorporate topics related to hobbies into a learning plan. This makes it possible to provide learning content based on a student's hobbies and interests.

[0032] The learning support unit can consider a student's home environment and lifestyle to suggest optimal study times and break times. For example, the learning support unit uses a generation AI to analyze a student's home environment and lifestyle to suggest optimal study times and break times. For example, it creates a study schedule that suits family circumstances and lifestyle patterns. The learning support unit also uses a generation AI to suggest optimal study times and break times based on the student's lifestyle. For example, it provides a night study plan for night owls. The learning support unit also develops an algorithm that considers a student's home environment and lifestyle to suggest optimal study times and break times. For example, it creates a flexible study schedule that suits family circumstances. This makes it possible to provide study times and break times based on the student's home environment and lifestyle.

[0033] The platform can analyze student activity data and provide a dashboard that evaluates learning progress and communication quality. The platform, for example, analyzes student activity data on the platform and provides a dashboard that evaluates learning progress and communication quality. For example, it visualizes study time and communication frequency. The platform also creates a dashboard that evaluates learning progress and communication quality based on student activity data. For example, it displays learning achievement and communication activity in graphs. The platform also analyzes student activity data on the platform and develops an algorithm that provides a dashboard that evaluates learning progress and communication quality. For example, it analyzes activity data in real time and reflects it in the dashboard. This makes it possible to analyze student activity data and evaluate learning progress and communication quality.

[0034] The platform may provide a function for automatically matching students with common interests and goals. For example, the platform may add a function for automatically matching students with common interests and goals on the platform. For example, it may connect students with similar hobbies or learning goals. The platform may also develop an algorithm for automatically matching students with common interests and goals based on student profile data. For example, it may perform matching based on hobbies or learning goals. The platform may also add a function for automatically matching students with common interests and goals on the platform to promote communication. For example, it may provide a function that allows students with similar hobbies to chat with each other. This may enable automatic matching of students with similar interests and goals.

[0035] The platform may add a virtual reality (VR) function to enable learning and communication in a virtual classroom. For example, the platform may add a virtual reality (VR) function to the platform to enable learning and communication in a virtual classroom. For example, a VR headset may be used to participate in the virtual classroom. The platform may also develop an algorithm that uses the virtual reality (VR) function to enable learning and communication in the virtual classroom. For example, the platform may provide interactive lessons in a virtual space. The platform may also add a virtual reality (VR) function to build a system that enables learning and communication in the virtual classroom. For example, the platform may enable group discussions in the virtual classroom. This enables learning and communication in the virtual classroom.

[0036] The platform can provide learning content in formats that accommodate different learning styles. For example, the platform provides learning content on the platform in formats that accommodate visual, auditory, and tactile input. For example, the platform may provide videos, audio, and interactive simulations. The platform also develops algorithms that provide learning content that accommodates different learning styles. For example, the platform may provide diagrams and graphs for visual learners and audio commentary for auditory learners. The platform also builds a system that provides learning content on the platform in formats that accommodate different learning styles. For example, the platform may provide interactive experiments for tactile learners. This makes it possible to provide learning content that accommodates different learning styles.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The support system can also be equipped with a health management module that monitors students' physical health and suggests appropriate exercise and rest. For example, the generative AI can analyze students' activity levels and sleep patterns and suggest light exercise if it detects a lack of exercise. It can also send notifications encouraging students to stretch or take a break after long periods of study. Furthermore, the health management module can analyze students' food records and suggest nutritionally balanced meal plans. This can support students' physical health and improve their learning efficiency.

[0039] The learning support department can provide customizable learning tools tailored to each student's learning style. For example, they can provide infographics and video materials for visual learners, podcasts and audio commentaries for auditory learners, and interactive simulations and experiment kits for tactile learners. Furthermore, the learning support department can continuously improve the learning tools based on student feedback to provide a more effective learning experience. This allows them to provide learning tools that best suit each student's learning style and maximize learning outcomes.

