System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting initial information from a user; generative artificial intelligence means for generating a study plan based on the initial information; means for presenting the study plan generated by the generative artificial intelligence means to the user; means for collecting study progress of the user; means for optimizing the study plan based on the study progress; means for presenting the optimized study plan to the user; means for supporting participation in an online community based on user interests; and means for consulting with a counselor or an artificial intelligence teacher.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] In modern society, the number of children not attending school is increasing, resulting in them losing opportunities to learn, becoming isolated from society, and experiencing a decline in self-esteem. It is difficult for families and educational institutions to provide an appropriate environment for children who are not attending school, placing a heavy burden on parents. Under these circumstances, support is needed to help these children regain their motivation to learn and reconnect with society. Another issue is the lack of technology that can monitor individual learning progress in real time and provide optimal learning plans accordingly. Furthermore, there is a need to build online communities where children can participate with peace of mind and provide technology that enables individual support. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: means for collecting initial information from a user; means for generating a study plan based on the initial information using a generative artificial intelligence; means for presenting the generated study plan to the user; means for collecting the user's study progress; means for optimizing the study plan based on the study progress; means for presenting the optimized study plan to the user; means for supporting participation in an online community based on the user's interests; and means for consulting with a counselor or an artificial intelligence teacher. This allows children who are not attending school to study based on an individually optimized study plan, thereby maintaining their motivation to learn. Furthermore, participation in a safe and friendly online community allows them to connect with society. Furthermore, the consultation function with a counselor or teacher allows children to receive psychological support and increase their self-esteem.

[0006] "Initial information" refers to personal information related to learning, such as the user's grade, favorite subjects, favorite subjects, and interests.

[0007] "Generative artificial intelligence means" refers to artificial intelligence (AI) technology that automatically generates an optimal learning plan based on the initial information entered by the user.

[0008] A "study plan" refers to specific study content and schedules designed according to the user's study goals and progress.

[0009] "Study progress" refers to data such as the progress, level of understanding, and study time achieved by the user through their daily learning activities.

[0010] An "online community" is a virtual group, forum, or social network that users can join over the Internet.

[0011] A "counselor" is a professional who can provide advice on problems users may be facing in their studies or daily life, and who plays a role in providing psychological support.

[0012] An "artificial intelligence teacher" is a virtual teacher that uses artificial intelligence technology to assist users in their learning, answering questions and helping them understand.

[0013] "Optimization" refers to readjusting a study plan to make it more effective based on data such as the user's learning progress.

[0014] "Support means" refers to methods and technologies for providing the support and services necessary for users to achieve their goals. [Brief explanation of the drawings]

[0015] [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. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention is a system that provides learning opportunities and social connections to children who are not attending school. The system collects initial information about the user, uses generative AI to provide an optimal learning plan, and provides a means to participate in online communities and counseling functions.

[0037] Program processing overview

[0038] The system of the present invention performs the following main processes.

[0039] 1. User registration and initial settings

[0040] User: The user starts the application and enters initial information such as their grade, favorite subjects, least favorite subjects, and interests.

[0041] Terminal: The terminal collects the information entered by the user and generates a request to send the data to the server.

[0042] Server: The server receives the request, stores the user information in a database, and the generative AI creates an optimal initial learning plan.

[0043] 2. Generate a learning plan

[0044] Server: The generation AI generates a learning plan based on the user's initial information and sends it to the terminal.

[0045] Terminal: The terminal displays the received learning plan to the user.

[0046] 3. Learning progress and progress records

[0047] User: The user follows the presented learning plan and enters their progress into the learning application.

[0048] Terminal: The terminal collects the progress input by the user and sends it to the server.

[0049] Server: The server receives the progress information, the generation AI optimizes the learning plan, and sends the new learning plan to the device.

[0050] 4. Participating in online communities

[0051] Users: Users can search for community groups that interest them and submit a request to join.

[0052] Terminal: The terminal sends a join request to the server.

[0053] Server: The server receives the request, recommends groups based on the user's interests, and approves their participation.

[0054] 5. Use of the consultation function

[0055] User: The user enters the consultation content and submits it.

[0056] Terminal: The terminal sends the consultation content to the server.

[0057] Server: The server receives the consultation content, assigns it to an appropriate counselor or AI teacher, and sends the reply content to the terminal and displays it to the user.

[0058] Specific examples

[0059] An example of user registration and initial settings

[0060] 1. User: A 8th grade user launches the application and enters, "I'm not good at math, but I'm interested in science."

[0061] 2. Terminal: The terminal sends this to the server.

[0062] 3. Server: Based on the user information, the server uses a generative AI to generate an initial learning plan such as "basic math problems for 30 minutes every day, and watching science experiment videos once a week" and sends it to the device.

[0063] 4. Terminal: The terminal displays the lesson plan to the user.

[0064] An example of learning progress and progress record

[0065] 1. User: The user studies for a week and enters their progress (e.g., "Mathematics comprehension 80%, Science interest 95%) into the application.

[0066] 2. Terminal: The terminal sends progress information to the server.

[0067] 3. Server: The server receives the progress information, and the generation AI optimizes the learning plan based on this and sends the plan for the next week to the device.

[0068] 4. Device: The device presents the optimized learning plan to the user.

[0069] An example of using online communities

[0070] 1. User: A user becomes interested in the "Plant Growing Group" and sends a request to join.

[0071] 2. Terminal: The terminal sends a join request to the server.

[0072] 3. Server: The server approves participation based on the user's interests and sends community information to the terminal.

[0073] 4. Terminal: The terminal displays community information to the user and supports interaction with other members.

[0074] In this way, the system provides an optimal learning environment and social connections for children who are not attending school.

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] User: When the user starts the application for the first time, they enter their own information (grade, favorite subjects, least favorite subjects, interests, etc.) on the registration screen.

[0078] Step 2:

[0079] Terminal: The terminal receives the user's input information and generates a data transmission request to the server.

[0080] Step 3:

[0081] Server: The server receives user information and stores it in a database. The generation AI creates an optimal initial learning plan for the user based on the initial information received.

[0082] Step 4:

[0083] Server: Sends the generated learning plan to the device.

[0084] Step 5:

[0085] Device: The device displays the received learning plan to the user and prompts them to start learning.

[0086] Step 6:

[0087] User: The user follows the presented learning plan and proceeds with their studies. They enter their daily learning progress (e.g., learning content, level of understanding, study time, etc.) into the learning application.

[0088] Step 7:

[0089] Terminal: The terminal collects the progress information entered by the user and generates a request to send to the server.

[0090] Step 8:

[0091] Server: The server receives the progress information and stores it in a database. The generation AI analyzes the progress information and optimizes the existing learning plan.

[0092] Step 9:

[0093] Server: Sends the optimized learning plan to the device.

[0094] Step 10:

[0095] Device: The device presents the optimized learning plan to the user.

[0096] Step 11:

[0097] Users: Users want to join an online community and search for groups that interest them.

[0098] Step 12:

[0099] User: A user sends a request to join a group they wish to join.

[0100] Step 13:

[0101] Terminal: The terminal sends a join request to the server.

[0102] Step 14:

[0103] Server: The server receives the join request, recommends appropriate community groups based on the user's interests, and approves the join.

[0104] Step 15:

[0105] Server: Sends community information to the terminal.

[0106] Step 16:

[0107] Terminal: The terminal displays community information to the user and facilitates interaction with other members.

[0108] Step 17:

[0109] User: The user enters into the application what they would like to discuss with the counselor or AI teacher.

[0110] Step 18:

[0111] Terminal: The terminal generates a request to send the consultation content to the server.

[0112] Step 19:

[0113] Server: The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[0114] Step 20:

[0115] Server: Sends the reply from the counselor or AI teacher to the device.

[0116] Step 21:

[0117] Terminal: The terminal displays the reply from the counselor or AI teacher to the user.

[0118] In this way, the system provides learning plan generation and optimization, online community participation, and consultation functions according to user needs.

[0119] Example 1

[0120] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0121] In the modern education system, it is difficult for children who do not attend school to obtain an appropriate learning environment and social connections. In particular, it is difficult for children who do not attend school to create individual learning plans that correspond to their own learning progress, and there is insufficient support to maintain motivation for learning. Furthermore, there are challenges in providing children with opportunities to participate in communities to avoid social isolation and an environment where they can seek advice.

[0122] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0123] In this invention, the server includes means for collecting initial information from a user, artificial intelligence generating means for generating a study plan based on the initial information, means for presenting the study plan generated by the artificial intelligence generating means to the user, means for collecting the user's study progress, means for optimizing the study plan based on the study progress, means for presenting the optimized study plan to the user, means for supporting participation in an online community based on the user's interests, means for consulting with a counselor or an artificial intelligence teacher, means for transmitting the generated study plan to a terminal, and means for transmitting online community information to the terminal. This not only enables children who are not attending school to receive individually optimized study plans and have them adjusted according to their progress, but also provides an environment in which they can connect with society through the online community and receive appropriate consultation.

[0124] "User" refers to an individual who uses the system.

[0125] "Initial information" refers to basic information such as grade, favorite subjects, weak subjects, and interests that users enter when they start using the system.

[0126] "Generative artificial intelligence means" refers to an artificial intelligence that generates an optimal learning plan based on the user's initial information.

[0127] The term "study plan" refers to specific study content and schedules generated by the artificial intelligence generating means to enable the user to study effectively.

[0128] "Progress" refers to information that indicates the degree of progress in learning, such as the level of understanding or interest at that point in time, which is input by the user as they proceed with their learning.

[0129] "Optimization" refers to updating the study plan to the most effective content for the user based on the user's progress.

[0130] "Online Community" means a virtual community where Users can interact with other Users who share their interests.

[0131] "Counselor or AI teacher" refers to a human counselor or AI that responds to a user's consultation and provides appropriate advice and support.

[0132] "Terminal" refers to electronic devices such as smartphones, tablets, and personal computers that users use to operate the system.

[0133] "Server" refers to the central management system that manages users' initial information and progress information, and generates and optimizes learning plans using generative AI models.

[0134] MODE FOR CARRYING OUT THE INVENTION

[0135] System Overview

[0136] This invention is a system that provides learning opportunities and social connections to children who are not attending school. The system collects initial information about the user, uses a generative AI model to provide an optimal learning plan, and provides a means to participate in online communities and counseling functions. The entire system consists of three main components: the user, the device, and the server.

[0137] Hardware and Software

[0138] Terminal: Refers to an electronic device such as a smartphone, tablet, or PC that a user uses to access the system.

[0139] Server: A central management system that manages initial information and progress information from users and generates and optimizes learning plans using generative AI models. It uses a cloud-based data processing server (e.g., AWS, Google Cloud Platform).

[0140] Generative AI model: An AI model that generates and optimizes a learning plan based on the user's initial information and progress information. For example, OpenAI GPT-4 is used for this purpose.

[0141] Gathering initial information

[0142] User: Starts the application and enters initial information such as grade, favorite subjects, favorite subjects, and interests.

[0143] Terminal: Collects information entered by the user and sends it to the server in the form of an HTTP request.

[0144] Server: Stores the received user information in a database. Generates prompts from the generative AI model and asks it to generate an optimal learning plan. Sends the generated learning plan to the device.

[0145] View your learning plan

[0146] Terminal: Displays the learning plan received from the server to the user.

[0147] As a concrete example, a second-year junior high school student can input, "I'm not good at math, but I'm interested in science," and the generative AI model will generate a study plan such as, "Basic math problems for 30 minutes every day, watching science experiment videos once a week."

[0148] Learning progress and progress records

[0149] User: Study based on the presented study plan. After completing the study, enter the progress (for example, level of understanding and interest) into the application.

[0150] Terminal: Collects the progress information entered by the user and sends it to the server in the form of an HTTP request.

[0151] Server: Stores the received progress information in a database, sends prompts to the generative AI model to optimize the new learning plan, receives the new learning plan, and sends it to the device.

[0152] Terminal: Displays the newly received lesson plan to the user.

[0153] As a concrete example, based on the progress of "80% understanding of mathematics, 95% interest in science," the generative AI generates a new optimized learning plan.

[0154] Participating in online communities

[0155] Users: Search for online community groups that interest them within the application and submit a request to join.

[0156] Terminal: Sends a request to the server to join the community group selected by the user.

[0157] Server: Based on the received request, recommends groups based on the user's interests and approves their participation.

[0158] Terminal: Displays community information to users and helps them interact with other members.

[0159] As a specific example, if a user becomes interested in a "plant growing group" and sends a request to join, the server will approve the request and the terminal will display community information.

[0160] Use of consultation function

[0161] User: Enter the consultation details within the application and submit.

[0162] Terminal: Collects consultation details and sends them to the server.

[0163] Server: Assigns the received consultation content to the appropriate counselor or AI teacher, collects the reply content, and sends it to the device.

[0164] Terminal: Display the reply to the user.

[0165] As a specific example, a user inputs "advice about career paths," the server assigns it to a counselor, and the reply is sent to the terminal and displayed to the user.

[0166] Prompt Sentence Examples

[0167] "Please create the optimal study plan based on the initial information of the new user. The student is in the second year of junior high school, is not good at math, and is interested in science."

[0168] "Please optimize your study plan based on the progress information below. Your math comprehension is 80% and your science interest is 95%."

[0169] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0170] Step 1: User launches application and enters initial information

[0171] Input: Grade, strong subjects, weak subjects, interests

[0172] Output: User's initial information

[0173] Specific operation: The user enters initial information such as grade level, favorite subjects, weak subjects, and interests into the application's input form.

[0174] Step 2: The device sends initial information to the server

[0175] Input: Initial information entered by the user

[0176] Output: Data in HTTP request format

[0177] Specific operation: The terminal collects the user's initial information and sends it to the server as an HTTP request.

[0178] Step 3: The server saves the initial information and sends a prompt to the generative AI model.

[0179] Input: Initial information in the HTTP request format

[0180] Output: Saved user information, prompts to send to the generative AI model

[0181] Specific operation: The server stores the received user information in a database, then generates and sends prompts to the generative AI model to generate an optimal learning plan.

[0182] Step 4: The generative AI model generates a learning plan and sends it to the server

[0183] Input: prompt statement

[0184] Output: The generated learning plan

[0185] Specific operation: The generative AI model generates a learning plan based on the prompt sentence and sends the generated results back to the server.

[0186] Step 5: The server sends the generated lesson plan to the device.

[0187] Input: Generated lesson plan

[0188] Output: Learning plan in HTTP response format

[0189] Specific operation: The server sends the learning plan received from the generative AI model to the terminal as an HTTP response.

[0190] Step 6: The device displays the lesson plan to the user

[0191] Input: Lesson plan in HTTP response format

[0192] Output: A displayed lesson plan

[0193] Specific operation: The device displays the received study plan in an easy-to-understand manner to the user. Specifically, it displays a plan such as "30 minutes of basic math problems every day and watching science experiment videos once a week."

[0194] Step 7: User progresses and enters progress information

[0195] Input: Progress information (level of understanding, interest, etc.)

[0196] Output: User progress information

[0197] Specific operation: The user studies according to the presented study plan and enters their study progress in the application's input form.

[0198] Step 8: The device sends progress information to the server

[0199] Input: Progress information entered by the user

[0200] Output: Progress information in the form of an HTTP request

[0201] Specific operation: The device collects the user's progress information and sends it to the server as an HTTP request.

[0202] Step 9: The server saves the progress and sends a new prompt to the generative AI model.

[0203] Input: Progress information in HTTP request format

[0204] Output: Saved progress information, new prompt text

[0205] Specific operation: The server stores the received progress information in the database, then generates and sends a prompt to the generative AI model to generate a new learning plan.

[0206] Step 10: The generative AI model generates a new learning plan and sends it to the server.

[0207] Input: New prompt text

[0208] Output: The new learning plan generated.

[0209] Specific operation: The generative AI model generates a new learning plan based on progress information and sends the generated results back to the server.

[0210] Step 11: The server sends the new lesson plan to the device.

[0211] Input: New lesson plan

[0212] Output: The new learning plan in the form of an HTTP response.

[0213] Specific operation: The server sends the new learning plan received from the generative AI model to the terminal as an HTTP response.

[0214] Step 12: The device displays the new lesson plan to the user.

[0215] Input: A new learning plan in the form of an HTTP response

[0216] Output: The new lesson plan displayed.

[0217] Specific operation: The device displays the new learning plan to the user in an easy-to-understand manner.

[0218] Step 13: User finds online community and submits request to join

[0219] Input: Community name of interest

[0220] Output: Join request

[0221] What happens: A user searches for an online community group of interest within the application, selects one, and submits a request to join.

[0222] Step 14: The device sends a join request to the server

[0223] Input: Join request

[0224] Output: Join request in HTTP request format

[0225] Specific operation: The terminal collects the user's participation request and sends it to the server as an HTTP request.

[0226] Step 15: The server processes the join request and sends the online community information

[0227] Input: Join request

[0228] Output: Online community information

[0229] Specific operation: The server receives the participation request, recommends communities based on the user's interests, and sends information approving participation to the device.

[0230] Step 16: The terminal displays online community information to the user

[0231] Input: Online community information

[0232] Output: Displayed online community information

[0233] Specific operation: The device displays online community information to the user and supports interaction with other members.

[0234] (Application example 1)

[0235] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0236] Conventional learning support systems have struggled to provide optimal learning plans and connections to society for children who are not attending school. Furthermore, they lacked the ability to manage learning progress and dynamic responses to individual users' interests and needs. Furthermore, they lacked systems that integrated online communities and consultation functions, which led to students becoming isolated.

[0237] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0238] In this invention, the server includes means for collecting initial information from a user, artificial intelligence generating means for generating a study plan based on the initial information, means for presenting the study plan generated by the artificial intelligence generating means to the user, means for collecting the user's study progress, means for optimizing the study plan based on the study progress, means for presenting the optimized study plan to the user, means for supporting participation in an online community based on the user's interests, means for consulting with a counselor or an artificial intelligence teacher, and means for being installed as a smartphone application in a virtual store. This makes it possible to provide children who are not attending school with an individualized, optimal study plan, an online community, and consultation functions in an integrated manner.

[0239] "User information" refers to initial setting information about an individual user, such as grade, favorite subjects, weak subjects, and interests.

[0240] A "study plan" is a study schedule or curriculum created by the artificial intelligence based on user information.

[0241] "Generative AI" is an AI model that creates optimal learning plans based on user information.

[0242] "Progress" refers to information that records the user's learning progress and achievement level.

[0243] "Optimization" refers to the process of modifying and adjusting the learning plan based on the user's progress and needs.

[0244] An "online community" is an online group or forum that users can join based on their interests.

[0245] A "counselor" is a professional consultant who provides advice to users and offers solutions.

[0246] An "artificial intelligence teacher" is an AI-based support system provided by generative artificial intelligence to respond to user inquiries.

[0247] A "virtual store" is a virtual store that provides the same services online as a physical store.

[0248] A "smartphone application" is a software application that runs on a smartphone and allows users to use the system.

[0249] This invention is a system that provides optimal learning opportunities and social connections to children who are not attending school. This system is installed as a smartphone application in a virtual store, and is configured to allow users to access personalized learning plans, online communities, and even counseling services.

[0250] 1. System Configuration

[0251] Hardware

[0252] Smartphone: A device that allows users to access the system.

[0253] Server: Processes data, runs generative AI models, and manages databases.

[0254] software

[0255] Smartphone application: An app for collecting user information, displaying learning plans, recording progress, participating in online communities, and sending and receiving consultations.

[0256] Generative AI model: An AI model for creating learning plans based on user information.

[0257] 2. Operational flow

[0258] User registration and initial settings

[0259] The user launches the smartphone application and enters initial information such as their grade level, favorite subjects, weak subjects, and interests. The smartphone then sends this information to the server. The server stores the received information in a database and uses a generative AI model to create an optimal initial learning plan. The created learning plan is sent to the user via the smartphone application and displayed.

[0260] Learning progress and progress records

[0261] The user studies according to the generated study plan and inputs their progress into the smartphone application. The smartphone then sends the input progress to the server. The server then uses the generative AI model to optimize the study plan based on the received progress information and creates a new study plan. This plan is also sent to the user via the smartphone application and displayed.

[0262] Participating in online communities

[0263] Users search for community groups that interest them and send a request to join from their smartphone application. The server receives the request, recommends the most suitable group based on the user's interests, and approves their participation. The approval result and community information are notified to the smartphone, allowing users to interact with other members.

[0264] Use of consultation function

[0265] The user inputs the consultation content and sends it to the server via the smartphone application. The server receives the consultation content and assigns it to an appropriate counselor or AI model. The counselor or AI model then creates a reply and sends it to the smartphone application via the server, where it is displayed to the user.

[0266] 3. Specific Examples

[0267] Prompt Sentence Examples

[0268] For example, a second-year junior high school student launches the application and enters, "I'm not good at math, but I'm interested in science." An example of a study plan generated based on this user information might be, "Basic math problems, 30 minutes a day, watching science experiment videos once a week."

[0269] User initial information:

[0270] Grade: 2nd year of junior high school

[0271] Favorite subject: Science

[0272] Weak subject: Mathematics

[0273] Interests: Science experiments

[0274] Generate an optimal weekly study plan for this user.

[0275] In this way, this system provides children who are not attending school with an optimal, individualized learning plan, an online community, and consultation functions in an integrated manner, realizing learning opportunities and connections to society.

[0276] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0277] Step 1:

[0278] The user launches the smartphone application and enters their initial information (grade, favorite subjects, weak subjects, interests, etc.). The device collects this information and sends it to the server. The entered data includes grade, favorite / weak subjects, and interests, and a process is performed to transfer this data to the server. Specifically, when the submit button on the input form is pressed, an API request is sent.

[0279] Step 2:

[0280] The server stores the received initial information in a database. The stored data includes the user ID and corresponding grade, favorite subjects, weak subjects, and interests. The server inputs the stored data into the generative AI model and requests it to generate an optimal learning plan. Specific operations include a database insert query and prompt generation for the AI ​​model.

[0281] Step 3:

[0282] The generative AI model generates a study plan based on the input user information. An example of the generated study plan might be "basic math problems for 30 minutes every day, watching science experiment videos once a week." This plan is sent to the server, which then transmits it to the smartphone application. Specific operations include the AI ​​model's calculation process and the generation of API responses.

[0283] Step 4:

[0284] The device displays the learning plan received from the server to the user. The displayed learning plan includes specific assignments and a learning schedule, and the user follows this to progress with their studies. Specifically, the device performs a process of rendering the received data on the screen.

[0285] Step 5:

[0286] Users record their learning progress and enter it into a smartphone application. The device collects this progress data and sends it to the server. The progress data entered includes the level of understanding and achievement, and this is then transferred to the server. Specifically, an API request is sent when the send button on the progress input form is pressed.

