System

Abstract

To provide a system for reducing the labor of a protector, and for effectively supporting the middle school examination of a child.SOLUTION: The system includes means for receiving necessary information input by the user and registering data, means for receiving a small test or a result of a class and analyzing data to create a user profile, means for specifying a weak point or a strong point of the user to create an individual learning plan, means for evaluating a progress status from an input learning progress and generating a recovery plan by the server, means for proposing a school of choice based on the characteristics, results, and progress of the user, and means for displaying the progress status and feedback of the user in a dashboard format.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] Junior high school entrance exams require meticulous management of children's academic progress and the creation of effective study plans. However, it is extremely difficult for parents to accomplish these tasks while also juggling housework and work, placing a heavy physical and mental burden on them. Furthermore, efficiently analyzing test and class results provided by cram schools to identify children's weaknesses and find the best schools to apply to that fit their characteristics is a complex task. To address these challenges, a system is needed that analyzes children's academic performance and characteristics, proposes effective study plans, and guides them to the right schools. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following system. First, it provides a means for the user to input the necessary information and send it to a server. Second, it provides a means for the server to register the received data and create a user profile. Furthermore, it provides a means for the server to receive quiz and class results, analyze the data using a generative AI model, and identify the user's weaknesses and strengths. It also provides a means for generating an individualized study plan and sending it to a device. The user follows the study plan and enters their study progress into the device, which then sends the progress data to the server. The server evaluates the progress and, if necessary, generates a recovery plan and sends it to the device. Furthermore, the server suggests the most suitable school based on the user's characteristics, grades, and progress, and sends the suggestion to the device. The device displays this information to the user and also provides a dashboard that allows the user to check the user's study progress and feedback at a glance. This reduces the burden on parents and effectively supports their children's junior high school entrance exams.

[0006] "User" is a term that refers to parents and students who use the system.

[0007] "Terminal" is a term that refers to a device (e.g., PC, smartphone, tablet) that a user uses to access the system and input and display information.

[0008] "Server" is a term used to refer to a central system that receives, processes, and analyzes data from users.

[0009] "Study plan" is a term used to describe a specific study schedule or tasks that are created using a generative AI model based on a user's characteristics and performance.

[0010] "Generative AI model" is a term used to describe artificial intelligence techniques used to analyze data and recognize patterns.

[0011] "Recovery plan" is a term that refers to new learning tasks or schedules created to improve or reinforce a learning plan when the original goal is not achieved.

[0012] "Preferred school" is a term that refers to the junior high school one wishes to take the entrance exam for.

[0013] "Dashboard" is a term that refers to a display interface that allows users to see their learning progress and feedback at a glance.

[0014] "Study progress" is a term that refers to the progress of a user's learning according to a study plan.

[0015] "Progress data" is a term that refers to information that records the results and progress of a user's learning based on a learning plan.

[0016] "Feedback" is a term that refers to information provided by the server to evaluate the user's learning progress and performance and provide next learning steps and areas for improvement.

[0017] "Profile" is a term that refers to a data set that registers a user's basic information (e.g., grade, current grades, preferred school, etc.).

[0018] "Test results" is a term that refers to the results of quizzes and class evaluations provided by cram schools and other learning institutions. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] This invention is a system in which a user inputs necessary information, and a server generates a study plan based on that information, monitors progress, and provides a recovery plan as needed. This system is designed to support users in taking junior high school entrance exams, with the aim of reducing the burden on parents and maximizing the learning effect of their children.

[0041] This system operates in the following steps:

[0042] User registration and initial settings

[0043] First, the user uses a terminal to input the necessary information, including the child's grade, current grades, desired school, etc. This input data is then sent from the terminal to the server.

[0044] Data collection and analysis

[0045] The server registers the received user information and creates a user profile. The server also receives quizzes and class results provided by the cram school. This data is analyzed by the generative AI model to identify the child's weaknesses and strengths.

[0046] Generate a lesson plan

[0047] The server uses the generative AI model to generate a personalized learning plan based on the user's characteristics and performance, which is then sent to the user's device and displayed to them.

[0048] Learning progression and monitoring

[0049] The user studies daily according to the study plan and enters their progress into the device. The device then sends this progress data to the server, which monitors and evaluates the progress data.

[0050] Feedback and Recovery Plan

[0051] The server reanalyzes the latest learning data and past performance data and generates a recovery plan as needed, which is then sent to the device and displayed to the user.

[0052] Suggestion of desired school

[0053] The server then suggests the best schools to apply to based on the user's characteristics, grades, and progress. These suggestions are also sent to the device and displayed to the user.

[0054] Dashboard View

[0055] The device displays the user's progress and feedback in the form of a dashboard, allowing the user to see their learning progress at a glance.