[0040] The learning support department can analyze students' hobbies and interests and suggest learning content based on them. For example, the generative AI analyzes students' hobbies and interests and suggests learning content based on them. For example, it could provide learning materials related to their favorite sports or music. The learning support department also uses the generative AI to create customized learning content based on students' hobbies and interests. For example, it could suggest workbooks or videos related to topics of interest. The generative AI also analyzes students' hobbies and interests and develops an algorithm to suggest learning content based on them. For example, it could incorporate topics related to hobbies into a learning plan. This makes it possible to provide learning content based on students' hobbies and interests.

[0041] The learning support department can consider students' home environments and lifestyles to suggest optimal study times and break times. For example, the generation AI analyzes students' home environments and lifestyles to suggest optimal study times and break times. For example, it creates a study schedule that suits family circumstances and lifestyle patterns. The learning support department also uses the generation AI to suggest optimal study times and break times based on students' lifestyles. For example, it provides night-time study plans for night owls. The generation AI also develops an algorithm that considers students' home environments and lifestyles to suggest optimal study times and break times. For example, it creates a flexible study schedule that suits family circumstances. This makes it possible to provide study times and break times based on students' home environments and lifestyles.

[0042] The platform can analyze student activity data and provide a dashboard that evaluates learning progress and communication quality. For example, student activity data on the platform can be analyzed to provide a dashboard that evaluates learning progress and communication quality. For example, study time and communication frequency can be visualized. Furthermore, a dashboard that evaluates learning progress and communication quality can be created based on student activity data. For example, learning achievement and communication activity can be displayed in graphs. Furthermore, an algorithm can be developed that analyzes student activity data on the platform and provides a dashboard that evaluates learning progress and communication quality. For example, activity data can be analyzed in real time and reflected in a dashboard. This makes it possible to analyze student activity data and evaluate learning progress and communication quality.

[0043] The platform may provide a function to automatically match students with common interests and goals. For example, a function to automatically match students with common interests and goals may be added to the platform. For example, students with similar hobbies or learning goals may be connected. In addition, an algorithm may be developed to automatically match students with common interests and goals based on student profile data. For example, matching may be performed based on hobbies or learning goals. In addition, a function to automatically match students with common interests and goals may be added to the platform to promote communication. For example, a function may be provided that allows students with similar hobbies to chat with each other. This may allow students with similar interests and goals to be automatically matched.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The generative AI analyzes the student's learning progress and level of understanding and proposes an individual learning plan. For example, the generative AI analyzes the student's past learning data and suggests what they should study next. Step 2: The learning support department proposes an individual learning plan based on the student's learning progress and level of understanding analyzed by the generation AI, allowing the student to receive the learning plan that is best suited to them. Step 3: The mental support department uses the generated AI to listen to the student's feelings and worries, and offers encouragement and advice. For example, if a student confides that they are afraid of going to school, the AI ​​will empathize with their feelings and provide appropriate advice. Step 4: The Community Department provides functions for communicating with other students and teachers through the platform. For example, students can share information about their studies with other students and ask questions through forums and chat functions. They can also have individual consultations with teachers to receive advice on their studies and daily life.

[0046] (Example 2) The support system according to an embodiment of the present invention is a generative AI (My Buddy) for supporting students who are not attending school, and a platform (Generative Stage) that utilizes the generative AI. As a result, the support system enables students who are not attending school to study and communicate smoothly.

[0047] The support system according to the embodiment includes a generation AI, a platform, a learning support unit, a mental support unit, and a community unit. The generation AI supports students through the learning support unit, which analyzes students' learning progress and comprehension and proposes individual learning plans. For example, the generation AI analyzes students' past learning data and suggests what they should study next. The generation AI also supports students through the mental support unit, which listens to students' feelings and concerns and offers encouragement and advice. For example, if a student confides in the generation AI that they are afraid of going to school, the generation AI empathizes with their feelings and provides appropriate advice. The platform promotes communication between students and with teachers through a community unit for communicating with other students and teachers. For example, students can share information about their studies and ask questions with other students through forums and chat functions. Individual consultations with teachers are also possible, allowing students to receive advice on their studies and daily life. This allows the support system to facilitate smooth learning and communication for students who are absent from school.