[0287] Step 6:

[0288] The server stores the received progress information in a database and inputs it into the generative AI model. The generative AI model optimizes the learning plan based on the latest progress information. The optimized plan is sent to the server and then distributed to the smartphone application. Specific operations include issuing a database update query and generating a prompt for the AI ​​model.

[0289] Step 7:

[0290] A user searches for online communities that interest them and submits a request to join. The device then sends this request to the server, which then recommends the most suitable group based on the user's interests and approves their participation. Specific operations include sending a request and receiving an API response approving their participation.

[0291] Step 8:

[0292] The user enters the consultation details and sends them from the smartphone application to the server. The server receives the consultation details and assigns them to an appropriate counselor or AI model. The reply is sent to the smartphone application via the server and displayed to the user. Specific operations include sending the input form, generating an API response, and displaying the reply.

[0293] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0294] This invention is a system that provides learning opportunities and social connections to children who are not attending school. It further incorporates an emotion engine that recognizes the user's emotions to personalize the learning experience and provide emotional support. The system collects initial information about the user, uses generative AI to provide an optimal learning plan, and provides a means to participate in online communities and counseling functions. It also has the ability to recognize the user's emotions and adjust the learning plan and community participation support.

[0295] Program processing overview

[0296] The system of the present invention performs the following main processes.

[0297] 1. User registration and initial settings

[0298] User: The user starts the application and enters initial information such as their grade, favorite subjects, least favorite subjects, and interests.

[0299] Terminal: The terminal collects the user's input information and generates a data transmission request to the server.

[0300] Server: The server receives user information and stores it in a database. The generation AI creates an optimal initial learning plan for the user based on the initial information received.

[0301] 2. Generate a learning plan

[0302] Server: The generation AI generates a learning plan based on the user's initial information and sends it to the terminal.

[0303] Terminal: The terminal displays the received learning plan to the user.

[0304] 3. Learning progress and progress records

[0305] User: The user follows the presented learning plan and enters their progress into the learning application.

[0306] Terminal: The terminal collects the progress information entered by the user and generates a request to send to the server.

[0307] Server: The server receives the progress information and stores it in a database. The generation AI analyzes the progress information and optimizes the existing learning plan.

[0308] Server: Sends the optimized learning plan to the device.

[0309] Device: The device presents the optimized learning plan to the user.

[0310] 4. Participating in online communities

[0311] Users: Users can search for community groups that interest them and submit a request to join.

[0312] Terminal: The terminal sends a join request to the server.

[0313] Server: The server receives the join request, recommends appropriate community groups based on the user's interests, and approves the join.

[0314] Server: Sends community information to the terminal.

[0315] Terminal: The terminal displays community information to the user and facilitates interaction with other members.

[0316] 5. Use of the consultation function

[0317] User: The user enters the consultation content and submits it.

[0318] Terminal: The terminal generates a request to send the consultation content to the server.

[0319] Server: The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[0320] Server: Sends the reply from the counselor or AI teacher to the device.

[0321] Terminal: The terminal displays the reply from the counselor or AI teacher to the user.

[0322] 6. Use of Emotion Engines

[0323] Terminal: The terminal activates sensors (e.g., facial recognition camera, voice analysis, etc.) to recognize the user's emotions.

[0324] User: The user displays emotions such as facial expressions and voice on the input device while learning or consulting.

[0325] Terminal: The terminal collects emotion data and generates a request to send it to the server.

[0326] Server: The server receives the emotion data and the emotion engine analyzes it.

[0327] Emotional response: The emotion engine optimizes feedback on the user's study plan and consultation content based on the results of emotion analysis.

[0328] Terminal: Presents the user with a learning plan and consultation content optimized by the emotion engine.

[0329] Specific examples

[0330] An example of user registration and initial settings

[0331] 1. User: A 8th grade user launches the application and enters, "I'm not good at math, but I'm interested in science."

[0332] 2. Terminal: The terminal sends this to the server.

[0333] 3. Server: Based on the user information, the server uses a generative AI to generate an initial learning plan such as "basic math problems for 30 minutes every day, and watching science experiment videos once a week" and sends it to the device.

[0334] 4. Terminal: The terminal displays the lesson plan to the user.

[0335] An example of learning progress and progress record

[0336] 1. User: The user studies for a week and enters their progress (e.g., "Mathematics comprehension 80%, Science interest 95%) into the application.

[0337] 2. Terminal: The terminal sends progress information to the server.

[0338] 3. Server: The server receives the progress information, and the generation AI optimizes the learning plan based on this information and sends the new learning plan to the device.

[0339] 4. Device: The device presents the optimized learning plan to the user.

[0340] An example of using online communities

[0341] 1. User: A user becomes interested in the "Plant Growing Group" and sends a request to join.

[0342] 2. Terminal: The terminal sends a join request to the server.

[0343] 3. Server: The server approves participation based on the user's interests and sends community information to the terminal.

[0344] 4. Terminal: The terminal displays community information to the user and supports interaction with other members.

[0345] An example of using the emotion engine

[0346] 1. Device: The device captures the user's facial expressions with a camera and recognizes their emotions.

[0347] 2. Server: The server analyzes the emotional data and determines that the user is feeling stressed.

[0348] 3. Emotional response: The emotion engine optimizes learning content to temporarily ease user stress and enhances consultation functions.

[0349] 4. Terminal: Presents the user with a learning plan and consultation options tailored by the emotion engine.

[0350] In this way, the system provides individually optimized learning environments and psychological support to children who are not attending school.

[0351] The processing flow will be explained below.

[0352] Step 1:

[0353] User: The user launches the application and enters their own information (grade, favorite subjects, weak subjects, interests, etc.) on the registration screen.

[0354] Step 2:

[0355] Terminal: The terminal receives the user's input information and generates a data transmission request to the server.

[0356] Step 3:

[0357] Server: The server receives user information and stores it in a database. The generation AI creates an optimal initial learning plan for the user based on the initial information received.

[0358] Step 4:

[0359] Server: Sends the generated learning plan to the device.

[0360] Step 5:

[0361] Device: The device displays the received learning plan to the user and prompts them to start learning.

[0362] Step 6:

[0363] User: The user follows the presented learning plan and proceeds with their studies. They enter their daily learning progress (e.g., learning content, level of understanding, study time, etc.) into the learning application.

[0364] Step 7:

[0365] Terminal: The terminal collects the progress information entered by the user and generates a request to send to the server.

[0366] Step 8:

[0367] Server: The server receives the progress information and stores it in a database. The generation AI analyzes the progress information and optimizes the existing learning plan.

[0368] Step 9:

[0369] Server: Sends the optimized learning plan to the device.

[0370] Step 10:

[0371] Device: The device presents the optimized learning plan to the user.

[0372] Step 11:

[0373] Users: Users want to join an online community and search for groups that interest them.

[0374] Step 12:

[0375] User: A user sends a request to join a group they wish to join.

[0376] Step 13:

[0377] Terminal: The terminal sends a join request to the server.

[0378] Step 14:

[0379] Server: The server receives the join request, recommends appropriate community groups based on the user's interests, and approves the join.

[0380] Step 15:

[0381] Server: Sends community information to the terminal.

[0382] Step 16:

[0383] Terminal: The terminal displays community information to the user and facilitates interaction with other members.

[0384] Step 17:

[0385] User: The user enters into the application what they would like to discuss with the counselor or AI teacher.

[0386] Step 18:

[0387] Terminal: The terminal generates a request to send the consultation content to the server.

[0388] Step 19:

[0389] Server: The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[0390] Step 20:

[0391] Server: Sends the reply from the counselor or AI teacher to the device.

[0392] Step 21:

[0393] Terminal: The terminal displays the reply from the counselor or AI teacher to the user.

[0394] Step 22:

[0395] Terminal: The terminal activates sensors (e.g., facial recognition camera, voice analysis, etc.) to recognize the user's emotions.

[0396] Step 23:

[0397] User: The user displays emotions such as facial expressions and voice on the input device while learning or consulting.

[0398] Step 24:

[0399] Terminal: The terminal collects emotion data and generates a request to send it to the server.

[0400] Step 25:

[0401] Server: The server receives the emotion data and the emotion engine analyzes it.

[0402] Step 26:

[0403] Emotional response: The emotion engine optimizes feedback on the user's study plan and consultation content based on the results of emotion analysis.

[0404] Step 27:

[0405] Terminal: Presents the user with a learning plan and consultation content optimized by the emotion engine.

[0406] Through the above process, this system can provide individually optimized learning environments and psychological support to children who are not attending school.

[0407] Example 2

[0408] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0409] In modern society, the problem of children not attending school is extremely serious, with a lack of learning opportunities and social connections being major issues. Furthermore, these children require emotional support and require individualized learning plans and psychological assistance. However, existing education systems do not adequately create learning plans tailored to the needs of individual children or recognize and respond to their emotions. As a result, children may lose motivation to learn and become even more isolated.

[0410] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting initial information from a user; artificial intelligence generating means for generating a study plan based on the initial information; means for presenting the study plan generated by the artificial intelligence generating means to the user; means for collecting the user's learning progress; means for optimizing the study plan based on the learning progress; means for presenting the optimized study plan to the user; means for supporting participation in an online community based on the user's interests; means for consulting with a counselor or an artificial intelligence teacher; means including a sensor for recognizing the user's emotions; emotional response means for analyzing the emotional data and optimizing the study plan and consultation content; and means for presenting the optimized study plan and consultation content to the user based on the emotional response. This makes it possible to provide an individualized learning environment and psychological support to children who are not attending school, thereby improving their motivation to study and promoting their connections with society.

[0411] "User" refers to the learner who uses the system, specifically the entity who inputs initial information, follows a learning plan, and reports progress.

[0412] "Initial information" refers to basic data such as grade, favorite subjects, weak subjects, and interests that users enter when using the system.

[0413] "Study plan" refers to a schedule of learning content that is most suitable for a user, created by the generation AI based on the user's initial information.

[0414] "Generative AI means" refers to the AI ​​technology deployed to generate an optimal learning plan based on the user's initial information.

[0415] "Study progress status" refers to progress information entered by a user as a result of studying according to a study plan.

[0416] The "optimizing means" refers to a means for adjusting the study plan based on the user's study progress and generating a new optimized study plan.

[0417] An "online community" refers to a virtual group of users who share common interests and who can interact with each other and share information.

[0418] "Counselor or AI teacher" refers to a real-life expert or AI (artificial intelligence) technology that provides appropriate advice and support in response to the consultation content entered by the user.

[0419] A "sensor" is a device used to recognize a user's emotions, and includes facial recognition cameras and voice analysis devices.

[0420] "Emotion data" refers to information about emotions obtained from the user's facial expressions, voice, etc. collected by sensors.

[0421] "Emotional response" refers to the process of analyzing emotional data and optimizing the user's study plan or consultation content based on the results.

[0422] MODE FOR CARRYING OUT THE INVENTION

[0423] The present invention is a system that provides learning opportunities and social connections to children who are not attending school, and by further incorporating an emotion engine that recognizes the user's emotions, it personalizes the learning experience and provides emotional support.

[0424] This system performs various data processing using the following hardware and software:

[0425] Hardware: smartphones, tablets, laptops, facial recognition cameras, voice analysis devices

[0426] Software: Applications that collect user information, generative AI (e.g., GPT-4), database management systems, online community platforms, sentiment analysis engines

[0427] The specific processing flow is shown below.

[0428] User registration and initial settings

[0429] The user starts the learning application and enters initial information such as their grade, favorite subjects, weak subjects, and interests.

[0430] Example: A second-year junior high school student enters, "I'm not good at math, but I'm interested in science."

[0431] The terminal collects the input information and generates a data transmission request to the server.

[0432] The server receives the user's initial information and stores it in a database.

[0433] Example: The server receives user information and records it in a database as "user ID, grade, favorite subjects, weak subjects, and interests."

[0434] The server uses generative AI to create an optimal learning plan based on the user's initial information.

[0435] Example: Generative AI generates a study plan such as "Basic math problems for 30 minutes every day, and watch science experiment videos once a week."

[0436] The server sends the generated learning plan to the terminal.

[0437] The terminal displays the received learning plan to the user.

[0438] An example of learning progress and progress record

[0439] The user studies according to the presented study plan and enters their progress into the application.

[0440] Example: A user enters into an application, "I understand math 80% and I'm interested in science 95%."

[0441] The terminal collects the progress information and generates a transmission request to the server.

[0442] The server receives the progress information and stores it in a database.

[0443] The server uses generated AI to analyze progress information and optimize learning plans.

[0444] Example: A generative AI adjusts next week's math problem set based on progress results.

[0445] The server sends the optimized learning plan to the device.

[0446] The device displays the optimized study plan to the user.

[0447] An example of participating in an online community

[0448] Users search for community groups that interest them and submit a request to join.

[0449] Example: A user searches for "plant growing group" and sends a request to join.

[0450] The device sends a join request to the server.

[0451] The server receives the request, recommends appropriate community groups, and approves participation.

[0452] The server transmits the community information to the terminal.

[0453] The terminal displays community information to the user and encourages interaction with other members.

[0454] An example of using the consultation function

[0455] The user inputs the consultation content and sends it.

[0456] Example: A user types, "Studying is hard."

[0457] The terminal generates a request to transmit the consultation content to the server.

[0458] The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[0459] The server sends the reply from the counselor or AI teacher to the device.

[0460] The device displays the reply from the counselor or AI teacher to the user.

[0461] An example of using the emotion engine

[0462] The device activates sensors (facial recognition camera, voice analysis, etc.) to recognize the user's emotions.

[0463] Example: The device captures the user's facial expressions through the camera.

[0464] The user indicates emotions (e.g., facial expressions or voice) using an input device.

[0465] The device collects emotion data and generates a transmission request to the server.

[0466] The server receives the emotion data and the emotion engine analyzes it.

[0467] Example: Determining that a user is feeling stressed based on emotional data.

[0468] Emotional response: The emotion engine optimizes learning plans and consultation content based on the analysis results.

[0469] Example: Reduce the learning load or add a refresh feature.

[0470] The device presents the user with optimized learning plans and consultation contents.

[0471] This makes it possible for the system to provide children who are not attending school with an individualized learning environment and psychological support, thereby improving their motivation to learn and promoting connections with society.

[0472] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0473] Program processing flow

[0474] User registration and initial settings

[0475] Step 1:

[0476] The user launches an application.

[0477] Specific operation: A second-year junior high school student launches the app on his smartphone and the initial startup screen is displayed.

[0478] Input: User action

[0479] Output: Application Launch

[0480] Step 2:

[0481] The user enters their initial information.

[0482] Specific operation: The user inputs "grade: second year of junior high school, favorite subject: science, least favorite subject: mathematics, interest: natural science."

[0483] Input: Grade, favorite subjects, weak subjects, interests

[0484] Output: Initial information data

[0485] Step 3:

[0486] The terminal collects the input information and generates a data transmission request to the server.

[0487] Specific operation: The terminal compiles the user's initial information and sends it to the server.

[0488] Input: Initial information data

[0489] Output: Request sent to server

[0490] Step 4:

[0491] The server receives the user's initial information and stores it in a database.

[0492] Specific operation: The server records the received user information in a database.

[0493] Input: Initial information data

[0494] Output: Save to database

[0495] Step 5:

[0496] The server uses generative AI to create an optimal learning plan based on the user's initial information.

[0497] Specific operation: The generative AI generates a study plan such as "basic math problems for 30 minutes every day, and watching science experiment videos once a week."

[0498] Input: Initial information data

[0499] Output: Learning plan

[0500] Step 6:

[0501] The server sends the generated learning plan to the terminal.

[0502] Specific operation: The server transfers the newly generated learning plan to the user's device.

[0503] Input:Study plan

[0504] Output: Data sent to the terminal

[0505] Step 7:

[0506] The terminal displays the received learning plan to the user.

[0507] Specific behavior: The device displays the lesson plan within the app and asks the user to confirm it.

[0508] Input: Learning plan data

[0509] Output: What is displayed to the user

[0510] Learning progress and progress records

[0511] Step 1:

[0512] The user proceeds with their studies according to the presented study plan.

[0513] Specific action: The user studies math for 30 minutes every day.

[0514] Input:Study plan

[0515] Output: Learning progress

[0516] Step 2:

[0517] The user enters their learning progress into the application.

[0518] Specific operation: The user reports to the app that "math comprehension level is 80% and science interest level is 95%."

[0519] Input: Progress information

[0520] Output: Progress data

[0521] Step 3:

[0522] The terminal collects the progress information and generates a transmission request to the server.

[0523] Specific operation: The device compiles progress data and sends it to the server.

[0524] Input: Progress data

[0525] Output: Request sent to server

[0526] Step 4:

[0527] The server receives the progress information and stores it in a database.

[0528] What happens: The server adds the new progress data to the database.

[0529] Input: Progress data

[0530] Output: Save to database

[0531] Step 5:

[0532] The server uses generated AI to analyze progress information and optimize learning plans.

[0533] Specific operation: The generative AI generates a new problem set based on the user's level of mathematical understanding.

[0534] Input: Progress data

[0535] Output: Optimized learning plan

[0536] Step 6:

[0537] The server sends the optimized learning plan to the device.

[0538] Specific operation: The optimized plan is sent from the server to the user's terminal.

[0539] Input: Optimized Study Plan

[0540] Output: Data sent to the terminal

[0541] Step 7:

[0542] The device presents the optimized study plan to the user.

[0543] Specific behavior: The device displays the new lesson plan to the user.

[0544] Input: Optimized lesson plan data

[0545] Output: What is displayed to the user

[0546] Participating in online communities

[0547] Step 1:

[0548] Users search for community groups that interest them and submit a request to join.

[0549] Specific behavior: A user searches for "plant growing group" and sends a request to join.

[0550] Input: Interest words

[0551] Output: Join request data

[0552] Step 2:

[0553] The device sends a join request to the server.

[0554] Specific operation: The terminal sends a request to the server.

[0555] Input: Join request data

[0556] Output: Data sent to the server

[0557] Step 3:

[0558] The server receives the request, recommends appropriate community groups, and approves participation.

[0559] Specific Operation: The server approves the user to join a plant growing group based on their interests.

[0560] Input: Join request data

[0561] Output: Approval data and recommendation data

[0562] Step 4:

[0563] The server transmits the community information to the terminal.

[0564] Specific operation: Community information is sent from the server to the terminal.

[0565] Input: Approval data and recommendation data

[0566] Output: Data sent to the terminal

[0567] Step 5:

[0568] The terminal displays community information to the user and encourages interaction with other members.

[0569] Specific operation: The device displays the latest community information, message boards, etc. to the user.

[0570] Input: Community information data

[0571] Output: What is displayed to the user

[0572] Use of consultation function

[0573] Step 1:

[0574] The user inputs the consultation content and sends it.

[0575] Specific action: The user enters "Studying is hard" into the app.

[0576] Input: Consultation details

[0577] Output: Consultation data

[0578] Step 2:

[0579] The terminal generates a request to transmit the consultation content to the server.

[0580] Specific operation: The device prepares to send the consultation content to the server.

[0581] Input: Consultation data

[0582] Output: Request sent to server

[0583] Step 3:

[0584] The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[0585] Specific operation: The server transfers the consultation content to a psychological counselor.

[0586] Input: Consultation data

[0587] Output: Counselor assignment data

[0588] Step 4:

[0589] The server sends the reply from the counselor or AI teacher to the device.

[0590] Specific operation: The reply from the counselor is sent to the terminal via the server.

[0591] Input: Reply content data

[0592] Output: Data sent to the terminal

[0593] Step 5:

[0594] The device displays the reply from the counselor or AI teacher to the user.

[0595] Specific operation: The device displays advice from the counselor.

[0596] Input: Reply content data

[0597] Output: What is displayed to the user

[0598] Use of emotion engine

[0599] Step 1:

[0600] The device activates sensors (facial recognition camera, voice analysis, etc.) to recognize the user's emotions.

[0601] Specific operation: The device captures the user's facial expression through the built-in camera.

[0602] Input: Sensor activation request

[0603] Output: Sensor activation

[0604] Step 2:

[0605] The user indicates emotions such as facial expressions and voice on the input device.

[0606] Specific action: The user smiles or frowns in front of the camera.

[0607] Input: Real-time facial and voice data

[0608] Output: Emotion data

[0609] Step 3:

[0610] The device collects emotion data and generates a transmission request to the server.

[0611] Specific operation: The device prepares to send the collected emotion data to the server.

[0612] Input: Emotion data

[0613] Output: Request sent to server

[0614] Step 4:

[0615] The server receives the emotion data and the emotion engine analyzes it.

[0616] Specific operation: The server analyzes the user's stress level from facial expression data.

[0617] Input: Emotion data

[0618] Output: Analysis results

[0619] Step 5:

[0620] Emotional response: The emotion engine optimizes learning plans and consultation content based on the analysis results.

[0621] Specific behavior: The emotion engine reduces the learning load and provides a refresh function.

[0622] Input: Analysis results

[0623] Output: Optimized study plan and consultation content

[0624] Step 6:

[0625] The device presents the user with optimized learning plans and consultation contents.

[0626] Specific operation: The device displays the new learning plan and consultation details to the user.

[0627] Input: Optimized learning plan and consultation data

[0628] Output: What is displayed to the user

[0629] (Application example 2)

[0630] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0631] Existing online learning systems for school-refusing children often lack individualization and emotional support. They also lack the ability to provide effective social connections, such as learning plans based on users' interests and online communities. Furthermore, they lack the ability to optimize learning plans and counseling functions that incorporate users' emotional data, resulting in insufficient psychological support.

[0632] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting initial information from the user; artificial intelligence generating means for generating a study plan based on the initial information; means for presenting the study plan generated by the artificial intelligence generating means to the user; means for collecting the user's study progress; means for optimizing the study plan based on the study progress; means for presenting the optimized study plan to the user; means for supporting participation in an online community based on the user's interests; means for consulting with a counselor or an artificial intelligence teacher; means for collecting user emotion data using an emotion recognition sensor; and emotion engine means for analyzing the emotion data and optimizing the study plan or consultation content. This makes it possible to provide an individualized study plan, provide emotional support, and strengthen social connections through the online community.

[0633] "Initial information" is basic information for generating a study plan, such as the user's grade, favorite subjects, weak subjects, and interests.

[0634] The "generative artificial intelligence means" is an artificial intelligence technology for generating an optimal learning plan based on initial information provided by the user.

[0635] A "study plan" is a plan that specifically shows how a user will actually proceed with their studies, and its contents are created by a generative artificial intelligence means.

[0636] An "emotion recognition sensor" is a sensor device that recognizes a user's facial expressions and voice and detects their emotional state.

[0637] The "emotion engine means" is a technology for analyzing the user's emotional data acquired from the emotion recognition sensor and optimizing the study plan and counseling based on the data.

[0638] An "online community" is a virtual group that users can join based on common interests.

[0639] "Counselor or AI teacher" refers to a counseling function using a real counselor or AI that provides support based on the user's consultation content.

[0640] Generating a Program

[0641] The system for implementing this invention is composed of a program that performs the following main processes: A system for providing individualized learning plans and emotional support to children who are not attending school.

[0642] Program processing overview

[0643] The system consists of several steps: user registration and initial setup, generating and optimizing a learning plan, recording learning progress and progress, participating in an online community, counseling functions, and using an emotion engine.

[0644] Hardware and Software Used

[0645] Smartphone: A device on which users install and use applications.

[0646] Flask: A server-side web framework.

[0647] Generative AI models: For example, OpenAI's GPT-3.

[0648] Emotion engine: Libraries and services used for user facial expression recognition and voice analysis (e.g., Google Cloud Vision).

[0649] Program processing explanation

[0650] 1. Collecting initial user information

[0651] The user enters initial information such as grade level, favorite subjects, weak subjects, and interests through a smartphone application. This information is then sent from the smartphone to the server.

[0652] 2. Generate a learning plan

[0653] The server uses a generative AI model (e.g., GPT-3) to generate an optimal learning plan based on the received initial information. The generated learning plan is sent to the smartphone and presented to the user.

[0654] 3. Track and optimize your learning progress

[0655] As the user progresses through their studies, they input their progress into the application. The server receives the progress information and optimizes the study plan using a generative AI model. The optimized study plan is then presented to the user again.

[0656] 4. Participating in online communities

[0657] Users search for community groups that interest them and send a request to join. The server receives the request, recommends appropriate groups based on the user's interests, and approves their participation. Community information is sent to the smartphone and displayed to the user.

[0658] 5. Use of counseling functions

[0659] When a user inputs their consultation details, the details are sent to the server, which then assigns the consultation details to an appropriate counselor or AI teacher and sends the answer to the user.

[0660] 6. Use of Emotion Engines

[0661] The smartphone's emotion recognition sensor recognizes the user's facial expressions and voice and sends the data to a server, which then uses an emotion engine to analyze the emotion data and optimize the learning plan and counseling content.

[0662] Specific examples

[0663] Example of user registration and initial settings

[0664] The user launches the application and enters, "I'm in the second year of junior high school, I'm not good at math, and I'm interested in science." This information is sent to the server, and the generative AI model generates an initial study plan, such as "basic math problems, 30 minutes a day, watching science experiment videos once a week." The generated study plan is then displayed on the smartphone.

[0665] Examples of learning progress recording and optimization

[0666] The user studies for a week and enters progress information into the application, such as "Mathematics comprehension 80%, Science interest 95%." The server receives the progress information, and the generative AI model uses it to optimize the study plan. The new study plan is then presented on the smartphone.

[0667] Examples of prompt statements

[0668] Examples of prompts for generative AI models include:

[0669] "Create a customized learning plan for a grade 8 student who is struggling with mathematics but has a keen interest in science. The learning plan should include daily math practice for 30 minutes and weekly science experiment videos."

[0670] In this way, the present invention can provide a personalized learning environment and psychological support for children who are not attending school.

[0671] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0672] Step 1:

[0673] The user starts the smartphone application and inputs initial information such as grade level, favorite subjects, weak subjects, and interests. The input information is sent from the device to the server. This input allows the server to understand the user's learning needs and collect initial setting data.

[0674] Step 2:

[0675] The server receives the initial information and inputs the prompt to the generative AI model (e.g., GPT-3). Based on this prompt, the generative AI model generates an optimal learning plan for the user. For example, the prompt might be, "Create a customized learning plan for a grade 8 student who is struggling with mathematics but has a keen interest in science. The learning plan should include daily math practice for 30 minutes and weekly science experiment videos." The server receives the learning plan obtained from the generative AI model and sends it to the device.

[0676] Step 3:

[0677] The terminal presents the optimal study plan sent from the server to the user, who then begins studying according to the presented study plan.

[0678] Step 4:

[0679] As the user progresses through their studies, they input their progress (e.g., level of understanding, interest, etc.) into the application. This progress information is sent from the device to the server, which uses the progress information as input data to provide the server with the basis for determining how the learning plan needs to be adjusted.

[0680] Step 5:

[0681] The server receives the progress information and uses a generative AI model to optimize the current lesson plan. Based on the prompt, the generative AI model generates a new lesson plan that reflects the progress. For example, the lesson plan is updated based on the progress information: "Math comprehension 80%, Science interest 95%." The server then sends the generated optimized lesson plan to the device.

[0682] Step 6:

[0683] The device presents the optimized study plan to the user and allows the user to continue studying according to the new study plan, so that the user can always use a study plan optimized to suit their own progress.

[0684] Step 7:

[0685] The user searches for an online community that interests them within the application and submits a request to join. The device then sends this request to the server. The input data is the user's interests, and the server then recommends appropriate online communities based on this information.

[0686] Step 8:

[0687] The server receives the participation request, recommends appropriate online communities based on the user's interests, generates recommendation information, approves the user's participation, and sends community information to the terminal.

[0688] Step 9:

[0689] The device displays online community information sent from the server to the user and allows the user to interact with other members. Users can receive support and exchange information within groups with shared interests.

[0690] Step 10:

[0691] The user inputs the details of their consultation and sends them from their device to the server via the application. The input data is the consultation details, and the server uses this information to obtain data for assigning the consultation to an appropriate counselor or AI teacher.

[0692] Step 11:

[0693] The server receives the consultation content, assigns it to an appropriate counselor or AI teacher, and then generates a reply and sends it to the device. Based on the reply, the server provides the support the user needs.

[0694] Step 12:

[0695] The device displays the reply from the counselor or AI teacher sent from the server to the user, allowing the user to receive the counseling support they need in a timely manner.

[0696] Step 13:

[0697] During learning or consultation, the smartphone's emotion recognition sensor collects the user's facial expressions and voice. This emotion data is sent from the device to the server. The input data is the user's emotional state, and the server obtains data for emotion analysis based on this.

[0698] Step 14:

[0699] The server receives the emotion data and analyzes it using an emotion engine. Based on the analysis results, it optimizes learning plans and counseling. It generates prompts based on the emotion data and creates optimal feedback using a generative AI model.

[0700] Step 15:

[0701] The server sends optimized learning plans and consultation details to the device, which then presents them to the user, allowing them to receive optimal support tailored to their emotional state.

[0702] 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.

[0703] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0704] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0705] [Second embodiment]

[0706] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0707] 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.

[0708] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0709] 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.

[0710] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0711] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0712] 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.

[0713] 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.

[0714] 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 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.

[0715] 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.

[0716] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0717] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0718] This invention is a system that provides learning opportunities and social connections to children who are not attending school. The system collects initial information about the user, uses generative AI to provide an optimal learning plan, and provides a means to participate in online communities and counseling functions.

[0719] Program processing overview

[0720] The system of the present invention performs the following main processes.

[0721] 1. User registration and initial settings

[0722] User: The user starts the application and enters initial information such as their grade, favorite subjects, least favorite subjects, and interests.

[0723] Terminal: The terminal collects the information entered by the user and generates a request to send the data to the server.

[0724] Server: The server receives the request, stores the user information in a database, and the generative AI creates an optimal initial learning plan.

[0725] 2. Generate a learning plan

[0726] Server: The generation AI generates a learning plan based on the user's initial information and sends it to the terminal.

[0727] Terminal: The terminal displays the received learning plan to the user.

[0728] 3. Learning progress and progress records

[0729] User: The user follows the presented learning plan and enters their progress into the learning application.

[0730] Terminal: The terminal collects the progress input by the user and sends it to the server.

[0731] Server: The server receives the progress information, the generation AI optimizes the learning plan, and sends the new learning plan to the device.

[0732] 4. Participating in online communities

[0733] Users: Users can search for community groups that interest them and submit a request to join.

[0734] Terminal: The terminal sends a join request to the server.

[0735] Server: The server receives the request, recommends groups based on the user's interests, and approves their participation.

[0736] 5. Use of the consultation function

[0737] User: The user enters the consultation content and submits it.

[0738] Terminal: The terminal sends the consultation content to the server.

[0739] Server: The server receives the consultation content, assigns it to an appropriate counselor or AI teacher, and sends the reply content to the terminal and displays it to the user.

[0740] Specific examples

[0741] An example of user registration and initial settings

[0742] 1. User: A 8th grade user launches the application and enters, "I'm not good at math, but I'm interested in science."

[0743] 2. Terminal: The terminal sends this to the server.

[0744] 3. Server: Based on the user information, the server uses a generative AI to generate an initial learning plan such as "basic math problems for 30 minutes every day, and watching science experiment videos once a week" and sends it to the device.

[0745] 4. Terminal: The terminal displays the lesson plan to the user.

[0746] An example of learning progress and progress record

[0747] 1. User: The user studies for a week and enters their progress (e.g., "Mathematics comprehension 80%, Science interest 95%) into the application.

[0748] 2. Terminal: The terminal sends progress information to the server.

[0749] 3. Server: The server receives the progress information, and the generation AI optimizes the learning plan based on this and sends the plan for the next week to the device.

[0750] 4. Device: The device presents the optimized learning plan to the user.

[0751] An example of using online communities

[0752] 1. User: A user becomes interested in the "Plant Growing Group" and sends a request to join.

[0753] 2. Terminal: The terminal sends a join request to the server.

[0754] 3. Server: The server approves participation based on the user's interests and sends community information to the terminal.

[0755] 4. Terminal: The terminal displays community information to the user and supports interaction with other members.

[0756] In this way, the system provides an optimal learning environment and social connections for children who are not attending school.

[0757] The processing flow will be explained below.

[0758] Step 1:

[0759] User: When the user starts the application for the first time, they enter their own information (grade, favorite subjects, least favorite subjects, interests, etc.) on the registration screen.

[0760] Step 2:

[0761] Terminal: The terminal receives the user's input information and generates a data transmission request to the server.

[0762] Step 3:

[0763] Server: The server receives user information and stores it in a database. The generation AI creates an optimal initial learning plan for the user based on the initial information received.

[0764] Step 4:

[0765] Server: Sends the generated learning plan to the device.

[0766] Step 5:

[0767] Device: The device displays the received learning plan to the user and prompts them to start learning.

[0768] Step 6:

[0769] User: The user follows the presented learning plan and proceeds with their studies. They enter their daily learning progress (e.g., learning content, level of understanding, study time, etc.) into the learning application.

[0770] Step 7:

[0771] Terminal: The terminal collects the progress information entered by the user and generates a request to send to the server.

[0772] Step 8:

[0773] Server: The server receives the progress information and stores it in a database. The generation AI analyzes the progress information and optimizes the existing learning plan.

[0774] Step 9:

[0775] Server: Sends the optimized learning plan to the device.

[0776] Step 10:

[0777] Device: The device presents the optimized learning plan to the user.

[0778] Step 11:

[0779] Users: Users want to join an online community and search for groups that interest them.

[0780] Step 12:

[0781] User: A user sends a request to join a group they wish to join.

[0782] Step 13:

[0783] Terminal: The terminal sends a join request to the server.

[0784] Step 14:

[0785] Server: The server receives the join request, recommends appropriate community groups based on the user's interests, and approves the join.

[0786] Step 15:

[0787] Server: Sends community information to the terminal.

[0788] Step 16:

[0789] Terminal: The terminal displays community information to the user and facilitates interaction with other members.

[0790] Step 17:

[0791] User: The user enters into the application what they would like to discuss with the counselor or AI teacher.

[0792] Step 18:

[0793] Terminal: The terminal generates a request to send the consultation content to the server.

[0794] Step 19:

[0795] Server: The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[0796] Step 20:

[0797] Server: Sends the reply from the counselor or AI teacher to the device.

[0798] Step 21:

[0799] Terminal: The terminal displays the reply from the counselor or AI teacher to the user.

[0800] In this way, the system provides learning plan generation and optimization, online community participation, and consultation functions according to user needs.

[0801] Example 1

[0802] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0803] In the modern education system, it is difficult for children who do not attend school to obtain an appropriate learning environment and social connections. In particular, it is difficult for children who do not attend school to create individual learning plans that correspond to their own learning progress, and there is insufficient support to maintain motivation for learning. Furthermore, there are challenges in providing children with opportunities to participate in communities to avoid social isolation and an environment where they can seek advice.

[0804] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0805] In this invention, the server includes means for collecting initial information from a user, artificial intelligence generating means for generating a study plan based on the initial information, means for presenting the study plan generated by the artificial intelligence generating means to the user, means for collecting the user's study progress, means for optimizing the study plan based on the study progress, means for presenting the optimized study plan to the user, means for supporting participation in an online community based on the user's interests, means for consulting with a counselor or an artificial intelligence teacher, means for transmitting the generated study plan to a terminal, and means for transmitting online community information to the terminal. This not only enables children who are not attending school to receive individually optimized study plans and have them adjusted according to their progress, but also provides an environment in which they can connect with society through the online community and receive appropriate consultation.

[0806] "User" refers to an individual who uses the system.

[0807] "Initial information" refers to basic information such as grade, favorite subjects, weak subjects, and interests that users enter when they start using the system.

[0808] "Generative artificial intelligence means" refers to an artificial intelligence that generates an optimal learning plan based on the user's initial information.

[0809] The term "study plan" refers to specific study content and schedules generated by the artificial intelligence generating means to enable the user to study effectively.

[0810] "Progress" refers to information that indicates the degree of progress in learning, such as the level of understanding or interest at that point in time, which is input by the user as they proceed with their learning.

[0811] "Optimization" refers to updating the study plan to the most effective content for the user based on the user's progress.

[0812] "Online Community" means a virtual community where Users can interact with other Users who share their interests.

[0813] "Counselor or AI teacher" refers to a human counselor or AI that responds to a user's consultation and provides appropriate advice and support.

[0814] "Terminal" refers to electronic devices such as smartphones, tablets, and personal computers that users use to operate the system.

[0815] "Server" refers to the central management system that manages users' initial information and progress information, and generates and optimizes learning plans using generative AI models.

[0816] MODE FOR CARRYING OUT THE INVENTION

[0817] System Overview

[0818] This invention is a system that provides learning opportunities and social connections to children who are not attending school. The system collects initial information about the user, uses a generative AI model to provide an optimal learning plan, and provides a means to participate in online communities and counseling functions. The entire system consists of three main components: the user, the device, and the server.

[0819] Hardware and Software

[0820] Terminal: Refers to an electronic device such as a smartphone, tablet, or PC that a user uses to access the system.

[0821] Server: A central management system that manages initial information and progress information from users and generates and optimizes learning plans using generative AI models. It uses a cloud-based data processing server (e.g., AWS, Google Cloud Platform).

[0822] Generative AI model: An AI model that generates and optimizes a learning plan based on the user's initial information and progress information. For example, OpenAI GPT-4 is used for this purpose.

[0823] Gathering initial information

[0824] User: Starts the application and enters initial information such as grade, favorite subjects, favorite subjects, and interests.

[0825] Terminal: Collects information entered by the user and sends it to the server in the form of an HTTP request.

[0826] Server: Stores the received user information in a database. Generates prompts from the generative AI model and asks it to generate an optimal learning plan. Sends the generated learning plan to the device.

[0827] View your learning plan

[0828] Terminal: Displays the learning plan received from the server to the user.

[0829] As a concrete example, a second-year junior high school student can input, "I'm not good at math, but I'm interested in science," and the generative AI model will generate a study plan such as, "Basic math problems for 30 minutes every day, watching science experiment videos once a week."

[0830] Learning progress and progress records

[0831] User: Study based on the presented study plan. After completing the study, enter the progress (for example, level of understanding and interest) into the application.

[0832] Terminal: Collects the progress information entered by the user and sends it to the server in the form of an HTTP request.

[0833] Server: Stores the received progress information in a database, sends prompts to the generative AI model to optimize the new learning plan, receives the new learning plan, and sends it to the device.

[0834] Terminal: Displays the newly received lesson plan to the user.

[0835] As a concrete example, based on the progress of "80% understanding of mathematics, 95% interest in science," the generative AI generates a new optimized learning plan.

[0836] Participating in online communities

[0837] Users: Search for online community groups that interest them within the application and submit a request to join.

[0838] Terminal: Sends a request to the server to join the community group selected by the user.

[0839] Server: Based on the received request, recommends groups based on the user's interests and approves their participation.

[0840] Terminal: Displays community information to users and helps them interact with other members.

[0841] As a specific example, if a user becomes interested in a "plant growing group" and sends a request to join, the server will approve the request and the terminal will display community information.

[0842] Use of consultation function

[0843] User: Enter the consultation details within the application and submit.

[0844] Terminal: Collects consultation details and sends them to the server.

[0845] Server: Assigns the received consultation content to the appropriate counselor or AI teacher, collects the reply content, and sends it to the device.

[0846] Terminal: Display the reply to the user.

[0847] As a specific example, a user inputs "advice about career paths," the server assigns it to a counselor, and the reply is sent to the terminal and displayed to the user.

[0848] Prompt Sentence Examples

[0849] "Please create the optimal study plan based on the initial information of the new user. The student is in the second year of junior high school, is not good at math, and is interested in science."

[0850] "Please optimize your study plan based on the progress information below. Your math comprehension is 80% and your science interest is 95%."

[0851] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0852] Step 1: User launches application and enters initial information

[0853] Input: Grade, strong subjects, weak subjects, interests

[0854] Output: User's initial information

[0855] Specific operation: The user enters initial information such as grade level, favorite subjects, weak subjects, and interests into the application's input form.

[0856] Step 2: The device sends initial information to the server

[0857] Input: Initial information entered by the user

[0858] Output: Data in HTTP request format

[0859] Specific operation: The terminal collects the user's initial information and sends it to the server as an HTTP request.

[0860] Step 3: The server saves the initial information and sends a prompt to the generative AI model.

[0861] Input: Initial information in the HTTP request format

[0862] Output: Saved user information, prompts to send to the generative AI model

[0863] Specific operation: The server stores the received user information in a database, then generates and sends prompts to the generative AI model to generate an optimal learning plan.

[0864] Step 4: The generative AI model generates a learning plan and sends it to the server

[0865] Input: prompt statement

[0866] Output: The generated learning plan

[0867] Specific operation: The generative AI model generates a learning plan based on the prompt sentence and sends the generated results back to the server.

[0868] Step 5: The server sends the generated lesson plan to the device.

[0869] Input: Generated lesson plan

[0870] Output: Learning plan in HTTP response format

[0871] Specific operation: The server sends the learning plan received from the generative AI model to the terminal as an HTTP response.

[0872] Step 6: The device displays the lesson plan to the user

[0873] Input: Lesson plan in HTTP response format

[0874] Output: A displayed lesson plan

[0875] Specific operation: The device displays the received study plan in an easy-to-understand manner to the user. Specifically, it displays a plan such as "30 minutes of basic math problems every day and watching science experiment videos once a week."

[0876] Step 7: User progresses and enters progress information

[0877] Input: Progress information (level of understanding, interest, etc.)

[0878] Output: User progress information

[0879] Specific operation: The user studies according to the presented study plan and enters their study progress in the application's input form.

[0880] Step 8: The device sends progress information to the server

[0881] Input: Progress information entered by the user

[0882] Output: Progress information in the form of an HTTP request

[0883] Specific operation: The device collects the user's progress information and sends it to the server as an HTTP request.

[0884] Step 9: The server saves the progress and sends a new prompt to the generative AI model.

[0885] Input: Progress information in HTTP request format

[0886] Output: Saved progress information, new prompt text

[0887] Specific operation: The server stores the received progress information in the database, then generates and sends a prompt to the generative AI model to generate a new learning plan.

[0888] Step 10: The generative AI model generates a new learning plan and sends it to the server.

[0889] Input: New prompt text

[0890] Output: The new learning plan generated.

[0891] Specific operation: The generative AI model generates a new learning plan based on progress information and sends the generated results back to the server.

[0892] Step 11: The server sends the new lesson plan to the device.

[0893] Input: New lesson plan

[0894] Output: The new learning plan in the form of an HTTP response.

[0895] Specific operation: The server sends the new learning plan received from the generative AI model to the terminal as an HTTP response.

[0896] Step 12: The device displays the new lesson plan to the user.

[0897] Input: A new learning plan in the form of an HTTP response

[0898] Output: The new lesson plan displayed.

[0899] Specific operation: The device displays the new learning plan to the user in an easy-to-understand manner.

[0900] Step 13: User finds online community and submits request to join

[0901] Input: Community name of interest

[0902] Output: Join request

[0903] What happens: A user searches for an online community group of interest within the application, selects one, and submits a request to join.

[0904] Step 14: The device sends a join request to the server

[0905] Input: Join request

[0906] Output: Join request in HTTP request format

[0907] Specific operation: The terminal collects the user's participation request and sends it to the server as an HTTP request.

[0908] Step 15: The server processes the join request and sends the online community information

[0909] Input: Join request

[0910] Output: Online community information

[0911] Specific operation: The server receives the participation request, recommends communities based on the user's interests, and sends information approving participation to the device.

[0912] Step 16: The terminal displays online community information to the user

[0913] Input: Online community information

[0914] Output: Displayed online community information

[0915] Specific operation: The device displays online community information to the user and supports interaction with other members.

[0916] (Application example 1)

[0917] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0918] Conventional learning support systems have struggled to provide optimal learning plans and connections to society for children who are not attending school. Furthermore, they lacked the ability to manage learning progress and dynamic responses to individual users' interests and needs. Furthermore, they lacked systems that integrated online communities and consultation functions, which led to students becoming isolated.

[0919] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0920] In this invention, the server includes means for collecting initial information from a user, artificial intelligence generating means for generating a study plan based on the initial information, means for presenting the study plan generated by the artificial intelligence generating means to the user, means for collecting the user's study progress, means for optimizing the study plan based on the study progress, means for presenting the optimized study plan to the user, means for supporting participation in an online community based on the user's interests, means for consulting with a counselor or an artificial intelligence teacher, and means for being installed as a smartphone application in a virtual store. This makes it possible to provide children who are not attending school with an individualized, optimal study plan, an online community, and consultation functions in an integrated manner.

[0921] "User information" refers to initial setting information about an individual user, such as grade, favorite subjects, weak subjects, and interests.

[0922] A "study plan" is a study schedule or curriculum created by the artificial intelligence based on user information.

[0923] "Generative AI" is an AI model that creates optimal learning plans based on user information.

[0924] "Progress" refers to information that records the user's learning progress and achievement level.

[0925] "Optimization" refers to the process of modifying and adjusting the learning plan based on the user's progress and needs.

[0926] An "online community" is an online group or forum that users can join based on their interests.