[0048] The learning support unit can predict future learning progress based on a student's learning history and propose long-term learning plans. For example, the learning support unit uses a generation AI to analyze a student's past learning data and predict future learning progress. For example, it suggests what to study next based on past test results and study time. The learning support unit also uses a generation AI to create long-term learning plans based on the student's learning history. For example, it sets annual learning goals and proposes monthly learning plans based on those goals. The learning support unit also uses a generation AI to analyze a student's learning history and develop an algorithm to predict future learning progress. For example, it suggests what to study next and what needs to be reviewed based on past learning patterns. This makes it possible to predict a student's learning progress and provide a long-term learning plan.

[0049] The mental support unit monitors students' emotional state in real time and can dynamically adjust learning content and advice according to changes in emotion. For example, the mental support unit uses a generative AI to analyze students' facial expressions and voices and monitor their emotional state in real time. For example, it uses a camera or microphone to detect students' emotions and adjust learning content accordingly. The mental support unit also uses a generative AI to dynamically adjust learning content and advice based on the student's emotional state. For example, if a student is feeling stressed, it will suggest content that will help them relax. The mental support unit also develops an algorithm that uses a generative AI to monitor students' emotional state in real time and adjusts learning plans according to changes in emotion. For example, if positive emotions are strong, it will suggest more difficult tasks. This makes it possible to dynamically adjust learning content and advice according to the student's emotional state.

[0050] The learning support unit can use the emotion estimation function to provide a study plan that incorporates game elements to improve motivation based on the student's emotions. For example, the learning support unit uses a generation AI to analyze the student's emotional state and provide a study plan that incorporates game elements to improve motivation. For example, a point system or level-up function is introduced. The learning support unit also uses the emotion estimation function to create a study plan that incorporates game elements based on the student's emotions. For example, if positive emotions are strong, challenging tasks are suggested. The learning support unit also develops an algorithm that uses a generation AI to analyze the student's emotional state and provide a study plan that incorporates game elements to improve motivation. For example, rewards are set according to the emotion score. This makes it possible to provide a study plan that improves motivation based on the student's emotions.

[0051] The learning support unit can analyze students' hobbies and interests and suggest learning content based on them. For example, the learning support unit uses a generating AI to analyze students' hobbies and interests and suggest learning content based on them. For example, it could provide learning materials related to a student's favorite sport or music. The learning support unit also uses a generating AI to create customized learning content based on the student's hobbies and interests. For example, it could suggest workbooks or videos related to topics of interest. The learning support unit also develops an algorithm that uses a generating AI to analyze students' hobbies and interests and suggest learning content based on them. For example, it could incorporate topics related to hobbies into a learning plan. This makes it possible to provide learning content based on a student's hobbies and interests.

[0052] The learning support unit can consider a student's home environment and lifestyle to suggest optimal study times and break times. For example, the learning support unit uses a generation AI to analyze a student's home environment and lifestyle to suggest optimal study times and break times. For example, it creates a study schedule that suits family circumstances and lifestyle patterns. The learning support unit also uses a generation AI to suggest optimal study times and break times based on the student's lifestyle. For example, it provides a night study plan for night owls. The learning support unit also develops an algorithm that considers a student's home environment and lifestyle to suggest optimal study times and break times. For example, it creates a flexible study schedule that suits family circumstances. This makes it possible to provide study times and break times based on the student's home environment and lifestyle.