[0927] A "counselor" is a professional consultant who provides advice to users and offers solutions.

[0928] An "artificial intelligence teacher" is an AI-based support system provided by generative artificial intelligence to respond to user inquiries.

[0929] A "virtual store" is a virtual store that provides the same services online as a physical store.

[0930] A "smartphone application" is a software application that runs on a smartphone and allows users to use the system.

[0931] This invention is a system that provides optimal learning opportunities and social connections to children who are not attending school. This system is installed as a smartphone application in a virtual store, and is configured to allow users to access personalized learning plans, online communities, and even counseling services.

[0932] 1. System Configuration

[0933] Hardware

[0934] Smartphone: A device that allows users to access the system.

[0935] Server: Processes data, runs generative AI models, and manages databases.

[0936] software

[0937] Smartphone application: An app for collecting user information, displaying learning plans, recording progress, participating in online communities, and sending and receiving consultations.

[0938] Generative AI model: An AI model for creating learning plans based on user information.

[0939] 2. Operational flow

[0940] User registration and initial settings

[0941] The user launches the smartphone application and enters initial information such as their grade level, favorite subjects, weak subjects, and interests. The smartphone then sends this information to the server. The server stores the received information in a database and uses a generative AI model to create an optimal initial learning plan. The created learning plan is sent to the user via the smartphone application and displayed.

[0942] Learning progress and progress records

[0943] The user studies according to the generated study plan and inputs their progress into the smartphone application. The smartphone then sends the input progress to the server. The server then uses the generative AI model to optimize the study plan based on the received progress information and creates a new study plan. This plan is also sent to the user via the smartphone application and displayed.

[0944] Participating in online communities

[0945] Users search for community groups that interest them and send a request to join from their smartphone application. The server receives the request, recommends the most suitable group based on the user's interests, and approves their participation. The approval result and community information are notified to the smartphone, allowing users to interact with other members.

[0946] Use of consultation function

[0947] The user inputs the consultation content and sends it to the server via the smartphone application. The server receives the consultation content and assigns it to an appropriate counselor or AI model. The counselor or AI model then creates a reply and sends it to the smartphone application via the server, where it is displayed to the user.

[0948] 3. Specific Examples

[0949] Prompt Sentence Examples

[0950] For example, a second-year junior high school student launches the application and enters, "I'm not good at math, but I'm interested in science." An example of a study plan generated based on this user information might be, "Basic math problems, 30 minutes a day, watching science experiment videos once a week."

[0951] User initial information:

[0952] Grade: 2nd year of junior high school

[0953] Favorite subject: Science

[0954] Weak subject: Mathematics

[0955] Interests: Science experiments

[0956] Generate an optimal weekly study plan for this user.

[0957] In this way, this system provides children who are not attending school with an optimal, individualized learning plan, an online community, and consultation functions in an integrated manner, realizing learning opportunities and connections to society.

[0958] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0959] Step 1:

[0960] The user launches the smartphone application and enters their initial information (grade, favorite subjects, weak subjects, interests, etc.). The device collects this information and sends it to the server. The entered data includes grade, favorite / weak subjects, and interests, and a process is performed to transfer this data to the server. Specifically, when the submit button on the input form is pressed, an API request is sent.

[0961] Step 2:

[0962] The server stores the received initial information in a database. The stored data includes the user ID and corresponding grade, favorite subjects, weak subjects, and interests. The server inputs the stored data into the generative AI model and requests it to generate an optimal learning plan. Specific operations include a database insert query and prompt generation for the AI ​​model.

[0963] Step 3:

[0964] The generative AI model generates a study plan based on the input user information. An example of the generated study plan might be "basic math problems for 30 minutes every day, watching science experiment videos once a week." This plan is sent to the server, which then transmits it to the smartphone application. Specific operations include the AI ​​model's calculation process and the generation of API responses.

[0965] Step 4:

[0966] The device displays the learning plan received from the server to the user. The displayed learning plan includes specific assignments and a learning schedule, and the user follows this to progress with their studies. Specifically, the device performs a process of rendering the received data on the screen.

[0967] Step 5:

[0968] Users record their learning progress and enter it into a smartphone application. The device collects this progress data and sends it to the server. The progress data entered includes the level of understanding and achievement, and this is then transferred to the server. Specifically, an API request is sent when the send button on the progress input form is pressed.

[0969] Step 6:

[0970] The server stores the received progress information in a database and inputs it into the generative AI model. The generative AI model optimizes the learning plan based on the latest progress information. The optimized plan is sent to the server and then distributed to the smartphone application. Specific operations include issuing a database update query and generating a prompt for the AI ​​model.

[0971] Step 7:

[0972] A user searches for online communities that interest them and submits a request to join. The device then sends this request to the server, which then recommends the most suitable group based on the user's interests and approves their participation. Specific operations include sending a request and receiving an API response approving their participation.

[0973] Step 8:

[0974] The user enters the consultation details and sends them from the smartphone application to the server. The server receives the consultation details and assigns them to an appropriate counselor or AI model. The reply is sent to the smartphone application via the server and displayed to the user. Specific operations include sending the input form, generating an API response, and displaying the reply.

[0975] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0976] This invention is a system that provides learning opportunities and social connections to children who are not attending school. It further incorporates an emotion engine that recognizes the user's emotions to personalize the learning experience and provide emotional support. The system collects initial information about the user, uses generative AI to provide an optimal learning plan, and provides a means to participate in online communities and counseling functions. It also has the ability to recognize the user's emotions and adjust the learning plan and community participation support.

[0977] Program processing overview

[0978] The system of the present invention performs the following main processes.

[0979] 1. User registration and initial settings

[0980] User: The user starts the application and enters initial information such as their grade, favorite subjects, least favorite subjects, and interests.

[0981] Terminal: The terminal collects the user's input information and generates a data transmission request to the server.

[0982] Server: The server receives user information and stores it in a database. The generation AI creates an optimal initial learning plan for the user based on the initial information received.

[0983] 2. Generate a learning plan

[0984] Server: The generation AI generates a learning plan based on the user's initial information and sends it to the terminal.

[0985] Terminal: The terminal displays the received learning plan to the user.

[0986] 3. Learning progress and progress records

[0987] User: The user follows the presented learning plan and enters their progress into the learning application.

[0988] Terminal: The terminal collects the progress information entered by the user and generates a request to send to the server.

[0989] Server: The server receives the progress information and stores it in a database. The generation AI analyzes the progress information and optimizes the existing learning plan.

[0990] Server: Sends the optimized learning plan to the device.

[0991] Device: The device presents the optimized learning plan to the user.

[0992] 4. Participating in online communities

[0993] Users: Users can search for community groups that interest them and submit a request to join.

[0994] Terminal: The terminal sends a join request to the server.

[0995] Server: The server receives the join request, recommends appropriate community groups based on the user's interests, and approves the join.

[0996] Server: Sends community information to the terminal.

[0997] Terminal: The terminal displays community information to the user and facilitates interaction with other members.

[0998] 5. Use of the consultation function

[0999] User: The user enters the consultation content and submits it.

[1000] Terminal: The terminal generates a request to send the consultation content to the server.

[1001] Server: The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[1002] Server: Sends the reply from the counselor or AI teacher to the device.

[1003] Terminal: The terminal displays the reply from the counselor or AI teacher to the user.

[1004] 6. Use of Emotion Engines

[1005] Terminal: The terminal activates sensors (e.g., facial recognition camera, voice analysis, etc.) to recognize the user's emotions.

[1006] User: The user displays emotions such as facial expressions and voice on the input device while learning or consulting.

[1007] Terminal: The terminal collects emotion data and generates a request to send it to the server.

[1008] Server: The server receives the emotion data and the emotion engine analyzes it.

[1009] Emotional response: The emotion engine optimizes feedback on the user's study plan and consultation content based on the results of emotion analysis.

[1010] Terminal: Presents the user with a learning plan and consultation content optimized by the emotion engine.

[1011] Specific examples

[1012] An example of user registration and initial settings

[1013] 1. User: A 8th grade user launches the application and enters, "I'm not good at math, but I'm interested in science."

[1014] 2. Terminal: The terminal sends this to the server.

[1015] 3. Server: Based on the user information, the server uses a generative AI to generate an initial learning plan such as "basic math problems for 30 minutes every day, and watching science experiment videos once a week" and sends it to the device.

[1016] 4. Terminal: The terminal displays the lesson plan to the user.

[1017] An example of learning progress and progress record

[1018] 1. User: The user studies for a week and enters their progress (e.g., "Mathematics comprehension 80%, Science interest 95%) into the application.

[1019] 2. Terminal: The terminal sends progress information to the server.

[1020] 3. Server: The server receives the progress information, and the generation AI optimizes the learning plan based on this information and sends the new learning plan to the device.

[1021] 4. Device: The device presents the optimized learning plan to the user.

[1022] An example of using online communities

[1023] 1. User: A user becomes interested in the "Plant Growing Group" and sends a request to join.

[1024] 2. Terminal: The terminal sends a join request to the server.

[1025] 3. Server: The server approves participation based on the user's interests and sends community information to the terminal.

[1026] 4. Terminal: The terminal displays community information to the user and supports interaction with other members.

[1027] An example of using the emotion engine

[1028] 1. Device: The device captures the user's facial expressions with a camera and recognizes their emotions.

[1029] 2. Server: The server analyzes the emotional data and determines that the user is feeling stressed.

[1030] 3. Emotional response: The emotion engine optimizes learning content to temporarily ease user stress and enhances consultation functions.

[1031] 4. Terminal: Presents the user with a learning plan and consultation options tailored by the emotion engine.

[1032] In this way, the system provides individually optimized learning environments and psychological support to children who are not attending school.

[1033] The processing flow will be explained below.

[1034] Step 1:

[1035] User: The user launches the application and enters their own information (grade, favorite subjects, weak subjects, interests, etc.) on the registration screen.

[1036] Step 2:

[1037] Terminal: The terminal receives the user's input information and generates a data transmission request to the server.

[1038] Step 3:

[1039] Server: The server receives user information and stores it in a database. The generation AI creates an optimal initial learning plan for the user based on the initial information received.

[1040] Step 4:

[1041] Server: Sends the generated learning plan to the device.

[1042] Step 5:

[1043] Device: The device displays the received learning plan to the user and prompts them to start learning.

[1044] Step 6:

[1045] User: The user follows the presented learning plan and proceeds with their studies. They enter their daily learning progress (e.g., learning content, level of understanding, study time, etc.) into the learning application.

[1046] Step 7:

[1047] Terminal: The terminal collects the progress information entered by the user and generates a request to send to the server.

[1048] Step 8:

[1049] Server: The server receives the progress information and stores it in a database. The generation AI analyzes the progress information and optimizes the existing learning plan.

[1050] Step 9:

[1051] Server: Sends the optimized learning plan to the device.

[1052] Step 10:

[1053] Device: The device presents the optimized learning plan to the user.

[1054] Step 11:

[1055] Users: Users want to join an online community and search for groups that interest them.

[1056] Step 12:

[1057] User: A user sends a request to join a group they wish to join.

[1058] Step 13:

[1059] Terminal: The terminal sends a join request to the server.

[1060] Step 14:

[1061] Server: The server receives the join request, recommends appropriate community groups based on the user's interests, and approves the join.

[1062] Step 15:

[1063] Server: Sends community information to the terminal.

[1064] Step 16:

[1065] Terminal: The terminal displays community information to the user and facilitates interaction with other members.

[1066] Step 17:

[1067] User: The user enters into the application what they would like to discuss with the counselor or AI teacher.

[1068] Step 18:

[1069] Terminal: The terminal generates a request to send the consultation content to the server.

[1070] Step 19:

[1071] Server: The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[1072] Step 20:

[1073] Server: Sends the reply from the counselor or AI teacher to the device.

[1074] Step 21:

[1075] Terminal: The terminal displays the reply from the counselor or AI teacher to the user.

[1076] Step 22:

[1077] Terminal: The terminal activates sensors (e.g., facial recognition camera, voice analysis, etc.) to recognize the user's emotions.

[1078] Step 23:

[1079] User: The user displays emotions such as facial expressions and voice on the input device while learning or consulting.

[1080] Step 24:

[1081] Terminal: The terminal collects emotion data and generates a request to send it to the server.

[1082] Step 25:

[1083] Server: The server receives the emotion data and the emotion engine analyzes it.

[1084] Step 26:

[1085] Emotional response: The emotion engine optimizes feedback on the user's study plan and consultation content based on the results of emotion analysis.

[1086] Step 27:

[1087] Terminal: Presents the user with a learning plan and consultation content optimized by the emotion engine.

[1088] Through the above process, this system can provide individually optimized learning environments and psychological support to children who are not attending school.

[1089] Example 2

[1090] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1091] In modern society, the problem of children not attending school is extremely serious, with a lack of learning opportunities and social connections being major issues. Furthermore, these children require emotional support and require individualized learning plans and psychological assistance. However, existing education systems do not adequately create learning plans tailored to the needs of individual children or recognize and respond to their emotions. As a result, children may lose motivation to learn and become even more isolated.

[1092] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting initial information from a user; artificial intelligence generating means for generating a study plan based on the initial information; means for presenting the study plan generated by the artificial intelligence generating means to the user; means for collecting the user's learning progress; means for optimizing the study plan based on the learning progress; means for presenting the optimized study plan to the user; means for supporting participation in an online community based on the user's interests; means for consulting with a counselor or an artificial intelligence teacher; means including a sensor for recognizing the user's emotions; emotional response means for analyzing the emotional data and optimizing the study plan and consultation content; and means for presenting the optimized study plan and consultation content to the user based on the emotional response. This makes it possible to provide an individualized learning environment and psychological support to children who are not attending school, thereby improving their motivation to study and promoting their connections with society.

[1093] "User" refers to the learner who uses the system, specifically the entity who inputs initial information, follows a learning plan, and reports progress.

[1094] "Initial information" refers to basic data such as grade, favorite subjects, weak subjects, and interests that users enter when using the system.

[1095] "Study plan" refers to a schedule of learning content that is most suitable for a user, created by the generation AI based on the user's initial information.

[1096] "Generative AI means" refers to the AI ​​technology deployed to generate an optimal learning plan based on the user's initial information.

[1097] "Study progress status" refers to progress information entered by a user as a result of studying according to a study plan.

[1098] The "optimizing means" refers to a means for adjusting the study plan based on the user's study progress and generating a new optimized study plan.

[1099] An "online community" refers to a virtual group of users who share common interests and who can interact with each other and share information.

[1100] "Counselor or AI teacher" refers to a real-life expert or AI (artificial intelligence) technology that provides appropriate advice and support in response to the consultation content entered by the user.

[1101] A "sensor" is a device used to recognize a user's emotions, and includes facial recognition cameras and voice analysis devices.

[1102] "Emotion data" refers to information about emotions obtained from the user's facial expressions, voice, etc. collected by sensors.

[1103] "Emotional response" refers to the process of analyzing emotional data and optimizing the user's study plan or consultation content based on the results.

[1104] MODE FOR CARRYING OUT THE INVENTION

[1105] The present invention is a system that provides learning opportunities and social connections to children who are not attending school, and by further incorporating an emotion engine that recognizes the user's emotions, it personalizes the learning experience and provides emotional support.

[1106] This system performs various data processing using the following hardware and software:

[1107] Hardware: smartphones, tablets, laptops, facial recognition cameras, voice analysis devices

[1108] Software: Applications that collect user information, generative AI (e.g., GPT-4), database management systems, online community platforms, sentiment analysis engines

[1109] The specific processing flow is shown below.

[1110] User registration and initial settings

[1111] The user starts the learning application and enters initial information such as their grade, favorite subjects, weak subjects, and interests.

[1112] Example: A second-year junior high school student enters, "I'm not good at math, but I'm interested in science."

[1113] The terminal collects the input information and generates a data transmission request to the server.

[1114] The server receives the user's initial information and stores it in a database.

[1115] Example: The server receives user information and records it in a database as "user ID, grade, favorite subjects, weak subjects, and interests."

[1116] The server uses generative AI to create an optimal learning plan based on the user's initial information.

[1117] Example: Generative AI generates a study plan such as "Basic math problems for 30 minutes every day, and watch science experiment videos once a week."

[1118] The server sends the generated learning plan to the terminal.

[1119] The terminal displays the received learning plan to the user.

[1120] An example of learning progress and progress record

[1121] The user studies according to the presented study plan and enters their progress into the application.

[1122] Example: A user enters into an application, "I understand math 80% and I'm interested in science 95%."

[1123] The terminal collects the progress information and generates a transmission request to the server.

[1124] The server receives the progress information and stores it in a database.

[1125] The server uses generated AI to analyze progress information and optimize learning plans.

[1126] Example: A generative AI adjusts next week's math problem set based on progress results.

[1127] The server sends the optimized learning plan to the device.

[1128] The device displays the optimized study plan to the user.

[1129] An example of participating in an online community

[1130] Users search for community groups that interest them and submit a request to join.

[1131] Example: A user searches for "plant growing group" and sends a request to join.

[1132] The device sends a join request to the server.

[1133] The server receives the request, recommends appropriate community groups, and approves participation.

[1134] The server transmits the community information to the terminal.

[1135] The terminal displays community information to the user and encourages interaction with other members.

[1136] An example of using the consultation function

[1137] The user inputs the consultation content and sends it.

[1138] Example: A user types, "Studying is hard."

[1139] The terminal generates a request to transmit the consultation content to the server.

[1140] The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[1141] The server sends the reply from the counselor or AI teacher to the device.

[1142] The device displays the reply from the counselor or AI teacher to the user.

[1143] An example of using the emotion engine

[1144] The device activates sensors (facial recognition camera, voice analysis, etc.) to recognize the user's emotions.

[1145] Example: The device captures the user's facial expressions through the camera.

[1146] The user indicates emotions (e.g., facial expressions or voice) using an input device.

[1147] The device collects emotion data and generates a transmission request to the server.

[1148] The server receives the emotion data and the emotion engine analyzes it.

[1149] Example: Determining that a user is feeling stressed based on emotional data.

[1150] Emotional response: The emotion engine optimizes learning plans and consultation content based on the analysis results.

[1151] Example: Reduce the learning load or add a refresh feature.

[1152] The device presents the user with optimized learning plans and consultation contents.

[1153] This makes it possible for the system to provide children who are not attending school with an individualized learning environment and psychological support, thereby improving their motivation to learn and promoting connections with society.

[1154] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1155] Program processing flow

[1156] User registration and initial settings

[1157] Step 1:

[1158] The user launches an application.

[1159] Specific operation: A second-year junior high school student launches the app on his smartphone and the initial startup screen is displayed.

[1160] Input: User action

[1161] Output: Application Launch

[1162] Step 2:

[1163] The user enters their initial information.

[1164] Specific operation: The user inputs "grade: second year of junior high school, favorite subject: science, least favorite subject: mathematics, interest: natural science."

[1165] Input: Grade, favorite subjects, weak subjects, interests

[1166] Output: Initial information data

[1167] Step 3:

[1168] The terminal collects the input information and generates a data transmission request to the server.

[1169] Specific operation: The terminal compiles the user's initial information and sends it to the server.

[1170] Input: Initial information data

[1171] Output: Request sent to server

[1172] Step 4:

[1173] The server receives the user's initial information and stores it in a database.

[1174] Specific operation: The server records the received user information in a database.

[1175] Input: Initial information data

[1176] Output: Save to database

[1177] Step 5:

[1178] The server uses generative AI to create an optimal learning plan based on the user's initial information.

[1179] Specific operation: The generative AI generates a study plan such as "basic math problems for 30 minutes every day, and watching science experiment videos once a week."

[1180] Input: Initial information data

[1181] Output: Learning plan

[1182] Step 6:

[1183] The server sends the generated learning plan to the terminal.

[1184] Specific operation: The server transfers the newly generated learning plan to the user's device.

[1185] Input:Study plan

[1186] Output: Data sent to the terminal

[1187] Step 7:

[1188] The terminal displays the received learning plan to the user.

[1189] Specific behavior: The device displays the lesson plan within the app and asks the user to confirm it.

[1190] Input: Learning plan data

[1191] Output: What is displayed to the user

[1192] Learning progress and progress records

[1193] Step 1:

[1194] The user proceeds with their studies according to the presented study plan.

[1195] Specific action: The user studies math for 30 minutes every day.

[1196] Input:Study plan

[1197] Output: Learning progress

[1198] Step 2:

[1199] The user enters their learning progress into the application.

[1200] Specific operation: The user reports to the app that "math comprehension level is 80% and science interest level is 95%."

[1201] Input: Progress information

[1202] Output: Progress data

[1203] Step 3:

[1204] The terminal collects the progress information and generates a transmission request to the server.

[1205] Specific operation: The device compiles progress data and sends it to the server.

[1206] Input: Progress data

[1207] Output: Request sent to server

[1208] Step 4:

[1209] The server receives the progress information and stores it in a database.

[1210] What happens: The server adds the new progress data to the database.

[1211] Input: Progress data

[1212] Output: Save to database

[1213] Step 5:

[1214] The server uses generated AI to analyze progress information and optimize learning plans.

[1215] Specific operation: The generative AI generates a new problem set based on the user's level of mathematical understanding.

[1216] Input: Progress data

[1217] Output: Optimized learning plan

[1218] Step 6:

[1219] The server sends the optimized learning plan to the device.

[1220] Specific operation: The optimized plan is sent from the server to the user's terminal.

[1221] Input: Optimized Study Plan

[1222] Output: Data sent to the terminal

[1223] Step 7:

[1224] The device presents the optimized study plan to the user.

[1225] Specific behavior: The device displays the new lesson plan to the user.

[1226] Input: Optimized lesson plan data

[1227] Output: What is displayed to the user

[1228] Participating in online communities

[1229] Step 1:

[1230] Users search for community groups that interest them and submit a request to join.

[1231] Specific behavior: A user searches for "plant growing group" and sends a request to join.

[1232] Input: Interest words

[1233] Output: Join request data

[1234] Step 2:

[1235] The device sends a join request to the server.

[1236] Specific operation: The terminal sends a request to the server.

[1237] Input: Join request data

[1238] Output: Data sent to the server

[1239] Step 3:

[1240] The server receives the request, recommends appropriate community groups, and approves participation.

[1241] Specific Operation: The server approves the user to join a plant growing group based on their interests.

[1242] Input: Join request data

[1243] Output: Approval data and recommendation data

[1244] Step 4:

[1245] The server transmits the community information to the terminal.

[1246] Specific operation: Community information is sent from the server to the terminal.

[1247] Input: Approval data and recommendation data

[1248] Output: Data sent to the terminal

[1249] Step 5:

[1250] The terminal displays community information to the user and encourages interaction with other members.

[1251] Specific operation: The device displays the latest community information, message boards, etc. to the user.