[0053] The mental support department can use the emotion estimation function to provide content for relaxation and stress relief that matches the student's emotions. For example, the generation AI in the mental support department analyzes the student's emotional state and provides content for relaxation and stress relief. For example, it can suggest relaxing music or meditation guides. The mental support department also uses the emotion estimation function to create content for relaxation and stress relief that matches the student's emotions. For example, it can provide relaxing videos if the student is feeling stressed. The mental support department also develops an algorithm in which the generation AI analyzes the student's emotional state and provides content for relaxation and stress relief. For example, it can suggest appropriate relaxation methods based on the emotion score. This makes it possible to provide content for relaxation and stress relief that matches the student's emotions.

[0054] The platform can analyze student activity data and provide a dashboard that evaluates learning progress and communication quality. The platform, for example, analyzes student activity data on the platform and provides a dashboard that evaluates learning progress and communication quality. For example, it visualizes study time and communication frequency. The platform also creates a dashboard that evaluates learning progress and communication quality based on student activity data. For example, it displays learning achievement and communication activity in graphs. The platform also analyzes student activity data on the platform and develops an algorithm that provides a dashboard that evaluates learning progress and communication quality. For example, it analyzes activity data in real time and reflects it in the dashboard. This makes it possible to analyze student activity data and evaluate learning progress and communication quality.

[0055] The platform may provide a function for automatically matching students with common interests and goals. For example, the platform may add a function for automatically matching students with common interests and goals on the platform. For example, it may connect students with similar hobbies or learning goals. The platform may also develop an algorithm for automatically matching students with common interests and goals based on student profile data. For example, it may perform matching based on hobbies or learning goals. The platform may also add a function for automatically matching students with common interests and goals on the platform to promote communication. For example, it may provide a function that allows students with similar hobbies to chat with each other. This may enable automatic matching of students with similar interests and goals.

[0056] The platform can use the emotion estimation function to visualize students' emotional states, allowing teachers and counselors to provide appropriate support. The platform, for example, uses the emotion estimation function to visualize students' emotional states on the platform. For example, emotion scores can be displayed as graphs or colors, allowing teachers and counselors to understand at a glance. The platform also analyzes students' emotional states in real time and visualizes them on the platform. For example, emotion changes can be displayed along a timeline, allowing teachers and counselors to provide support at the appropriate time. The platform also uses the emotion estimation function to visualize students' emotional states on the platform and develop algorithms that enable teachers and counselors to provide appropriate support. For example, a function to issue alerts based on emotion scores can be added. This visualizes students' emotional states, allowing teachers and counselors to provide appropriate support.

[0057] The platform may add a virtual reality (VR) function to enable learning and communication in a virtual classroom. For example, the platform may add a virtual reality (VR) function to the platform to enable learning and communication in a virtual classroom. For example, a VR headset may be used to participate in the virtual classroom. The platform may also develop an algorithm that uses the virtual reality (VR) function to enable learning and communication in the virtual classroom. For example, the platform may provide interactive lessons in a virtual space. The platform may also add a virtual reality (VR) function to build a system that enables learning and communication in the virtual classroom. For example, the platform may enable group discussions in the virtual classroom. This enables learning and communication in the virtual classroom.

[0058] The platform can provide learning content in formats that accommodate different learning styles. For example, the platform provides learning content on the platform in formats that accommodate visual, auditory, and tactile input. For example, the platform may provide videos, audio, and interactive simulations. The platform also develops algorithms that provide learning content that accommodates different learning styles. For example, the platform may provide diagrams and graphs for visual learners and audio commentary for auditory learners. The platform also builds a system that provides learning content on the platform in formats that accommodate different learning styles. For example, the platform may provide interactive experiments for tactile learners. This makes it possible to provide learning content that accommodates different learning styles.