[1252] Input: Community information data

[1253] Output: What is displayed to the user

[1254] Use of consultation function

[1255] Step 1:

[1256] The user inputs the consultation content and sends it.

[1257] Specific action: The user enters "Studying is hard" into the app.

[1258] Input: Consultation details

[1259] Output: Consultation data

[1260] Step 2:

[1261] The terminal generates a request to transmit the consultation content to the server.

[1262] Specific operation: The device prepares to send the consultation content to the server.

[1263] Input: Consultation data

[1264] Output: Request sent to server

[1265] Step 3:

[1266] The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[1267] Specific operation: The server transfers the consultation content to a psychological counselor.

[1268] Input: Consultation data

[1269] Output: Counselor assignment data

[1270] Step 4:

[1271] The server sends the reply from the counselor or AI teacher to the device.

[1272] Specific operation: The reply from the counselor is sent to the terminal via the server.

[1273] Input: Reply content data

[1274] Output: Data sent to the terminal

[1275] Step 5:

[1276] The device displays the reply from the counselor or AI teacher to the user.

[1277] Specific operation: The device displays advice from the counselor.

[1278] Input: Reply content data

[1279] Output: What is displayed to the user

[1280] Use of emotion engine

[1281] Step 1:

[1282] The device activates sensors (facial recognition camera, voice analysis, etc.) to recognize the user's emotions.

[1283] Specific operation: The device captures the user's facial expression through the built-in camera.

[1284] Input: Sensor activation request

[1285] Output: Sensor activation

[1286] Step 2:

[1287] The user indicates emotions such as facial expressions and voice on the input device.

[1288] Specific action: The user smiles or frowns in front of the camera.

[1289] Input: Real-time facial and voice data

[1290] Output: Emotion data

[1291] Step 3:

[1292] The device collects emotion data and generates a transmission request to the server.

[1293] Specific operation: The device prepares to send the collected emotion data to the server.

[1294] Input: Emotion data

[1295] Output: Request sent to server

[1296] Step 4:

[1297] The server receives the emotion data and the emotion engine analyzes it.

[1298] Specific operation: The server analyzes the user's stress level from facial expression data.

[1299] Input: Emotion data

[1300] Output: Analysis results

[1301] Step 5:

[1302] Emotional response: The emotion engine optimizes learning plans and consultation content based on the analysis results.

[1303] Specific behavior: The emotion engine reduces the learning load and provides a refresh function.

[1304] Input: Analysis results

[1305] Output: Optimized study plan and consultation content

[1306] Step 6:

[1307] The device presents the user with optimized learning plans and consultation contents.

[1308] Specific operation: The device displays the new learning plan and consultation details to the user.

[1309] Input: Optimized learning plan and consultation data

[1310] Output: What is displayed to the user

[1311] (Application example 2)

[1312] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1313] Existing online learning systems for school-refusing children often lack individualization and emotional support. They also lack the ability to provide effective social connections, such as learning plans based on users' interests and online communities. Furthermore, they lack the ability to optimize learning plans and counseling functions that incorporate users' emotional data, resulting in insufficient psychological support.

[1314] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting initial information from the user; artificial intelligence generating means for generating a study plan based on the initial information; means for presenting the study plan generated by the artificial intelligence generating means to the user; means for collecting the user's study progress; means for optimizing the study plan based on the study progress; means for presenting the optimized study plan to the user; means for supporting participation in an online community based on the user's interests; means for consulting with a counselor or an artificial intelligence teacher; means for collecting user emotion data using an emotion recognition sensor; and emotion engine means for analyzing the emotion data and optimizing the study plan or consultation content. This makes it possible to provide an individualized study plan, provide emotional support, and strengthen social connections through the online community.

[1315] "Initial information" is basic information for generating a study plan, such as the user's grade, favorite subjects, weak subjects, and interests.

[1316] The "generative artificial intelligence means" is an artificial intelligence technology for generating an optimal learning plan based on initial information provided by the user.

[1317] A "study plan" is a plan that specifically shows how a user will actually proceed with their studies, and its contents are created by a generative artificial intelligence means.

[1318] An "emotion recognition sensor" is a sensor device that recognizes a user's facial expressions and voice and detects their emotional state.

[1319] The "emotion engine means" is a technology for analyzing the user's emotional data acquired from the emotion recognition sensor and optimizing the study plan and counseling based on the data.

[1320] An "online community" is a virtual group that users can join based on common interests.

[1321] "Counselor or AI teacher" refers to a counseling function using a real counselor or AI that provides support based on the user's consultation content.

[1322] Generating a Program

[1323] The system for implementing this invention is composed of a program that performs the following main processes: A system for providing individualized learning plans and emotional support to children who are not attending school.

[1324] Program processing overview

[1325] The system consists of several steps: user registration and initial setup, generating and optimizing a learning plan, recording learning progress and progress, participating in an online community, counseling functions, and using an emotion engine.

[1326] Hardware and Software Used

[1327] Smartphone: A device on which users install and use applications.

[1328] Flask: A server-side web framework.

[1329] Generative AI models: For example, OpenAI's GPT-3.

[1330] Emotion engine: Libraries and services used for user facial expression recognition and voice analysis (e.g., Google Cloud Vision).

[1331] Program processing explanation

[1332] 1. Collecting initial user information

[1333] The user enters initial information such as grade level, favorite subjects, weak subjects, and interests through a smartphone application. This information is then sent from the smartphone to the server.

[1334] 2. Generate a learning plan

[1335] The server uses a generative AI model (e.g., GPT-3) to generate an optimal learning plan based on the received initial information. The generated learning plan is sent to the smartphone and presented to the user.

[1336] 3. Track and optimize your learning progress

[1337] As the user progresses through their studies, they input their progress into the application. The server receives the progress information and optimizes the study plan using a generative AI model. The optimized study plan is then presented to the user again.

[1338] 4. Participating in online communities

[1339] Users search for community groups that interest them and send a request to join. The server receives the request, recommends appropriate groups based on the user's interests, and approves their participation. Community information is sent to the smartphone and displayed to the user.

[1340] 5. Use of counseling functions

[1341] When a user inputs their consultation details, the details are sent to the server, which then assigns the consultation details to an appropriate counselor or AI teacher and sends the answer to the user.

[1342] 6. Use of Emotion Engines

[1343] The smartphone's emotion recognition sensor recognizes the user's facial expressions and voice and sends the data to a server, which then uses an emotion engine to analyze the emotion data and optimize the learning plan and counseling content.

[1344] Specific examples

[1345] Example of user registration and initial settings

[1346] The user launches the application and enters, "I'm in the second year of junior high school, I'm not good at math, and I'm interested in science." This information is sent to the server, and the generative AI model generates an initial study plan, such as "basic math problems, 30 minutes a day, watching science experiment videos once a week." The generated study plan is then displayed on the smartphone.

[1347] Examples of learning progress recording and optimization

[1348] The user studies for a week and enters progress information into the application, such as "Mathematics comprehension 80%, Science interest 95%." The server receives the progress information, and the generative AI model uses it to optimize the study plan. The new study plan is then presented on the smartphone.

[1349] Examples of prompt statements

[1350] Examples of prompts for generative AI models include:

[1351] "Create a customized learning plan for a grade 8 student who is struggling with mathematics but has a keen interest in science. The learning plan should include daily math practice for 30 minutes and weekly science experiment videos."

[1352] In this way, the present invention can provide a personalized learning environment and psychological support for children who are not attending school.

[1353] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1354] Step 1:

[1355] The user starts the smartphone application and inputs initial information such as grade level, favorite subjects, weak subjects, and interests. The input information is sent from the device to the server. This input allows the server to understand the user's learning needs and collect initial setting data.

[1356] Step 2:

[1357] The server receives the initial information and inputs the prompt to the generative AI model (e.g., GPT-3). Based on this prompt, the generative AI model generates an optimal learning plan for the user. For example, the prompt might be, "Create a customized learning plan for a grade 8 student who is struggling with mathematics but has a keen interest in science. The learning plan should include daily math practice for 30 minutes and weekly science experiment videos." The server receives the learning plan obtained from the generative AI model and sends it to the device.

[1358] Step 3:

[1359] The terminal presents the optimal study plan sent from the server to the user, who then begins studying according to the presented study plan.

[1360] Step 4:

[1361] As the user progresses through their studies, they input their progress (e.g., level of understanding, interest, etc.) into the application. This progress information is sent from the device to the server, which uses the progress information as input data to provide the server with the basis for determining how the learning plan needs to be adjusted.

[1362] Step 5:

[1363] The server receives the progress information and uses a generative AI model to optimize the current lesson plan. Based on the prompt, the generative AI model generates a new lesson plan that reflects the progress. For example, the lesson plan is updated based on the progress information: "Math comprehension 80%, Science interest 95%." The server then sends the generated optimized lesson plan to the device.

[1364] Step 6:

[1365] The device presents the optimized study plan to the user and allows the user to continue studying according to the new study plan, so that the user can always use a study plan optimized to suit their own progress.

[1366] Step 7:

[1367] The user searches for an online community that interests them within the application and submits a request to join. The device then sends this request to the server. The input data is the user's interests, and the server then recommends appropriate online communities based on this information.

[1368] Step 8:

[1369] The server receives the participation request, recommends appropriate online communities based on the user's interests, generates recommendation information, approves the user's participation, and sends community information to the terminal.

[1370] Step 9:

[1371] The device displays online community information sent from the server to the user and allows the user to interact with other members. Users can receive support and exchange information within groups with shared interests.

[1372] Step 10:

[1373] The user inputs the details of their consultation and sends them from their device to the server via the application. The input data is the consultation details, and the server uses this information to obtain data for assigning the consultation to an appropriate counselor or AI teacher.

[1374] Step 11:

[1375] The server receives the consultation content, assigns it to an appropriate counselor or AI teacher, and then generates a reply and sends it to the device. Based on the reply, the server provides the support the user needs.

[1376] Step 12:

[1377] The device displays the reply from the counselor or AI teacher sent from the server to the user, allowing the user to receive the counseling support they need in a timely manner.

[1378] Step 13:

[1379] During learning or consultation, the smartphone's emotion recognition sensor collects the user's facial expressions and voice. This emotion data is sent from the device to the server. The input data is the user's emotional state, and the server obtains data for emotion analysis based on this.

[1380] Step 14:

[1381] The server receives the emotion data and analyzes it using an emotion engine. Based on the analysis results, it optimizes learning plans and counseling. It generates prompts based on the emotion data and creates optimal feedback using a generative AI model.

[1382] Step 15:

[1383] The server sends optimized learning plans and consultation details to the device, which then presents them to the user, allowing them to receive optimal support tailored to their emotional state.

[1384] 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.

[1385] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1386] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1387] [Third embodiment]

[1388] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1389] 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.

[1390] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1391] 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.

[1392] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1393] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1394] 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.

[1395] 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.

[1396] 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 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.

[1397] 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.

[1398] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1399] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1400] This invention is a system that provides learning opportunities and social connections to children who are not attending school. The system collects initial information about the user, uses generative AI to provide an optimal learning plan, and provides a means to participate in online communities and counseling functions.

[1401] Program processing overview

[1402] The system of the present invention performs the following main processes.

[1403] 1. User registration and initial settings

[1404] User: The user starts the application and enters initial information such as their grade, favorite subjects, least favorite subjects, and interests.

[1405] Terminal: The terminal collects the information entered by the user and generates a request to send the data to the server.

[1406] Server: The server receives the request, stores the user information in a database, and the generative AI creates an optimal initial learning plan.

[1407] 2. Generate a learning plan

[1408] Server: The generation AI generates a learning plan based on the user's initial information and sends it to the terminal.

[1409] Terminal: The terminal displays the received learning plan to the user.

[1410] 3. Learning progress and progress records

[1411] User: The user follows the presented learning plan and enters their progress into the learning application.

[1412] Terminal: The terminal collects the progress input by the user and sends it to the server.

[1413] Server: The server receives the progress information, the generation AI optimizes the learning plan, and sends the new learning plan to the device.

[1414] 4. Participating in online communities

[1415] Users: Users can search for community groups that interest them and submit a request to join.

[1416] Terminal: The terminal sends a join request to the server.

[1417] Server: The server receives the request, recommends groups based on the user's interests, and approves their participation.

[1418] 5. Use of the consultation function

[1419] User: The user enters the consultation content and submits it.

[1420] Terminal: The terminal sends the consultation content to the server.

[1421] Server: The server receives the consultation content, assigns it to an appropriate counselor or AI teacher, and sends the reply content to the terminal and displays it to the user.

[1422] Specific examples

[1423] An example of user registration and initial settings

[1424] 1. User: A 8th grade user launches the application and enters, "I'm not good at math, but I'm interested in science."

[1425] 2. Terminal: The terminal sends this to the server.

[1426] 3. Server: Based on the user information, the server uses a generative AI to generate an initial learning plan such as "basic math problems for 30 minutes every day, and watching science experiment videos once a week" and sends it to the device.

[1427] 4. Terminal: The terminal displays the lesson plan to the user.

[1428] An example of learning progress and progress record

[1429] 1. User: The user studies for a week and enters their progress (e.g., "Mathematics comprehension 80%, Science interest 95%) into the application.

[1430] 2. Terminal: The terminal sends progress information to the server.

[1431] 3. Server: The server receives the progress information, and the generation AI optimizes the learning plan based on this and sends the plan for the next week to the device.

[1432] 4. Device: The device presents the optimized learning plan to the user.

[1433] An example of using online communities

[1434] 1. User: A user becomes interested in the "Plant Growing Group" and sends a request to join.

[1435] 2. Terminal: The terminal sends a join request to the server.

[1436] 3. Server: The server approves participation based on the user's interests and sends community information to the terminal.

[1437] 4. Terminal: The terminal displays community information to the user and supports interaction with other members.

[1438] In this way, the system provides an optimal learning environment and social connections for children who are not attending school.

[1439] The processing flow will be explained below.

[1440] Step 1:

[1441] User: When the user starts the application for the first time, they enter their own information (grade, favorite subjects, least favorite subjects, interests, etc.) on the registration screen.

[1442] Step 2:

[1443] Terminal: The terminal receives the user's input information and generates a data transmission request to the server.

[1444] Step 3:

[1445] Server: The server receives user information and stores it in a database. The generation AI creates an optimal initial learning plan for the user based on the initial information received.

[1446] Step 4:

[1447] Server: Sends the generated learning plan to the device.

[1448] Step 5:

[1449] Device: The device displays the received learning plan to the user and prompts them to start learning.

[1450] Step 6:

[1451] User: The user follows the presented learning plan and proceeds with their studies. They enter their daily learning progress (e.g., learning content, level of understanding, study time, etc.) into the learning application.

[1452] Step 7:

[1453] Terminal: The terminal collects the progress information entered by the user and generates a request to send to the server.

[1454] Step 8:

[1455] Server: The server receives the progress information and stores it in a database. The generation AI analyzes the progress information and optimizes the existing learning plan.

[1456] Step 9:

[1457] Server: Sends the optimized learning plan to the device.

[1458] Step 10:

[1459] Device: The device presents the optimized learning plan to the user.

[1460] Step 11:

[1461] Users: Users want to join an online community and search for groups that interest them.

[1462] Step 12:

[1463] User: A user sends a request to join a group they wish to join.

[1464] Step 13:

[1465] Terminal: The terminal sends a join request to the server.

[1466] Step 14:

[1467] Server: The server receives the join request, recommends appropriate community groups based on the user's interests, and approves the join.

[1468] Step 15:

[1469] Server: Sends community information to the terminal.

[1470] Step 16:

[1471] Terminal: The terminal displays community information to the user and facilitates interaction with other members.

[1472] Step 17:

[1473] User: The user enters into the application what they would like to discuss with the counselor or AI teacher.

[1474] Step 18:

[1475] Terminal: The terminal generates a request to send the consultation content to the server.

[1476] Step 19:

[1477] Server: The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[1478] Step 20:

[1479] Server: Sends the reply from the counselor or AI teacher to the device.

[1480] Step 21:

[1481] Terminal: The terminal displays the reply from the counselor or AI teacher to the user.

[1482] In this way, the system provides learning plan generation and optimization, online community participation, and consultation functions according to user needs.

[1483] Example 1

[1484] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1485] In the modern education system, it is difficult for children who do not attend school to obtain an appropriate learning environment and social connections. In particular, it is difficult for children who do not attend school to create individual learning plans that correspond to their own learning progress, and there is insufficient support to maintain motivation for learning. Furthermore, there are challenges in providing children with opportunities to participate in communities to avoid social isolation and an environment where they can seek advice.

[1486] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1487] In this invention, the server includes means for collecting initial information from a user, artificial intelligence generating means for generating a study plan based on the initial information, means for presenting the study plan generated by the artificial intelligence generating means to the user, means for collecting the user's study progress, means for optimizing the study plan based on the study progress, means for presenting the optimized study plan to the user, means for supporting participation in an online community based on the user's interests, means for consulting with a counselor or an artificial intelligence teacher, means for transmitting the generated study plan to a terminal, and means for transmitting online community information to the terminal. This not only enables children who are not attending school to receive individually optimized study plans and have them adjusted according to their progress, but also provides an environment in which they can connect with society through the online community and receive appropriate consultation.

[1488] "User" refers to an individual who uses the system.

[1489] "Initial information" refers to basic information such as grade, favorite subjects, weak subjects, and interests that users enter when they start using the system.

[1490] "Generative artificial intelligence means" refers to an artificial intelligence that generates an optimal learning plan based on the user's initial information.

[1491] The term "study plan" refers to specific study content and schedules generated by the artificial intelligence generating means to enable the user to study effectively.

[1492] "Progress" refers to information that indicates the degree of progress in learning, such as the level of understanding or interest at that point in time, which is input by the user as they proceed with their learning.

[1493] "Optimization" refers to updating the study plan to the most effective content for the user based on the user's progress.

[1494] "Online Community" means a virtual community where Users can interact with other Users who share their interests.

[1495] "Counselor or AI teacher" refers to a human counselor or AI that responds to a user's consultation and provides appropriate advice and support.

[1496] "Terminal" refers to electronic devices such as smartphones, tablets, and personal computers that users use to operate the system.

[1497] "Server" refers to the central management system that manages users' initial information and progress information, and generates and optimizes learning plans using generative AI models.

[1498] MODE FOR CARRYING OUT THE INVENTION

[1499] System Overview

[1500] This invention is a system that provides learning opportunities and social connections to children who are not attending school. The system collects initial information about the user, uses a generative AI model to provide an optimal learning plan, and provides a means to participate in online communities and counseling functions. The entire system consists of three main components: the user, the device, and the server.

[1501] Hardware and Software

[1502] Terminal: Refers to an electronic device such as a smartphone, tablet, or PC that a user uses to access the system.

[1503] Server: A central management system that manages initial information and progress information from users and generates and optimizes learning plans using generative AI models. It uses a cloud-based data processing server (e.g., AWS, Google Cloud Platform).

[1504] Generative AI model: An AI model that generates and optimizes a learning plan based on the user's initial information and progress information. For example, OpenAI GPT-4 is used.

[1505] Gathering initial information

[1506] User: Starts the application and enters initial information such as grade, favorite subjects, favorite subjects, and interests.

[1507] Terminal: Collects information entered by the user and sends it to the server in the form of an HTTP request.

[1508] Server: Stores the received user information in a database. Generates prompts from the generative AI model and asks it to generate an optimal learning plan. Sends the generated learning plan to the device.

[1509] View your learning plan

[1510] Terminal: Displays the learning plan received from the server to the user.

[1511] As a concrete example, a second-year junior high school student inputs, "I'm not good at math, but I'm interested in science," and the generative AI model generates a study plan such as, "Basic math problems, 30 minutes a day, watching science experiment videos once a week."

[1512] Learning progress and progress records

[1513] User: Study based on the presented study plan. After completing the study, enter the progress (for example, level of understanding and interest) into the application.

[1514] Terminal: Collects the progress information entered by the user and sends it to the server in the form of an HTTP request.

[1515] Server: Stores the received progress information in a database, sends prompts to the generative AI model to optimize the new learning plan, receives the new learning plan, and sends it to the device.

[1516] Terminal: Displays the newly received lesson plan to the user.

[1517] As a concrete example, based on the progress of "80% understanding of mathematics, 95% interest in science," the generative AI generates a new optimized learning plan.

[1518] Participating in online communities

[1519] Users: Search for online community groups that interest them within the application and submit a request to join.

[1520] Terminal: Sends a request to the server to join the community group selected by the user.

[1521] Server: Based on the received request, recommends groups based on the user's interests and approves their participation.

[1522] Terminal: Displays community information to users and helps them interact with other members.

[1523] As a specific example, if a user becomes interested in a "plant growing group" and sends a request to join, the server will approve the request and the terminal will display community information.

[1524] Use of consultation function

[1525] User: Enter the consultation details within the application and submit.

[1526] Terminal: Collects consultation details and sends them to the server.

[1527] Server: Assigns the received consultation content to the appropriate counselor or AI teacher, collects the reply content, and sends it to the device.

[1528] Terminal: Display the reply to the user.

[1529] As a specific example, a user inputs "advice about career paths," the server assigns it to a counselor, and the reply is sent to the terminal and displayed to the user.

[1530] Prompt Sentence Examples

[1531] "Please create the optimal study plan based on the initial information of the new user. The student is in the second year of junior high school, is not good at math, and is interested in science."

[1532] "Please optimize your study plan based on the progress information below. Your math comprehension is 80% and your science interest is 95%."

[1533] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1534] Step 1: User launches application and enters initial information

[1535] Input: Grade, strong subjects, weak subjects, interests

[1536] Output: User's initial information

[1537] Specific operation: The user enters initial information such as grade level, favorite subjects, weak subjects, and interests into the application's input form.

[1538] Step 2: The device sends initial information to the server

[1539] Input: Initial information entered by the user

[1540] Output: HTTP request format data

[1541] Specific operation: The terminal collects the user's initial information and sends it to the server as an HTTP request.

[1542] Step 3: The server saves the initial information and sends a prompt to the generative AI model.

[1543] Input: Initial information in the HTTP request format

[1544] Output: Saved user information, prompts to send to the generative AI model

[1545] Specific operation: The server stores the received user information in a database, then generates and sends prompts to the generative AI model to generate an optimal learning plan.

[1546] Step 4: The generative AI model generates a learning plan and sends it to the server

[1547] Input: prompt statement

[1548] Output: The generated learning plan

[1549] Specific operation: The generative AI model generates a learning plan based on the prompt sentence and sends the generated results back to the server.

[1550] Step 5: The server sends the generated lesson plan to the device.

[1551] Input: Generated lesson plan

[1552] Output: Learning plan in HTTP response format

[1553] Specific operation: The server sends the learning plan received from the generative AI model to the terminal as an HTTP response.

[1554] Step 6: The device displays the lesson plan to the user

[1555] Input: Lesson plan in HTTP response format

[1556] Output: A displayed lesson plan

[1557] Specific operation: The device displays the received learning plan in an easy-to-understand manner to the user. Specifically, it displays a plan such as "30 minutes of basic math problems every day and watching science experiment videos once a week."

[1558] Step 7: User progresses and enters progress information

[1559] Input: Progress information (level of understanding, interest, etc.)

[1560] Output: User progress information

[1561] Specific operation: The user studies according to the presented study plan and enters their study progress into the application's input form.

[1562] Step 8: The device sends progress information to the server

[1563] Input: Progress information entered by the user

[1564] Output: Progress information in the form of an HTTP request

[1565] Specific operation: The device collects the user's progress information and sends it to the server as an HTTP request.

[1566] Step 9: The server saves the progress and sends a new prompt to the generative AI model.

[1567] Input: Progress information in HTTP request format

[1568] Output: Saved progress information, new prompt text

[1569] Specific operation: The server stores the received progress information in the database, then generates and sends a prompt to the generative AI model to generate a new learning plan.

[1570] Step 10: The generative AI model generates a new learning plan and sends it to the server.

[1571] Input: New prompt text

[1572] Output: The new learning plan generated.

[1573] Specific operation: The generative AI model generates a new learning plan based on progress information and sends the generated results back to the server.

[1574] Step 11: The server sends the new lesson plan to the device.

[1575] Input: New lesson plan

[1576] Output: The new learning plan in the form of an HTTP response.

[1577] Specific operation: The server sends the new learning plan received from the generative AI model to the terminal as an HTTP response.

[1578] Step 12: The device displays the new lesson plan to the user.

[1579] Input: A new learning plan in the form of an HTTP response

[1580] Output: The new lesson plan displayed.

[1581] Specific operation: The device displays the new learning plan to the user in an easy-to-understand manner.

[1582] Step 13: User finds online community and submits request to join

[1583] Input: Community name of interest

[1584] Output: Join request

[1585] What happens: A user searches for an online community group of interest within the application, selects one, and submits a request to join.

[1586] Step 14: The device sends a join request to the server

[1587] Input: Join request

[1588] Output: Join request in HTTP request format

[1589] Specific operation: The terminal collects the user's participation request and sends it to the server as an HTTP request.

[1590] Step 15: The server processes the join request and sends the online community information

[1591] Input: Join request

[1592] Output: Online community information

[1593] Specific operation: The server receives the participation request, recommends communities based on the user's interests, and sends information approving participation to the device.

[1594] Step 16: The terminal displays online community information to the user

[1595] Input: Online community information

[1596] Output: Displayed online community information

[1597] Specific operation: The device displays online community information to the user and supports interaction with other members.

[1598] (Application example 1)

[1599] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1600] Conventional learning support systems have struggled to provide optimal learning plans and connections to society for children who are not attending school. Furthermore, they lacked the ability to manage learning progress and dynamic responses to individual users' interests and needs. Furthermore, they lacked systems that integrated online communities and consultation functions, which led to students becoming isolated.

[1601] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1602] In this invention, the server includes means for collecting initial information from a user, artificial intelligence generating means for generating a study plan based on the initial information, means for presenting the study plan generated by the artificial intelligence generating means to the user, means for collecting the user's study progress, means for optimizing the study plan based on the study progress, means for presenting the optimized study plan to the user, means for supporting participation in an online community based on the user's interests, means for consulting with a counselor or an artificial intelligence teacher, and means for being installed as a smartphone application in a virtual store. This makes it possible to provide children who are not attending school with an individualized, optimal study plan, an online community, and consultation functions in an integrated manner.

[1603] "User information" refers to initial setting information about an individual user, such as grade, favorite subjects, weak subjects, and interests.

[1604] A "study plan" is a study schedule or curriculum created by the artificial intelligence based on user information.

[1605] "Generative AI" is an AI model that creates optimal learning plans based on user information.

[1606] "Progress" refers to information that records the user's learning progress and achievement level.

[1607] "Optimization" refers to the process of modifying and adjusting the learning plan based on the user's progress and needs.

[1608] An "online community" is an online group or forum that users can join based on their interests.

[1609] A "counselor" is a professional consultant who provides advice to users and offers solutions.

[1610] An "artificial intelligence teacher" is an AI-based support system provided by generative artificial intelligence to respond to user inquiries.

[1611] A "virtual store" is a virtual store that provides the same services online as a physical store.

[1612] A "smartphone application" is a software application that runs on a smartphone and allows users to use the system.

[1613] This invention is a system that provides optimal learning opportunities and social connections to children who are not attending school. This system is installed as a smartphone application in a virtual store, and is configured to allow users to access personalized learning plans, online communities, and even counseling services.

[1614] 1. System Configuration

[1615] Hardware

[1616] Smartphone: A device that allows users to access the system.

[1617] Server: Processes data, runs generative AI models, and manages databases.

[1618] software

[1619] Smartphone application: An app for collecting user information, displaying learning plans, recording progress, participating in online communities, and sending and receiving consultations.

[1620] Generative AI model: An AI model for creating learning plans based on user information.

[1621] 2. Operational flow

[1622] User registration and initial settings

[1623] The user launches the smartphone application and enters initial information such as their grade level, favorite subjects, weak subjects, and interests. The smartphone then sends this information to the server. The server stores the received information in a database and uses a generative AI model to create an optimal initial learning plan. The created learning plan is sent to the user via the smartphone application and displayed.

[1624] Learning progress and progress records

[1625] The user studies according to the generated study plan and inputs their progress into the smartphone application. The smartphone then sends the input progress to the server. The server then uses the generative AI model to optimize the study plan based on the received progress information and creates a new study plan. This plan is also sent to the user via the smartphone application and displayed.

[1626] Participating in online communities

[1627] Users search for community groups that interest them and send a request to join from their smartphone application. The server receives the request, recommends the most suitable group based on the user's interests, and approves their participation. The approval result and community information are notified to the smartphone, allowing users to interact with other members.

[1628] Use of consultation function

[1629] The user inputs the consultation content and sends it to the server via the smartphone application. The server receives the consultation content and assigns it to an appropriate counselor or AI model. The counselor or AI model then creates a reply and sends it to the smartphone application via the server, where it is displayed to the user.

[1630] 3. Specific Examples

[1631] Prompt Sentence Examples

[1632] For example, a second-year junior high school student launches the application and enters, "I'm not good at math, but I'm interested in science." An example of a study plan generated based on this user information might be, "Basic math problems, 30 minutes a day, watching science experiment videos once a week."

[1633] User initial information:

[1634] Grade: 2nd year of junior high school

[1635] Favorite subject: Science

[1636] Weak subject: Mathematics

[1637] Interests: Science experiments

[1638] Generate an optimal weekly study plan for this user.

[1639] In this way, this system provides children who are not attending school with an optimal, individualized learning plan, an online community, and consultation functions in an integrated manner, realizing learning opportunities and connections to society.

[1640] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1641] Step 1:

[1642] The user launches the smartphone application and enters their initial information (grade, favorite subjects, weak subjects, interests, etc.). The device collects this information and sends it to the server. The entered data includes grade, favorite / weak subjects, and interests, and a process is performed to transfer this data to the server. Specifically, when the submit button on the input form is pressed, an API request is sent.

[1643] Step 2:

[1644] The server stores the received initial information in a database. The stored data includes the user ID and corresponding grade, favorite subjects, weak subjects, and interests. The server inputs the stored data into the generative AI model and requests it to generate an optimal learning plan. Specific operations include a database insert query and prompt generation for the AI ​​model.

[1645] Step 3:

[1646] The generative AI model generates a study plan based on the input user information. An example of the generated study plan might be "basic math problems for 30 minutes every day, watching science experiment videos once a week." This plan is sent to the server, which then transmits it to the smartphone application. Specific operations include the AI ​​model's calculation process and the generation of API responses.

[1647] Step 4:

[1648] The device displays the learning plan received from the server to the user. The displayed learning plan includes specific assignments and a learning schedule, and the user follows this to progress with their studies. Specifically, the device performs a process of rendering the received data on the screen.

[1649] Step 5:

[1650] Users record their learning progress and enter it into a smartphone application. The device collects this progress data and sends it to the server. The progress data entered includes the level of understanding and achievement, and this is then transferred to the server. Specifically, an API request is sent when the send button on the progress input form is pressed.

[1651] Step 6:

[1652] The server stores the received progress information in a database and inputs it into the generative AI model. The generative AI model optimizes the learning plan based on the latest progress information. The optimized plan is sent to the server and then distributed to the smartphone application. Specific operations include issuing a database update query and generating a prompt for the AI ​​model.

[1653] Step 7:

[1654] A user searches for online communities that interest them and submits a request to join. The device then sends this request to the server, which then recommends the most suitable group based on the user's interests and approves their participation. Specific operations include sending a request and receiving an API response approving their participation.

[1655] Step 8:

[1656] The user enters the consultation details and sends them from the smartphone application to the server. The server receives the consultation details and assigns them to an appropriate counselor or AI model. The reply is sent to the smartphone application via the server and displayed to the user. Specific operations include sending the input form, generating an API response, and displaying the reply.

[1657] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1658] This invention is a system that provides learning opportunities and social connections to children who are not attending school. It further incorporates an emotion engine that recognizes the user's emotions to personalize the learning experience and provide emotional support. The system collects initial information about the user, uses generative AI to provide an optimal learning plan, and provides a means to participate in online communities and counseling functions. It also has the ability to recognize the user's emotions and adjust the learning plan and community participation support.

[1659] Program processing overview

[1660] The system of the present invention performs the following main processes.

[1661] 1. User registration and initial settings

[1662] User: The user starts the application and enters initial information such as their grade, favorite subjects, least favorite subjects, and interests.

[1663] Terminal: The terminal collects the user's input information and generates a data transmission request to the server.

[1664] Server: The server receives user information and stores it in a database. The generation AI creates an optimal initial learning plan for the user based on the initial information received.

[1665] 2. Generate a learning plan

[1666] Server: The generation AI generates a learning plan based on the user's initial information and sends it to the terminal.

[1667] Terminal: The terminal displays the received learning plan to the user.

[1668] 3. Learning progress and progress records

[1669] User: The user follows the presented learning plan and enters their progress into the learning application.

[1670] Terminal: The terminal collects the progress information entered by the user and generates a request to send to the server.

[1671] Server: The server receives the progress information and stores it in a database. The generation AI analyzes the progress information and optimizes the existing learning plan.

[1672] Server: Sends the optimized learning plan to the device.

[1673] Device: The device presents the optimized learning plan to the user.

[1674] 4. Participating in online communities

[1675] Users: Users can search for community groups that interest them and submit a request to join.

[1676] Terminal: The terminal sends a join request to the server.

[1677] Server: The server receives the join request, recommends appropriate community groups based on the user's interests, and approves the join.

[1678] Server: Sends community information to the terminal.

[1679] Terminal: The terminal displays community information to the user and facilitates interaction with other members.

[1680] 5. Use of the consultation function

[1681] User: The user enters the consultation content and submits it.

[1682] Terminal: The terminal generates a request to send the consultation content to the server.

[1683] Server: The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[1684] Server: Sends the reply from the counselor or AI teacher to the device.

[1685] Terminal: The terminal displays the reply from the counselor or AI teacher to the user.

[1686] 6. Use of Emotion Engines

[1687] Terminal: The terminal activates sensors (e.g., facial recognition camera, voice analysis, etc.) to recognize the user's emotions.

[1688] User: The user displays emotions such as facial expressions and voice on the input device while learning or consulting.

[1689] Terminal: The terminal collects emotion data and generates a request to send it to the server.

[1690] Server: The server receives the emotion data and the emotion engine analyzes it.

[1691] Emotional response: The emotion engine optimizes feedback on the user's study plan and consultation content based on the results of emotion analysis.

[1692] Terminal: Presents the user with a learning plan and consultation content optimized by the emotion engine.

[1693] Specific examples

[1694] An example of user registration and initial settings

[1695] 1. User: A 8th grade user launches the application and enters, "I'm not good at math, but I'm interested in science."

[1696] 2. Terminal: The terminal sends this to the server.

[1697] 3. Server: Based on the user information, the server uses a generative AI to generate an initial learning plan such as "basic math problems for 30 minutes every day, and watching science experiment videos once a week" and sends it to the device.

[1698] 4. Terminal: The terminal displays the lesson plan to the user.

[1699] An example of learning progress and progress record

[1700] 1. User: The user studies for a week and enters their progress (e.g., "Mathematics comprehension 80%, Science interest 95%) into the application.

[1701] 2. Terminal: The terminal sends progress information to the server.

[1702] 3. Server: The server receives the progress information, and the generation AI optimizes the learning plan based on this information and sends the new learning plan to the device.

[1703] 4. Device: The device presents the optimized learning plan to the user.

[1704] An example of using online communities

[1705] 1. User: A user becomes interested in the "Plant Growing Group" and sends a request to join.

[1706] 2. Terminal: The terminal sends a join request to the server.

[1707] 3. Server: The server approves participation based on the user's interests and sends community information to the terminal.

[1708] 4. Terminal: The terminal displays community information to the user and supports interaction with other members.

[1709] An example of using the emotion engine

[1710] 1. Device: The device captures the user's facial expressions with a camera and recognizes their emotions.

[1711] 2. Server: The server analyzes the emotional data and determines that the user is feeling stressed.

[1712] 3. Emotional response: The emotion engine optimizes learning content to temporarily ease user stress and enhances consultation functions.

[1713] 4. Terminal: Presents the user with a learning plan and consultation options tailored by the emotion engine.

[1714] In this way, the system provides individually optimized learning environments and psychological support to children who are not attending school.

[1715] The processing flow will be explained below.

[1716] Step 1:

[1717] User: The user launches the application and enters their own information (grade, favorite subjects, weak subjects, interests, etc.) on the registration screen.

[1718] Step 2:

[1719] Terminal: The terminal receives the user's input information and generates a data transmission request to the server.

[1720] Step 3:

[1721] Server: The server receives user information and stores it in a database. The generation AI creates an optimal initial learning plan for the user based on the initial information received.

[1722] Step 4:

[1723] Server: Sends the generated learning plan to the device.

[1724] Step 5:

[1725] Device: The device displays the received learning plan to the user and prompts them to start learning.

[1726] Step 6:

[1727] User: The user follows the presented learning plan and proceeds with their studies. They enter their daily learning progress (e.g., learning content, level of understanding, study time, etc.) into the learning application.

[1728] Step 7:

[1729] Terminal: The terminal collects the progress information entered by the user and generates a request to send to the server.

[1730] Step 8:

[1731] Server: The server receives the progress information and stores it in a database. The generation AI analyzes the progress information and optimizes the existing learning plan.

[1732] Step 9:

[1733] Server: Sends the optimized learning plan to the device.

[1734] Step 10:

[1735] Device: The device presents the optimized learning plan to the user.

[1736] Step 11:

[1737] Users: Users want to join an online community and search for groups that interest them.

[1738] Step 12:

[1739] User: A user sends a request to join a group they wish to join.

[1740] Step 13:

[1741] Terminal: The terminal sends a join request to the server.

[1742] Step 14:

[1743] Server: The server receives the join request, recommends appropriate community groups based on the user's interests, and approves the join.

[1744] Step 15:

[1745] Server: Sends community information to the terminal.

[1746] Step 16:

[1747] Terminal: The terminal displays community information to the user and facilitates interaction with other members.

[1748] Step 17:

[1749] User: The user enters into the application what they would like to discuss with the counselor or AI teacher.

[1750] Step 18:

[1751] Terminal: The terminal generates a request to send the consultation content to the server.

[1752] Step 19:

[1753] Server: The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[1754] Step 20:

[1755] Server: Sends the reply from the counselor or AI teacher to the device.

[1756] Step 21:

[1757] Terminal: The terminal displays the reply from the counselor or AI teacher to the user.

[1758] Step 22:

[1759] Terminal: The terminal activates sensors (e.g., facial recognition camera, voice analysis, etc.) to recognize the user's emotions.

[1760] Step 23:

[1761] User: The user displays emotions such as facial expressions and voice on the input device while learning or consulting.

[1762] Step 24:

[1763] Terminal: The terminal collects emotion data and generates a request to send it to the server.

[1764] Step 25:

[1765] Server: The server receives the emotion data and the emotion engine analyzes it.

[1766] Step 26:

[1767] Emotional response: The emotion engine optimizes feedback on the user's study plan and consultation content based on the results of emotion analysis.

[1768] Step 27:

[1769] Terminal: Presents the user with a learning plan and consultation content optimized by the emotion engine.

[1770] Through the above process, this system can provide individually optimized learning environments and psychological support to children who are not attending school.

[1771] Example 2

[1772] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1773] In modern society, the problem of children not attending school is extremely serious, with a lack of learning opportunities and social connections being major issues. Furthermore, these children require emotional support and require individualized learning plans and psychological assistance. However, existing education systems do not adequately create learning plans tailored to the needs of individual children or recognize and respond to their emotions. As a result, children may lose motivation to learn and become even more isolated.

[1774] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting initial information from a user; artificial intelligence generation means for generating a study plan based on the initial information; means for presenting the study plan generated by the artificial intelligence generation means to the user; means for collecting the user's learning progress; means for optimizing the study plan based on the learning progress; means for presenting the optimized study plan to the user; means for supporting participation in an online community based on the user's interests; means for consulting with a counselor or an artificial intelligence teacher; means including a sensor for recognizing the user's emotions; emotional response means for analyzing the emotional data and optimizing the study plan and consultation content; and means for presenting the optimized study plan and consultation content to the user based on the emotional response. This makes it possible to provide an individualized learning environment and psychological support to children who are not attending school, thereby improving their motivation to study and promoting their connections with society.

[1775] "User" refers to the learner who uses the system, specifically the entity who inputs initial information, follows a learning plan, and reports progress.

[1776] "Initial information" refers to basic data such as grade, favorite subjects, weak subjects, and interests that users enter when using the system.

[1777] "Study plan" refers to a schedule of learning content that is most suitable for a user, created by the generation AI based on the user's initial information.

[1778] "Generative AI means" refers to the AI ​​technology deployed to generate an optimal learning plan based on the user's initial information.

[1779] "Study progress status" refers to progress information entered by a user as a result of studying according to a study plan.

[1780] The "optimizing means" refers to a means for adjusting the study plan based on the user's study progress and generating a new optimized study plan.

[1781] An "online community" refers to a virtual group of users who share common interests and who can interact with each other and share information.

[1782] "Counselor or AI teacher" refers to a real-life expert or AI (artificial intelligence) technology that provides appropriate advice and support in response to the consultation content entered by the user.

[1783] A "sensor" is a device used to recognize a user's emotions, and includes facial recognition cameras and voice analysis devices.

[1784] "Emotion data" refers to information about emotions obtained from the user's facial expressions, voice, etc. collected by sensors.

[1785] "Emotional response" refers to the process of analyzing emotional data and optimizing the user's study plan or consultation content based on the results.

[1786] MODE FOR CARRYING OUT THE INVENTION

[1787] The present invention is a system that provides learning opportunities and social connections to children who are not attending school, and by further incorporating an emotion engine that recognizes the user's emotions, it personalizes the learning experience and provides emotional support.

[1788] This system performs various data processing using the following hardware and software:

[1789] Hardware: smartphones, tablets, laptops, facial recognition cameras, voice analysis devices

[1790] Software: Applications that collect user information, generative AI (e.g., GPT-4), database management systems, online community platforms, sentiment analysis engines

[1791] The specific processing flow is shown below.

[1792] User registration and initial settings

[1793] The user starts the learning application and enters initial information such as their grade, favorite subjects, weak subjects, and interests.

[1794] Example: A second-year junior high school student enters, "I'm not good at math, but I'm interested in science."

[1795] The terminal collects the input information and generates a data transmission request to the server.

[1796] The server receives the user's initial information and stores it in a database.

[1797] Example: The server receives user information and records it in a database as "user ID, grade, favorite subjects, weak subjects, and interests."

[1798] The server uses generative AI to create an optimal learning plan based on the user's initial information.

[1799] Example: Generative AI generates a study plan such as "Basic math problems for 30 minutes every day, and watch science experiment videos once a week."

[1800] The server sends the generated learning plan to the terminal.

[1801] The terminal displays the received learning plan to the user.

[1802] An example of learning progress and progress record

[1803] The user studies according to the presented study plan and enters their progress into the application.

[1804] Example: A user enters into an application, "I understand math 80% and I'm interested in science 95%."

[1805] The terminal collects the progress information and generates a transmission request to the server.

[1806] The server receives the progress information and stores it in a database.

[1807] The server uses generated AI to analyze progress information and optimize learning plans.

[1808] Example: A generative AI adjusts next week's math problem set based on progress results.

[1809] The server sends the optimized learning plan to the device.

[1810] The device displays the optimized study plan to the user.

[1811] An example of participating in an online community

[1812] Users search for community groups that interest them and submit a request to join.

[1813] Example: A user searches for "plant growing group" and sends a request to join.

[1814] The device sends a join request to the server.

[1815] The server receives the request, recommends appropriate community groups, and approves participation.

[1816] The server transmits the community information to the terminal.

[1817] The terminal displays community information to the user and encourages interaction with other members.

[1818] An example of using the consultation function

[1819] The user inputs the consultation content and sends it.

[1820] Example: A user types, "Studying is hard."

[1821] The terminal generates a request to transmit the consultation content to the server.

[1822] The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[1823] The server sends the reply from the counselor or AI teacher to the device.

[1824] The device displays the reply from the counselor or AI teacher to the user.

[1825] An example of using the emotion engine

[1826] The device activates sensors (facial recognition camera, voice analysis, etc.) to recognize the user's emotions.

[1827] Example: The device captures the user's facial expressions through the camera.

[1828] The user indicates emotions (e.g., facial expressions or voice) using an input device.

[1829] The device collects emotion data and generates a transmission request to the server.

[1830] The server receives the emotion data and the emotion engine analyzes it.

[1831] Example: Determining that a user is feeling stressed based on emotional data.

[1832] Emotional response: The emotion engine optimizes learning plans and consultation content based on the analysis results.

[1833] Example: Reduce the learning load or add a refresh feature.

[1834] The device presents the user with optimized learning plans and consultation contents.

[1835] This makes it possible for the system to provide children who are not attending school with an individualized learning environment and psychological support, thereby increasing their motivation to learn and promoting connections with society.

[1836] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1837] Program processing flow

[1838] User registration and initial settings

[1839] Step 1:

[1840] The user launches an application.

[1841] Specific operation: A second-year junior high school student launches the app on his smartphone and the initial startup screen is displayed.