[0059] The platform can use the emotion estimation function to provide a customizable learning environment based on the student's emotions. For example, the platform uses the emotion estimation function to provide a customizable learning environment based on the student's emotions. For example, the platform suggests relaxing background music or color settings. The platform also analyzes the student's emotional state and develops an algorithm to provide a customizable learning environment. For example, the platform dynamically adjusts the learning environment according to the emotion score. The platform also uses the emotion estimation function to build a system that provides a customizable learning environment based on the student's emotions. For example, if the student is feeling stressed, the platform suggests a relaxing environment. This makes it possible to provide a customizable learning environment based on the student's emotions.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The support system can also be equipped with a health management module that monitors students' physical health and suggests appropriate exercise and rest. For example, the generative AI can analyze students' activity levels and sleep patterns and suggest light exercise if it detects a lack of exercise. It can also send notifications encouraging students to stretch or take a break after long periods of study. Furthermore, the health management module can analyze students' food records and suggest nutritionally balanced meal plans. This can support students' physical health and improve their learning efficiency.

[0062] The learning support department can provide customizable learning tools tailored to each student's learning style. For example, they can provide infographics and video materials for visual learners, podcasts and audio commentaries for auditory learners, and interactive simulations and experiment kits for tactile learners. Furthermore, the learning support department can continuously improve the learning tools based on student feedback to provide a more effective learning experience. This allows them to provide learning tools that best suit each student's learning style and maximize learning outcomes.

[0063] The mental support department can estimate a student's emotional state and provide relaxation and stress relief content based on the estimated emotions. For example, generative AI can analyze a student's emotional state and suggest relaxing music or meditation guides. It can also provide relaxation videos or stress relief activities according to the student's emotional state. Furthermore, the mental support department can develop an algorithm that monitors a student's emotional state in real time and provides appropriate support in response to changes in emotions. This makes it possible to provide relaxation and stress relief content according to the student's emotional state.

[0064] The learning support unit can use the emotion estimation function to provide learning plans that incorporate game elements to improve motivation based on students' emotions. For example, the generation AI analyzes the student's emotional state and provides learning plans that incorporate game elements to improve motivation. For example, a point system or level-up function can be introduced. The emotion estimation function can also be used to create learning plans that incorporate game elements based on the student's emotions. For example, challenging tasks can be suggested if positive emotions are strong. The generation AI can also analyze the student's emotional state and develop an algorithm that provides learning plans that incorporate game elements to improve motivation. For example, rewards can be set according to the emotional score. This makes it possible to provide learning plans that improve motivation based on the student's emotions.

[0065] The learning support department can analyze students' hobbies and interests and suggest learning content based on them. For example, the generative AI analyzes students' hobbies and interests and suggests learning content based on them. For example, it could provide learning materials related to their favorite sports or music. The learning support department also uses the generative AI to create customized learning content based on students' hobbies and interests. For example, it could suggest workbooks or videos related to topics of interest. The generative AI also analyzes students' hobbies and interests and develops an algorithm to suggest learning content based on them. For example, it could incorporate topics related to hobbies into a learning plan. This makes it possible to provide learning content based on students' hobbies and interests.

[0066] The learning support department can consider students' home environments and lifestyles to suggest optimal study times and break times. For example, the generation AI analyzes students' home environments and lifestyles to suggest optimal study times and break times. For example, it creates a study schedule that suits family circumstances and lifestyle patterns. The learning support department also uses the generation AI to suggest optimal study times and break times based on students' lifestyles. For example, it provides night-time study plans for night owls. The generation AI also develops an algorithm that considers students' home environments and lifestyles to suggest optimal study times and break times. For example, it creates a flexible study schedule that suits family circumstances. This makes it possible to provide study times and break times based on students' home environments and lifestyles.

[0067] The Mental Support Department can use the emotion estimation function to provide content for relaxation and stress relief that matches the student's emotions. For example, the generation AI analyzes the student's emotional state and provides content for relaxation and stress relief. For example, it can suggest relaxing music or meditation guides. The emotion estimation function can also be used to create content for relaxation and stress relief that matches the student's emotions. For example, if the student is feeling stressed, it can provide a relaxing video. The generation AI can also analyze the student's emotional state and develop an algorithm to provide content for relaxation and stress relief. For example, it can suggest an appropriate relaxation method based on the emotion score. This makes it possible to provide content for relaxation and stress relief that matches the student's emotions.