[1842] Input: User action

[1843] Output: Application Launch

[1844] Step 2:

[1845] The user enters their initial information.

[1846] Specific operation: The user inputs "grade: second year of junior high school, favorite subject: science, least favorite subject: mathematics, interest: natural science."

[1847] Input: Grade, favorite subjects, weak subjects, interests

[1848] Output: Initial information data

[1849] Step 3:

[1850] The terminal collects the input information and generates a data transmission request to the server.

[1851] Specific operation: The terminal compiles the user's initial information and sends it to the server.

[1852] Input: Initial information data

[1853] Output: Request sent to server

[1854] Step 4:

[1855] The server receives the user's initial information and stores it in a database.

[1856] Specific operation: The server records the received user information in a database.

[1857] Input: Initial information data

[1858] Output: Save to database

[1859] Step 5:

[1860] The server uses generative AI to create an optimal learning plan based on the user's initial information.

[1861] Specific operation: The generative AI generates a study plan such as "basic math problems for 30 minutes every day, and watching science experiment videos once a week."

[1862] Input: Initial information data

[1863] Output: Learning plan

[1864] Step 6:

[1865] The server sends the generated learning plan to the terminal.

[1866] Specific operation: The server transfers the newly generated learning plan to the user's device.

[1867] Input:Study plan

[1868] Output: Data sent to the terminal

[1869] Step 7:

[1870] The terminal displays the received learning plan to the user.

[1871] Specific behavior: The device displays the lesson plan within the app and asks the user to confirm it.

[1872] Input: Learning plan data

[1873] Output: What is displayed to the user

[1874] Learning progress and progress records

[1875] Step 1:

[1876] The user proceeds with their studies according to the presented study plan.

[1877] Specific action: The user studies math for 30 minutes every day.

[1878] Input:Study plan

[1879] Output: Learning progress

[1880] Step 2:

[1881] The user enters their learning progress into the application.

[1882] Specific operation: The user reports to the app that "math comprehension level is 80% and science interest level is 95%."

[1883] Input: Progress information

[1884] Output: Progress data

[1885] Step 3:

[1886] The terminal collects the progress information and generates a transmission request to the server.

[1887] Specific operation: The device compiles progress data and sends it to the server.

[1888] Input: Progress data

[1889] Output: Request sent to server

[1890] Step 4:

[1891] The server receives the progress information and stores it in a database.

[1892] What happens: The server adds the new progress data to the database.

[1893] Input: Progress data

[1894] Output: Save to database

[1895] Step 5:

[1896] The server uses generated AI to analyze progress information and optimize learning plans.

[1897] Specific operation: The generative AI generates a new problem set based on the user's level of mathematical understanding.

[1898] Input: Progress data

[1899] Output: Optimized learning plan

[1900] Step 6:

[1901] The server sends the optimized learning plan to the device.

[1902] Specific operation: The optimized plan is sent from the server to the user's terminal.

[1903] Input: Optimized Study Plan

[1904] Output: Data sent to the terminal

[1905] Step 7:

[1906] The device presents the optimized study plan to the user.

[1907] Specific behavior: The device displays the new lesson plan to the user.

[1908] Input: Optimized lesson plan data

[1909] Output: What is displayed to the user

[1910] Participating in online communities

[1911] Step 1:

[1912] Users search for community groups that interest them and submit a request to join.

[1913] Specific behavior: A user searches for "plant growing group" and sends a request to join.

[1914] Input: Interest words

[1915] Output: Join request data

[1916] Step 2:

[1917] The device sends a join request to the server.

[1918] Specific operation: The terminal sends a request to the server.

[1919] Input: Join request data

[1920] Output: Data sent to the server

[1921] Step 3:

[1922] The server receives the request, recommends appropriate community groups, and approves participation.

[1923] Specific Operation: The server approves the user to join a plant growing group based on their interests.

[1924] Input: Join request data

[1925] Output: Approval data and recommendation data

[1926] Step 4:

[1927] The server transmits the community information to the terminal.

[1928] Specific operation: Community information is sent from the server to the terminal.

[1929] Input: Approval data and recommendation data

[1930] Output: Data sent to the terminal

[1931] Step 5:

[1932] The terminal displays community information to the user and encourages interaction with other members.

[1933] Specific operation: The device displays the latest community information, message boards, etc. to the user.

[1934] Input: Community information data

[1935] Output: What is displayed to the user

[1936] Use of consultation function

[1937] Step 1:

[1938] The user inputs the consultation content and sends it.

[1939] Specific action: The user enters "Studying is hard" into the app.

[1940] Input: Consultation details

[1941] Output: Consultation data

[1942] Step 2:

[1943] The terminal generates a request to transmit the consultation content to the server.

[1944] Specific operation: The device prepares to send the consultation content to the server.

[1945] Input: Consultation data

[1946] Output: Request sent to server

[1947] Step 3:

[1948] The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[1949] Specific operation: The server transfers the consultation content to a psychological counselor.

[1950] Input: Consultation data

[1951] Output: Counselor assignment data

[1952] Step 4:

[1953] The server sends the reply from the counselor or AI teacher to the device.

[1954] Specific operation: The reply from the counselor is sent to the terminal via the server.

[1955] Input: Reply content data

[1956] Output: Data sent to the terminal

[1957] Step 5:

[1958] The device displays the reply from the counselor or AI teacher to the user.

[1959] Specific operation: The device displays advice from the counselor.

[1960] Input: Reply content data

[1961] Output: What is displayed to the user

[1962] Use of emotion engine

[1963] Step 1:

[1964] The device activates sensors (facial recognition camera, voice analysis, etc.) to recognize the user's emotions.

[1965] Specific operation: The device captures the user's facial expression through the built-in camera.

[1966] Input: Sensor activation request

[1967] Output: Sensor activation

[1968] Step 2:

[1969] The user indicates emotions such as facial expressions and voice on the input device.

[1970] Specific action: The user smiles or frowns in front of the camera.

[1971] Input: Real-time facial and voice data

[1972] Output: Emotion data

[1973] Step 3:

[1974] The device collects emotion data and generates a transmission request to the server.

[1975] Specific operation: The device prepares to send the collected emotion data to the server.

[1976] Input: Emotion data

[1977] Output: Request sent to server

[1978] Step 4:

[1979] The server receives the emotion data and the emotion engine analyzes it.

[1980] Specific operation: The server analyzes the user's stress level from facial expression data.

[1981] Input: Emotion data

[1982] Output: Analysis results

[1983] Step 5:

[1984] Emotional response: The emotion engine optimizes learning plans and consultation content based on the analysis results.

[1985] Specific behavior: The emotion engine reduces the learning load and provides a refresh function.

[1986] Input: Analysis results

[1987] Output: Optimized study plan and consultation content

[1988] Step 6:

[1989] The device presents the user with optimized learning plans and consultation contents.

[1990] Specific operation: The device displays the new learning plan and consultation details to the user.

[1991] Input: Optimized learning plan and consultation data

[1992] Output: What is displayed to the user

[1993] (Application example 2)

[1994] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1995] Existing online learning systems for school-refusing children often lack individualization and emotional support. They also lack the ability to provide effective social connections, such as learning plans based on users' interests and online communities. Furthermore, they lack the ability to optimize learning plans and counseling functions that incorporate users' emotional data, resulting in insufficient psychological support.

[1996] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting initial information from the user; artificial intelligence generating means for generating a study plan based on the initial information; means for presenting the study plan generated by the artificial intelligence generating means to the user; means for collecting the user's study progress; means for optimizing the study plan based on the study progress; means for presenting the optimized study plan to the user; means for supporting participation in an online community based on the user's interests; means for consulting with a counselor or an artificial intelligence teacher; means for collecting user emotion data using an emotion recognition sensor; and emotion engine means for analyzing the emotion data and optimizing the study plan or consultation content. This makes it possible to provide an individualized study plan, provide emotional support, and strengthen social connections through the online community.

[1997] "Initial information" is basic information for generating a study plan, such as the user's grade, favorite subjects, weak subjects, and interests.

[1998] The "generative artificial intelligence means" is an artificial intelligence technology for generating an optimal learning plan based on initial information provided by the user.

[1999] A "study plan" is a plan that specifically shows how a user will actually proceed with their studies, and its contents are created by a generative artificial intelligence means.

[2000] An "emotion recognition sensor" is a sensor device that recognizes a user's facial expressions and voice and detects their emotional state.

[2001] The "emotion engine means" is a technology for analyzing the user's emotional data acquired from the emotion recognition sensor and optimizing the study plan and counseling based on the data.

[2002] An "online community" is a virtual group that users can join based on common interests.

[2003] "Counselor or AI teacher" refers to a counseling function using a real counselor or AI that provides support based on the user's consultation content.

[2004] Generating a Program

[2005] The system for implementing this invention is composed of a program that performs the following main processes: A system for providing individualized learning plans and emotional support to children who are not attending school.

[2006] Program processing overview

[2007] The system consists of several steps: user registration and initial setup, generating and optimizing a learning plan, recording learning progress and progress, participating in an online community, counseling functions, and using an emotion engine.

[2008] Hardware and Software Used

[2009] Smartphone: A device on which users install and use applications.

[2010] Flask: A server-side web framework.

[2011] Generative AI models: For example, OpenAI's GPT-3.

[2012] Emotion engine: Libraries and services used for user facial expression recognition and voice analysis (e.g., Google Cloud Vision).

[2013] Program processing explanation

[2014] 1. Collecting initial user information

[2015] The user enters initial information such as grade level, favorite subjects, weak subjects, and interests through a smartphone application. This information is then sent from the smartphone to the server.

[2016] 2. Generate a learning plan

[2017] The server uses a generative AI model (e.g., GPT-3) to generate an optimal learning plan based on the received initial information. The generated learning plan is sent to the smartphone and presented to the user.

[2018] 3. Track and optimize your learning progress

[2019] As the user progresses through their studies, they input their progress into the application. The server receives the progress information and optimizes the study plan using a generative AI model. The optimized study plan is then presented to the user again.

[2020] 4. Participating in online communities

[2021] Users search for community groups that interest them and send a request to join. The server receives the request, recommends appropriate groups based on the user's interests, and approves their participation. Community information is sent to the smartphone and displayed to the user.

[2022] 5. Use of counseling functions

[2023] When a user inputs their consultation details, the details are sent to the server, which then assigns the consultation details to an appropriate counselor or AI teacher and sends the answer to the user.

[2024] 6. Use of Emotion Engines

[2025] The smartphone's emotion recognition sensor recognizes the user's facial expressions and voice and sends the data to a server, which then uses an emotion engine to analyze the emotion data and optimize the learning plan and counseling content.

[2026] Specific examples

[2027] Example of user registration and initial settings

[2028] The user launches the application and enters, "I'm in the second year of junior high school, I'm not good at math, and I'm interested in science." This information is sent to the server, and the generative AI model generates an initial study plan, such as "basic math problems, 30 minutes a day, watching science experiment videos once a week." The generated study plan is then displayed on the smartphone.

[2029] Examples of learning progress recording and optimization

[2030] The user studies for a week and enters progress information into the application, such as "Mathematics comprehension 80%, Science interest 95%." The server receives the progress information, and the generative AI model uses it to optimize the study plan. The new study plan is then presented on the smartphone.

[2031] Examples of prompt statements

[2032] Examples of prompts for generative AI models include:

[2033] "Create a customized learning plan for a grade 8 student who is struggling with mathematics but has a keen interest in science. The learning plan should include daily math practice for 30 minutes and weekly science experiment videos."

[2034] In this way, the present invention can provide a personalized learning environment and psychological support for children who are not attending school.

[2035] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2036] Step 1:

[2037] The user starts the smartphone application and inputs initial information such as grade level, favorite subjects, weak subjects, and interests. The input information is sent from the device to the server. This input allows the server to understand the user's learning needs and collect initial setting data.

[2038] Step 2:

[2039] The server receives the initial information and inputs the prompt to the generative AI model (e.g., GPT-3). Based on this prompt, the generative AI model generates an optimal learning plan for the user. For example, the prompt might be, "Create a customized learning plan for a grade 8 student who is struggling with mathematics but has a keen interest in science. The learning plan should include daily math practice for 30 minutes and weekly science experiment videos." The server receives the learning plan obtained from the generative AI model and sends it to the device.

[2040] Step 3:

[2041] The terminal presents the optimal study plan sent from the server to the user, who then begins studying according to the presented study plan.

[2042] Step 4:

[2043] As the user progresses through their studies, they input their progress (e.g., level of understanding, interest, etc.) into the application. This progress information is sent from the device to the server, which uses the progress information as input data to provide the server with the basis for determining how the learning plan needs to be adjusted.

[2044] Step 5:

[2045] The server receives the progress information and uses a generative AI model to optimize the current lesson plan. Based on the prompt, the generative AI model generates a new lesson plan that reflects the progress. For example, the lesson plan is updated based on the progress information: "Math comprehension 80%, Science interest 95%." The server then sends the generated optimized lesson plan to the device.

[2046] Step 6:

[2047] The device presents the optimized study plan to the user and allows the user to continue studying according to the new study plan, so that the user can always use a study plan optimized to suit their own progress.

[2048] Step 7:

[2049] The user searches for an online community that interests them within the application and submits a request to join. The device then sends this request to the server. The input data is the user's interests, and the server then recommends appropriate online communities based on this information.

[2050] Step 8:

[2051] The server receives the participation request, recommends appropriate online communities based on the user's interests, generates recommendation information, approves the user's participation, and sends community information to the terminal.

[2052] Step 9:

[2053] The device displays online community information sent from the server to the user and allows the user to interact with other members. Users can receive support and exchange information within groups with shared interests.

[2054] Step 10:

[2055] The user inputs the details of their consultation and sends them from their device to the server via the application. The input data is the consultation details, and the server uses this information to obtain data for assigning the consultation to an appropriate counselor or AI teacher.

[2056] Step 11:

[2057] The server receives the consultation content, assigns it to an appropriate counselor or AI teacher, and then generates a reply and sends it to the device. Based on the reply, the server provides the support the user needs.

[2058] Step 12:

[2059] The device displays the reply from the counselor or AI teacher sent from the server to the user, allowing the user to receive the counseling support they need in a timely manner.

[2060] Step 13:

[2061] During learning or consultation, the smartphone's emotion recognition sensor collects the user's facial expressions and voice. This emotion data is sent from the device to the server. The input data is the user's emotional state, and the server obtains data for emotion analysis based on this.

[2062] Step 14:

[2063] The server receives the emotion data and analyzes it using an emotion engine. Based on the analysis results, it optimizes learning plans and counseling. It generates prompts based on the emotion data and creates optimal feedback using a generative AI model.

[2064] Step 15:

[2065] The server sends optimized learning plans and consultation details to the device, which then presents them to the user, allowing them to receive optimal support tailored to their emotional state.

[2066] 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.

[2067] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[2068] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[2069] [Fourth embodiment]

[2070] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2071] 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.

[2072] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[2073] 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.

[2074] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[2075] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[2076] 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.

[2077] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[2078] 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.

[2079] 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 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.

[2080] 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.

[2081] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[2082] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2083] This invention is a system that provides learning opportunities and social connections to children who are not attending school. The system collects initial information about the user, uses generative AI to provide an optimal learning plan, and provides a means to participate in online communities and counseling functions.

[2084] Program processing overview

[2085] The system of the present invention performs the following main processes.

[2086] 1. User registration and initial settings

[2087] User: The user starts the application and enters initial information such as their grade, favorite subjects, least favorite subjects, and interests.

[2088] Terminal: The terminal collects the information entered by the user and generates a request to send the data to the server.

[2089] Server: The server receives the request, stores the user information in a database, and the generative AI creates an optimal initial learning plan.

[2090] 2. Generate a learning plan

[2091] Server: The generation AI generates a learning plan based on the user's initial information and sends it to the terminal.

[2092] Terminal: The terminal displays the received learning plan to the user.

[2093] 3. Learning progress and progress records

[2094] User: The user follows the presented learning plan and enters their progress into the learning application.

[2095] Terminal: The terminal collects the progress input by the user and sends it to the server.

[2096] Server: The server receives the progress information, the generation AI optimizes the learning plan, and sends the new learning plan to the device.

[2097] 4. Participating in online communities

[2098] Users: Users can search for community groups that interest them and submit a request to join.

[2099] Terminal: The terminal sends a join request to the server.

[2100] Server: The server receives the request, recommends groups based on the user's interests, and approves their participation.

[2101] 5. Use of the consultation function

[2102] User: The user enters the consultation content and submits it.

[2103] Terminal: The terminal sends the consultation content to the server.

[2104] Server: The server receives the consultation content, assigns it to an appropriate counselor or AI teacher, and sends the reply content to the terminal and displays it to the user.

[2105] Specific examples

[2106] An example of user registration and initial settings

[2107] 1. User: A 8th grade user launches the application and enters, "I'm not good at math, but I'm interested in science."

[2108] 2. Terminal: The terminal sends this to the server.

[2109] 3. Server: Based on the user information, the server uses a generative AI to generate an initial learning plan such as "basic math problems for 30 minutes every day, and watching science experiment videos once a week" and sends it to the device.

[2110] 4. Terminal: The terminal displays the lesson plan to the user.

[2111] An example of learning progress and progress record

[2112] 1. User: The user studies for a week and enters their progress (e.g., "Mathematics comprehension 80%, Science interest 95%) into the application.

[2113] 2. Terminal: The terminal sends progress information to the server.

[2114] 3. Server: The server receives the progress information, and the generation AI optimizes the learning plan based on this and sends the plan for the next week to the device.

[2115] 4. Device: The device presents the optimized learning plan to the user.

[2116] An example of using online communities

[2117] 1. User: A user becomes interested in the "Plant Growing Group" and sends a request to join.

[2118] 2. Terminal: The terminal sends a join request to the server.

[2119] 3. Server: The server approves participation based on the user's interests and sends community information to the terminal.

[2120] 4. Terminal: The terminal displays community information to the user and supports interaction with other members.

[2121] In this way, the system provides an optimal learning environment and social connections for children who are not attending school.

[2122] The processing flow will be explained below.

[2123] Step 1:

[2124] User: When the user starts the application for the first time, they enter their own information (grade, favorite subjects, least favorite subjects, interests, etc.) on the registration screen.

[2125] Step 2:

[2126] Terminal: The terminal receives the user's input information and generates a data transmission request to the server.

[2127] Step 3:

[2128] Server: The server receives user information and stores it in a database. The generation AI creates an optimal initial learning plan for the user based on the initial information received.

[2129] Step 4:

[2130] Server: Sends the generated learning plan to the device.

[2131] Step 5:

[2132] Device: The device displays the received learning plan to the user and prompts them to start learning.

[2133] Step 6:

[2134] User: The user follows the presented learning plan and proceeds with their studies. They enter their daily learning progress (e.g., learning content, level of understanding, study time, etc.) into the learning application.

[2135] Step 7:

[2136] Terminal: The terminal collects the progress information entered by the user and generates a request to send to the server.

[2137] Step 8:

[2138] Server: The server receives the progress information and stores it in a database. The generation AI analyzes the progress information and optimizes the existing learning plan.

[2139] Step 9:

[2140] Server: Sends the optimized learning plan to the device.

[2141] Step 10:

[2142] Device: The device presents the optimized learning plan to the user.

[2143] Step 11:

[2144] Users: Users want to join an online community and search for groups that interest them.

[2145] Step 12:

[2146] User: A user sends a request to join a group they wish to join.

[2147] Step 13:

[2148] Terminal: The terminal sends a join request to the server.

[2149] Step 14:

[2150] Server: The server receives the join request, recommends appropriate community groups based on the user's interests, and approves the join.

[2151] Step 15:

[2152] Server: Sends community information to the terminal.

[2153] Step 16:

[2154] Terminal: The terminal displays community information to the user and facilitates interaction with other members.

[2155] Step 17:

[2156] User: The user enters into the application what they would like to discuss with the counselor or AI teacher.

[2157] Step 18:

[2158] Terminal: The terminal generates a request to send the consultation content to the server.

[2159] Step 19:

[2160] Server: The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[2161] Step 20:

[2162] Server: Sends the reply from the counselor or AI teacher to the device.

[2163] Step 21:

[2164] Terminal: The terminal displays the reply from the counselor or AI teacher to the user.

[2165] In this way, the system provides learning plan generation and optimization, online community participation, and consultation functions according to user needs.

[2166] Example 1

[2167] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2168] In the modern education system, it is difficult for children who do not attend school to obtain an appropriate learning environment and social connections. In particular, it is difficult for children who do not attend school to create individual learning plans that correspond to their own learning progress, and there is insufficient support to maintain motivation for learning. Furthermore, there are challenges in providing children with opportunities to participate in communities to avoid social isolation and an environment where they can seek advice.

[2169] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2170] In this invention, the server includes means for collecting initial information from a user, artificial intelligence generating means for generating a study plan based on the initial information, means for presenting the study plan generated by the artificial intelligence generating means to the user, means for collecting the user's study progress, means for optimizing the study plan based on the study progress, means for presenting the optimized study plan to the user, means for supporting participation in an online community based on the user's interests, means for consulting with a counselor or an artificial intelligence teacher, means for transmitting the generated study plan to a terminal, and means for transmitting online community information to the terminal. This not only enables children who are not attending school to receive individually optimized study plans and have them adjusted according to their progress, but also provides an environment in which they can connect with society through the online community and receive appropriate consultation.

[2171] "User" refers to an individual who uses the system.

[2172] "Initial information" refers to basic information such as grade, favorite subjects, weak subjects, and interests that users enter when they start using the system.

[2173] "Generative artificial intelligence means" refers to an artificial intelligence that generates an optimal learning plan based on the user's initial information.

[2174] The term "study plan" refers to specific study content and schedules generated by the artificial intelligence generating means to enable the user to study effectively.

[2175] "Progress" refers to information that indicates the degree of progress in learning, such as the level of understanding or interest at that point in time, which is input by the user as they proceed with their learning.

[2176] "Optimization" refers to updating the study plan to the most effective content for the user based on the user's progress.

[2177] "Online Community" means a virtual community where Users can interact with other Users who share their interests.

[2178] "Counselor or AI teacher" refers to a human counselor or AI that responds to a user's consultation and provides appropriate advice and support.

[2179] "Terminal" refers to electronic devices such as smartphones, tablets, and personal computers that users use to operate the system.

[2180] "Server" refers to the central management system that manages users' initial information and progress information, and generates and optimizes learning plans using generative AI models.

[2181] MODE FOR CARRYING OUT THE INVENTION

[2182] System Overview

[2183] This invention is a system that provides learning opportunities and social connections to children who are not attending school. The system collects initial information about the user, uses a generative AI model to provide an optimal learning plan, and provides a means to participate in online communities and counseling functions. The entire system consists of three main components: the user, the device, and the server.

[2184] Hardware and Software

[2185] Terminal: Refers to an electronic device such as a smartphone, tablet, or PC that a user uses to access the system.

[2186] Server: A central management system that manages initial information and progress information from users and generates and optimizes learning plans using generative AI models. It uses a cloud-based data processing server (e.g., AWS, Google Cloud Platform).

[2187] Generative AI model: An AI model that generates and optimizes a learning plan based on the user's initial information and progress information. For example, OpenAI GPT-4 is used.

[2188] Gathering initial information

[2189] User: Starts the application and enters initial information such as grade, favorite subjects, favorite subjects, and interests.

[2190] Terminal: Collects information entered by the user and sends it to the server in the form of an HTTP request.

[2191] Server: Stores the received user information in a database. Generates prompts from the generative AI model and asks it to generate an optimal learning plan. Sends the generated learning plan to the device.

[2192] View your learning plan

[2193] Terminal: Displays the learning plan received from the server to the user.

[2194] As a concrete example, a second-year junior high school student inputs, "I'm not good at math, but I'm interested in science," and the generative AI model generates a study plan such as, "Basic math problems, 30 minutes a day, watching science experiment videos once a week."

[2195] Learning progress and progress records

[2196] User: Study based on the presented study plan. After completing the study, enter the progress (for example, level of understanding and interest) into the application.

[2197] Terminal: Collects the progress information entered by the user and sends it to the server in the form of an HTTP request.

[2198] Server: Stores the received progress information in a database, sends prompts to the generative AI model to optimize the new learning plan, receives the new learning plan, and sends it to the device.

[2199] Terminal: Displays the newly received lesson plan to the user.

[2200] As a concrete example, based on the progress of "80% understanding of mathematics, 95% interest in science," the generative AI generates a new optimized learning plan.

[2201] Participating in online communities

[2202] Users: Search for online community groups that interest them within the application and submit a request to join.

[2203] Terminal: Sends a request to the server to join the community group selected by the user.

[2204] Server: Based on the received request, recommends groups based on the user's interests and approves their participation.

[2205] Terminal: Displays community information to users and helps them interact with other members.

[2206] As a specific example, if a user becomes interested in a "plant growing group" and sends a request to join, the server will approve the request and the terminal will display community information.

[2207] Use of consultation function

[2208] User: Enter the consultation details within the application and submit.

[2209] Terminal: Collects consultation details and sends them to the server.

[2210] Server: Assigns the received consultation content to the appropriate counselor or AI teacher, collects the reply content, and sends it to the device.

[2211] Terminal: Display the reply to the user.

[2212] As a specific example, a user inputs "advice about career paths," the server assigns it to a counselor, and the reply is sent to the terminal and displayed to the user.

[2213] Prompt Sentence Examples

[2214] "Please create the optimal study plan based on the initial information of the new user. The student is in the second year of junior high school, is not good at math, and is interested in science."

[2215] "Please optimize your study plan based on the progress information below. Your math comprehension is 80% and your science interest is 95%."

[2216] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2217] Step 1: User launches application and enters initial information

[2218] Input: Grade, strong subjects, weak subjects, interests

[2219] Output: User's initial information

[2220] Specific operation: The user enters initial information such as grade level, favorite subjects, weak subjects, and interests into the application's input form.

[2221] Step 2: The device sends initial information to the server

[2222] Input: Initial information entered by the user

[2223] Output: HTTP request format data

[2224] Specific operation: The terminal collects the user's initial information and sends it to the server as an HTTP request.

[2225] Step 3: The server saves the initial information and sends a prompt to the generative AI model.

[2226] Input: Initial information in the HTTP request format

[2227] Output: Saved user information, prompts to send to the generative AI model

[2228] Specific operation: The server stores the received user information in a database, then generates and sends prompts to the generative AI model to generate an optimal learning plan.

[2229] Step 4: The generative AI model generates a learning plan and sends it to the server

[2230] Input: prompt statement

[2231] Output: The generated learning plan

[2232] Specific operation: The generative AI model generates a learning plan based on the prompt sentence and sends the generated results back to the server.

[2233] Step 5: The server sends the generated lesson plan to the device.

[2234] Input: Generated lesson plan

[2235] Output: Learning plan in HTTP response format

[2236] Specific operation: The server sends the learning plan received from the generative AI model to the terminal as an HTTP response.

[2237] Step 6: The device displays the lesson plan to the user

[2238] Input: Lesson plan in HTTP response format

[2239] Output: A displayed lesson plan

[2240] Specific operation: The device displays the received learning plan in an easy-to-understand manner to the user. Specifically, it displays a plan such as "30 minutes of basic math problems every day and watching science experiment videos once a week."

[2241] Step 7: User progresses and enters progress information

[2242] Input: Progress information (level of understanding, interest, etc.)

[2243] Output: User progress information

[2244] Specific operation: The user studies according to the presented study plan and enters their study progress into the application's input form.

[2245] Step 8: The device sends progress information to the server

[2246] Input: Progress information entered by the user

[2247] Output: Progress information in the form of an HTTP request

[2248] Specific operation: The device collects the user's progress information and sends it to the server as an HTTP request.

[2249] Step 9: The server saves the progress and sends a new prompt to the generative AI model.

[2250] Input: Progress information in HTTP request format

[2251] Output: Saved progress information, new prompt text

[2252] Specific operation: The server stores the received progress information in the database, then generates and sends a prompt to the generative AI model to generate a new learning plan.

[2253] Step 10: The generative AI model generates a new learning plan and sends it to the server.

[2254] Input: New prompt text

[2255] Output: The new learning plan generated.

[2256] Specific operation: The generative AI model generates a new learning plan based on progress information and sends the generated results back to the server.

[2257] Step 11: The server sends the new lesson plan to the device.

[2258] Input: New lesson plan

[2259] Output: The new learning plan in the form of an HTTP response.

[2260] Specific operation: The server sends the new learning plan received from the generative AI model to the terminal as an HTTP response.

[2261] Step 12: The device displays the new lesson plan to the user.

[2262] Input: A new learning plan in the form of an HTTP response

[2263] Output: The new lesson plan displayed.

[2264] Specific operation: The device displays the new learning plan to the user in an easy-to-understand manner.

[2265] Step 13: User finds online community and submits request to join

[2266] Input: Community name of interest

[2267] Output: Join request

[2268] What happens: A user searches for an online community group of interest within the application, selects one, and submits a request to join.

[2269] Step 14: The device sends a join request to the server

[2270] Input: Join request

[2271] Output: Join request in HTTP request format

[2272] Specific operation: The terminal collects the user's participation request and sends it to the server as an HTTP request.

[2273] Step 15: The server processes the join request and sends the online community information

[2274] Input: Join request

[2275] Output: Online community information

[2276] Specific operation: The server receives the participation request, recommends communities based on the user's interests, and sends information approving participation to the device.

[2277] Step 16: The terminal displays online community information to the user

[2278] Input: Online community information

[2279] Output: Displayed online community information

[2280] Specific operation: The device displays online community information to the user and supports interaction with other members.

[2281] (Application example 1)

[2282] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2283] Conventional learning support systems have struggled to provide optimal learning plans and connections to society for children who are not attending school. Furthermore, they lacked the ability to manage learning progress and dynamic responses to individual users' interests and needs. Furthermore, they lacked systems that integrated online communities and consultation functions, which led to students becoming isolated.

[2284] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2285] In this invention, the server includes means for collecting initial information from a user, artificial intelligence generating means for generating a study plan based on the initial information, means for presenting the study plan generated by the artificial intelligence generating means to the user, means for collecting the user's study progress, means for optimizing the study plan based on the study progress, means for presenting the optimized study plan to the user, means for supporting participation in an online community based on the user's interests, means for consulting with a counselor or an artificial intelligence teacher, and means for being installed as a smartphone application in a virtual store. This makes it possible to provide children who are not attending school with an individualized, optimal study plan, an online community, and consultation functions in an integrated manner.

[2286] "User information" refers to initial setting information about an individual user, such as grade, favorite subjects, weak subjects, and interests.

[2287] A "study plan" is a study schedule or curriculum created by the artificial intelligence based on user information.

[2288] "Generative AI" is an AI model that creates optimal learning plans based on user information.

[2289] "Progress" refers to information that records the user's learning progress and achievement level.

[2290] "Optimization" refers to the process of modifying and adjusting the learning plan based on the user's progress and needs.

[2291] An "online community" is an online group or forum that users can join based on their interests.

[2292] A "counselor" is a professional consultant who provides advice to users and offers solutions.

[2293] An "artificial intelligence teacher" is an AI-based support system provided by generative artificial intelligence to respond to user inquiries.

[2294] A "virtual store" is a virtual store that provides the same services online as a physical store.

[2295] A "smartphone application" is a software application that runs on a smartphone and allows users to use the system.

[2296] This invention is a system that provides optimal learning opportunities and social connections to children who are not attending school. This system is installed as a smartphone application in a virtual store, and is configured to allow users to access personalized learning plans, online communities, and even counseling services.

[2297] 1. System Configuration

[2298] Hardware

[2299] Smartphone: A device that allows users to access the system.

[2300] Server: Processes data, runs generative AI models, and manages databases.

[2301] software

[2302] Smartphone application: An app for collecting user information, displaying learning plans, recording progress, participating in online communities, and sending and receiving consultations.

[2303] Generative AI model: An AI model for creating learning plans based on user information.

[2304] 2. Operational flow

[2305] User registration and initial settings

[2306] The user launches the smartphone application and enters initial information such as their grade level, favorite subjects, weak subjects, and interests. The smartphone then sends this information to the server. The server stores the received information in a database and uses a generative AI model to create an optimal initial learning plan. The created learning plan is sent to the user via the smartphone application and displayed.

[2307] Learning progress and progress records

[2308] The user studies according to the generated study plan and inputs their progress into the smartphone application. The smartphone then sends the input progress to the server. The server then uses the generative AI model to optimize the study plan based on the received progress information and creates a new study plan. This plan is also sent to the user via the smartphone application and displayed.

[2309] Participating in online communities

[2310] Users search for community groups that interest them and send a request to join from their smartphone application. The server receives the request, recommends the most suitable group based on the user's interests, and approves their participation. The approval result and community information are notified to the smartphone, allowing users to interact with other members.

[2311] Use of consultation function

[2312] The user inputs the consultation content and sends it to the server via the smartphone application. The server receives the consultation content and assigns it to an appropriate counselor or AI model. The counselor or AI model then creates a reply and sends it to the smartphone application via the server, where it is displayed to the user.

[2313] 3. Specific Examples

[2314] Prompt Sentence Examples

[2315] For example, a second-year junior high school student launches the application and enters, "I'm not good at math, but I'm interested in science." An example of a study plan generated based on this user information might be, "Basic math problems, 30 minutes a day, watching science experiment videos once a week."

[2316] User initial information:

[2317] Grade: 2nd year of junior high school

[2318] Favorite subject: Science

[2319] Weak subject: Mathematics

[2320] Interests: Science experiments

[2321] Generate an optimal weekly study plan for this user.

[2322] In this way, this system provides children who are not attending school with an optimal, individualized learning plan, an online community, and consultation functions in an integrated manner, realizing learning opportunities and connections to society.

[2323] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2324] Step 1:

[2325] The user launches the smartphone application and enters their initial information (grade, favorite subjects, weak subjects, interests, etc.). The device collects this information and sends it to the server. The entered data includes grade, favorite / weak subjects, and interests, and a process is performed to transfer this data to the server. Specifically, when the submit button on the input form is pressed, an API request is sent.

[2326] Step 2:

[2327] The server stores the received initial information in a database. The stored data includes the user ID and corresponding grade, favorite subjects, weak subjects, and interests. The server inputs the stored data into the generative AI model and requests it to generate an optimal learning plan. Specific operations include a database insert query and prompt generation for the AI ​​model.

[2328] Step 3:

[2329] The generative AI model generates a study plan based on the input user information. An example of the generated study plan might be "basic math problems for 30 minutes every day, watching science experiment videos once a week." This plan is sent to the server, which then transmits it to the smartphone application. Specific operations include the AI ​​model's calculation process and the generation of API responses.

[2330] Step 4:

[2331] The device displays the learning plan received from the server to the user. The displayed learning plan includes specific assignments and a learning schedule, and the user follows this to progress with their studies. Specifically, the device performs a process of rendering the received data on the screen.

[2332] Step 5:

[2333] Users record their learning progress and enter it into a smartphone application. The device collects this progress data and sends it to the server. The progress data entered includes the level of understanding and achievement, and this is then transferred to the server. Specifically, an API request is sent when the send button on the progress input form is pressed.

[2334] Step 6:

[2335] The server stores the received progress information in a database and inputs it into the generative AI model. The generative AI model optimizes the learning plan based on the latest progress information. The optimized plan is sent to the server and then distributed to the smartphone application. Specific operations include issuing a database update query and generating a prompt for the AI ​​model.

[2336] Step 7:

[2337] A user searches for online communities that interest them and submits a request to join. The device then sends this request to the server, which then recommends the most suitable group based on the user's interests and approves their participation. Specific operations include sending a request and receiving an API response approving their participation.

[2338] Step 8:

[2339] The user enters the consultation details and sends them from the smartphone application to the server. The server receives the consultation details and assigns them to an appropriate counselor or AI model. The reply is sent to the smartphone application via the server and displayed to the user. Specific operations include sending the input form, generating an API response, and displaying the reply.

[2340] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2341] This invention is a system that provides learning opportunities and social connections to children who are not attending school. It further incorporates an emotion engine that recognizes the user's emotions to personalize the learning experience and provide emotional support. The system collects initial information about the user, uses generative AI to provide an optimal learning plan, and provides a means to participate in online communities and counseling functions. It also has the ability to recognize the user's emotions and adjust the learning plan and community participation support.

[2342] Program processing overview

[2343] The system of the present invention performs the following main processes.

[2344] 1. User registration and initial settings

[2345] User: The user starts the application and enters initial information such as their grade, favorite subjects, least favorite subjects, and interests.

[2346] Terminal: The terminal collects the user's input information and generates a data transmission request to the server.

[2347] Server: The server receives user information and stores it in a database. The generation AI creates an optimal initial learning plan for the user based on the initial information received.

[2348] 2. Generate a learning plan

[2349] Server: The generation AI generates a learning plan based on the user's initial information and sends it to the terminal.

[2350] Terminal: The terminal displays the received learning plan to the user.

[2351] 3. Learning progress and progress records

[2352] User: The user follows the presented learning plan and enters their progress into the learning application.

[2353] Terminal: The terminal collects the progress information entered by the user and generates a request to send to the server.

[2354] Server: The server receives the progress information and stores it in a database. The generation AI analyzes the progress information and optimizes the existing learning plan.

[2355] Server: Sends the optimized learning plan to the device.

[2356] Device: The device presents the optimized learning plan to the user.

[2357] 4. Participating in online communities

[2358] Users: Users can search for community groups that interest them and submit a request to join.

[2359] Terminal: The terminal sends a join request to the server.

[2360] Server: The server receives the join request, recommends appropriate community groups based on the user's interests, and approves the join.

[2361] Server: Sends community information to the terminal.

[2362] Terminal: The terminal displays community information to the user and facilitates interaction with other members.

[2363] 5. Use of the consultation function

[2364] User: The user enters the consultation content and submits it.

[2365] Terminal: The terminal generates a request to send the consultation content to the server.

[2366] Server: The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[2367] Server: Sends the reply from the counselor or AI teacher to the device.

[2368] Terminal: The terminal displays the reply from the counselor or AI teacher to the user.

[2369] 6. Use of Emotion Engines

[2370] Terminal: The terminal activates sensors (e.g., facial recognition camera, voice analysis, etc.) to recognize the user's emotions.

[2371] User: The user displays emotions such as facial expressions and voice on the input device while learning or consulting.

[2372] Terminal: The terminal collects emotion data and generates a request to send it to the server.

[2373] Server: The server receives the emotion data and the emotion engine analyzes it.

[2374] Emotional response: The emotion engine optimizes feedback on the user's study plan and consultation content based on the results of emotion analysis.

[2375] Terminal: Presents the user with a learning plan and consultation content optimized by the emotion engine.

[2376] Specific examples

[2377] An example of user registration and initial settings

[2378] 1. User: A 8th grade user launches the application and enters, "I'm not good at math, but I'm interested in science."

[2379] 2. Terminal: The terminal sends this to the server.

[2380] 3. Server: Based on the user information, the server uses a generative AI to generate an initial learning plan such as "basic math problems for 30 minutes every day, and watching science experiment videos once a week" and sends it to the device.

[2381] 4. Terminal: The terminal displays the lesson plan to the user.

[2382] An example of learning progress and progress record

[2383] 1. User: The user studies for a week and enters their progress (e.g., "Mathematics comprehension 80%, Science interest 95%) into the application.

[2384] 2. Terminal: The terminal sends progress information to the server.

[2385] 3. Server: The server receives the progress information, and the generation AI optimizes the learning plan based on this information and sends the new learning plan to the device.

[2386] 4. Device: The device presents the optimized learning plan to the user.

[2387] An example of using online communities

[2388] 1. User: A user becomes interested in the "Plant Growing Group" and sends a request to join.

[2389] 2. Terminal: The terminal sends a join request to the server.

[2390] 3. Server: The server approves participation based on the user's interests and sends community information to the terminal.

[2391] 4. Terminal: The terminal displays community information to the user and supports interaction with other members.

[2392] An example of using the emotion engine

[2393] 1. Device: The device captures the user's facial expressions with a camera and recognizes their emotions.

[2394] 2. Server: The server analyzes the emotional data and determines that the user is feeling stressed.

[2395] 3. Emotional response: The emotion engine optimizes learning content to temporarily ease user stress and enhances consultation functions.

[2396] 4. Terminal: Presents the user with a learning plan and consultation options tailored by the emotion engine.

[2397] In this way, the system provides individually optimized learning environments and psychological support to children who are not attending school.

[2398] The processing flow will be explained below.

[2399] Step 1:

[2400] User: The user launches the application and enters their own information (grade, favorite subjects, weak subjects, interests, etc.) on the registration screen.

[2401] Step 2:

[2402] Terminal: The terminal receives the user's input information and generates a data transmission request to the server.

[2403] Step 3:

[2404] Server: The server receives user information and stores it in a database. The generation AI creates an optimal initial learning plan for the user based on the initial information received.

[2405] Step 4:

[2406] Server: Sends the generated learning plan to the device.

[2407] Step 5:

[2408] Device: The device displays the received learning plan to the user and prompts them to start learning.

[2409] Step 6:

[2410] User: The user follows the presented learning plan and proceeds with their studies. They enter their daily learning progress (e.g., learning content, level of understanding, study time, etc.) into the learning application.

[2411] Step 7:

[2412] Terminal: The terminal collects the progress information entered by the user and generates a request to send to the server.

[2413] Step 8:

[2414] Server: The server receives the progress information and stores it in a database. The generation AI analyzes the progress information and optimizes the existing learning plan.

[2415] Step 9:

[2416] Server: Sends the optimized learning plan to the device.

[2417] Step 10:

[2418] Device: The device presents the optimized learning plan to the user.

[2419] Step 11:

[2420] Users: Users want to join an online community and search for groups that interest them.

[2421] Step 12:

[2422] User: A user sends a request to join a group they wish to join.

[2423] Step 13:

[2424] Terminal: The terminal sends a join request to the server.

[2425] Step 14:

[2426] Server: The server receives the join request, recommends appropriate community groups based on the user's interests, and approves the join.

[2427] Step 15:

[2428] Server: Sends community information to the terminal.

[2429] Step 16:

[2430] Terminal: The terminal displays community information to the user and facilitates interaction with other members.

[2431] Step 17:

[2432] User: The user enters into the application what they would like to discuss with the counselor or AI teacher.

[2433] Step 18:

[2434] Terminal: The terminal generates a request to send the consultation content to the server.

[2435] Step 19:

[2436] Server: The server receives the consultation content and assigns it to an appropriate counselor or AI teacher.

[2437] Step 20:

[2438] Server: Sends the reply from the counselor or AI teacher to the device.

[2439] Step 21:

[2440] Terminal: The terminal displays the reply from the counselor or AI teacher to the user.

[2441] Step 22:

[2442] Terminal: The terminal activates sensors (e.g., facial recognition camera, voice analysis, etc.) to recognize the user's emotions.

[2443] Step 23:

[2444] User: The user displays emotions such as facial expressions and voice on the input device while learning or consulting.

[2445] Step 24:

[2446] Terminal: The terminal collects emotion data and generates a request to send it to the server.

[2447] Step 25:

[2448] Server: The server receives the emotion data and the emotion engine analyzes it.

[2449] Step 26:

[2450] Emotional response: The emotion engine optimizes feedback on the user's study plan and consultation content based on the results of emotion analysis.

[2451] Step 27:

[2452] Terminal: Presents the user with a learning plan and consultation content optimized by the emotion engine.

[2453] Through the above process, this system can provide individually optimized learning environments and psychological support to children who are not attending school.

[2454] Example 2

[2455] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2456] In modern society, the problem of children not attending school is extremely serious, with a lack of learning opportunities and social connections being major issues. Furthermore, these children require emotional support and require individualized learning plans and psychological assistance. However, existing education systems do not adequately create learning plans tailored to the needs of individual children or recognize and respond to their emotions. As a result, children may lose motivation to learn and become even more isolated.

[2457] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for collecting initial information from a user; artificial intelligence generation means for generating a study plan based on the initial information; means for presenting the study plan generated by the artificial intelligence generation means to the user; means for collecting the user's learning progress; means for optimizing the study plan based on the learning progress; means for presenting the optimized study plan to the user; means for supporting participation in an online community based on the user's interests; means for consulting with a counselor or an artificial intelligence teacher; means including a sensor for recognizing the user's emotions; emotional response means for analyzing the emotional data and optimizing the study plan and consultation content; and means for presenting the optimized study plan and consultation content to the user based on the emotional response. This makes it possible to provide an individualized learning environment and psychological support to children who are not attending school, thereby improving their motivation to study and promoting their connections with society.

[2458] "User" refers to the learner who uses the system, specifically the entity who inputs initial...

Claims

1. means for collecting initial information from a user; A generation artificial intelligence means for generating a learning plan based on the initial information; means for presenting to a user the study plan generated by the generating artificial intelligence means; a means for collecting information on the user's learning progress; means for optimizing the learning plan based on the learning progress; means for presenting the optimized study plan to a user; means for facilitating participation in online communities based on the user's interests; a means to consult with a counselor or AI teacher; A system including:

2. 2. The system according to claim 1, wherein the initial information includes grade, favorite subjects, and interests.

3. 10. The system of claim 1, wherein the online community includes means for making group recommendations based on user interests.

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