[0068] The platform can analyze student activity data and provide a dashboard that evaluates learning progress and communication quality. For example, student activity data on the platform can be analyzed to provide a dashboard that evaluates learning progress and communication quality. For example, study time and communication frequency can be visualized. Furthermore, a dashboard that evaluates learning progress and communication quality can be created based on student activity data. For example, learning achievement and communication activity can be displayed in graphs. Furthermore, an algorithm can be developed that analyzes student activity data on the platform and provides a dashboard that evaluates learning progress and communication quality. For example, activity data can be analyzed in real time and reflected in a dashboard. This makes it possible to analyze student activity data and evaluate learning progress and communication quality.

[0069] The platform may provide a function to automatically match students with common interests and goals. For example, a function to automatically match students with common interests and goals may be added to the platform. For example, students with similar hobbies or learning goals may be connected. In addition, an algorithm may be developed to automatically match students with common interests and goals based on student profile data. For example, matching may be performed based on hobbies or learning goals. In addition, a function to automatically match students with common interests and goals may be added to the platform to promote communication. For example, a function may be provided that allows students with similar hobbies to chat with each other. This may allow students with similar interests and goals to be automatically matched.

[0070] The platform can use the emotion estimation function to visualize students' emotional states, allowing teachers and counselors to provide appropriate support. For example, the emotion estimation function is used to visualize students' emotional states on the platform. For example, emotion scores can be displayed as graphs or colors, allowing teachers and counselors to understand at a glance. Students' emotional states can also be analyzed in real time and visualized on the platform. For example, changes in emotions can be displayed over time, allowing teachers and counselors to provide support at the appropriate time. The emotion estimation function can also be used to develop algorithms that visualize students' emotional states on the platform and allow teachers and counselors to provide appropriate support. For example, a function to issue alerts based on emotion scores can be added. This visualizes students' emotional states, allowing teachers and counselors to provide appropriate support.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The generative AI analyzes the student's learning progress and level of understanding and proposes an individual learning plan. For example, the generative AI analyzes the student's past learning data and suggests what they should study next. Step 2: The learning support department proposes an individual learning plan based on the student's learning progress and level of understanding analyzed by the generation AI, allowing the student to receive the learning plan that is best suited to them. Step 3: The mental support department uses the generated AI to listen to the student's feelings and worries, and offers encouragement and advice. For example, if a student confides that they are afraid of going to school, the AI ​​will empathize with their feelings and provide appropriate advice. Step 4: The Community Department provides functions for communicating with other students and teachers through the platform. For example, students can share information about their studies with other students and ask questions through forums and chat functions. They can also have individual consultations with teachers to receive advice on their studies and daily life.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0086] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0101] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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. Generative AI and A platform that utilizes the generative AI; A learning support department that uses the generation AI to analyze students' learning progress and level of understanding and proposes individual learning plans; The mental support department uses the AI ​​to listen to students' feelings and concerns and provide encouragement and advice. A community section for communicating with other students and teachers through the platform. A system characterized by:

2. The learning support unit Based on the student's learning history, predict future learning progress and propose long-term learning plans.

2. The system of claim 1.

3. The mental support department The emotional state of the student is monitored in real time, and the learning content and the advice are dynamically adjusted according to changes in the student's emotions.

2. The system of claim 1.

4. The learning support unit Providing the learning plan incorporating game elements to improve the students' emotional motivation 2. The system of claim 1.

5. The learning support unit Analyze the student's hobbies and interests and suggest learning content based on them 2. The system of claim 1.

6. The learning support unit Considering the student's home environment and daily routine, we propose optimal study times and break times.

2. The system of claim 1.

7. The mental support department Provide content for relaxation and stress relief according to the student's emotions 2. The system of claim 1.

8. The platform comprises: Analyzing the student's activity data and providing a dashboard to evaluate the student's learning progress and the quality of the communication 2. The system of claim 1.

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