[0056] Specific examples

[0057] For example, if a user inputs the grade data of their fifth-grade child and indicates their preference for Junior High School A, the server creates a profile based on the information the user inputs. The server analyzes test results provided by the cram school and uses a generative AI model to identify that the child is weak in "science." The server then generates a study plan that includes "30 minutes of science review every day" and sends it to the device. The user follows this study plan and enters their progress on the device. The server monitors their progress and provides a recovery plan as needed. The server also suggests to the user that "Junior High School B" is also suitable, and sends this information to the device and displays it to the user.

[0058] As described above, this system works in cooperation with the user, terminal, and server to support children's learning.

[0059] The processing flow will be explained below.

[0060] Program processing steps

[0061] Step 1: User registration and initial setup

[0062] 1.1 The user selects the "New Registration" option from the initial screen of the device.

[0063] Specific operation: The user enters information such as "name," "grade," "current grades," and "desired school" into the input form.

[0064] 1.2 The terminal sends the entered user information to the server.

[0065] Specific operation: The terminal encodes the input information into JSON format and sends a POST request to the server's API endpoint.

[0066] 1.3 The server registers the received user information in a database and creates a user profile.

[0067] Specific behavior: The server parses the received JSON data and saves it as a new user entry in the database.

[0068] Step 2: Collect and analyze data

[0069] 2.1 The server receives quizzes and class results provided by the cram school.

[0070] Specific operation: The server periodically retrieves test result data from the cram school's system via API.

[0071] 2.2 The server uses the generated AI model to analyze the test results and user performance data.

[0072] Specific operation: The server inputs the acquired test data into the generative AI model to analyze weaknesses and extract areas of strength.

[0073] Step 3: Generate a lesson plan

[0074] 3.1 The server generates an individualized learning plan based on the user's characteristics and performance data.

[0075] Specific operation: The server prioritizes and schedules learning tasks based on the user's grades and the question trends of the school of their choice.

[0076] 3.2 The server sends the generated learning plan to the device.

[0077] Specific operation: The server encodes the learning plan into JSON format and sends it to the device's API endpoint.

[0078] 3.3 The device visualizes the learning plan and displays it to the user.

[0079] Specific operation: The device displays the received study plan in calendar or list format and allows the user to confirm it.

[0080] Step 4: Progressing and monitoring your learning

[0081] 4.1 The user progresses through daily learning and inputs their learning progress into the device.

[0082] Specific operation: When the user finishes learning, he / she selects the content he / she learned on the "Learning Progress" screen of the device and presses the "Complete" button.

[0083] 4.2 The device sends the learning progress data to the server.

[0084] Specific operation: The device encodes the learning progress data entered by the user into JSON format and sends it to the server.

[0085] Step 5: Feedback and recovery plan

[0086] 5.1 The server evaluates the progress based on the latest learning progress data.

[0087] Specific operation: The server inputs new learning progress data into the generative AI model and evaluates the user's progress.

[0088] 5.2 The server generates a recovery plan as needed.

[0089] Specific operation: Based on the progress assessment results, the server generates a recovery plan for the required reinforcement.

[0090] 5.3 The server sends the recovery plan to the device.

[0091] Specific operation: The server encodes the generated recovery plan into JSON format and sends it to the terminal.

[0092] 5.4 The terminal visualizes the recovery plan and displays it to the user.

[0093] Specific operation: The device will display the received recovery plan in a pop-up notification or on the dashboard.

[0094] Step 6: Propose your preferred school

[0095] 6.1 The server suggests the most suitable schools to apply to based on the user's characteristics, grades, and progress data.

[0096] Specific operation: The server inputs learning data and past data into the generative AI model and creates a list of the most suitable schools for the user.

[0097] 6.2 The server sends the school preference suggestions to the device.

[0098] Specific operation: The server encodes the school preference proposals into JSON format and sends them to the device.

[0099] 6.3 The device displays school preference suggestions to the user.

[0100] Specific operation: The device displays the received list of preferred schools on the screen and provides the characteristics of each school and exam information.

[0101] Step 7: View the dashboard

[0102] 7.1 The device displays user progress and feedback in a dashboard format.

[0103] How it works: The device displays the latest learning data, progress reports, feedback, and information about the school of choice all on one screen.

[0104] As a result, this system meticulously monitors the user's learning progress and provides effective study plans and appropriate recovery plans, thereby reducing the burden on parents and providing effective support for their children's junior high school entrance exams.

[0105] Example 1

[0106] 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."

[0107] In today's educational environment, it is extremely difficult for parents to effectively manage and support their children's learning progress. Particularly when it comes to exam preparation and improving grades, accurately identifying a child's weaknesses and creating an effective learning plan to overcome those weaknesses requires advanced expertise and continuous monitoring. However, it is not realistic for parents to do this manually in their busy daily lives, and it places a significant burden on them. To solve this problem, a system is needed that can efficiently and effectively automatically generate learning plans and track and improve progress.

[0108] 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.

[0109] In this invention, the server includes: means for a user to input required information; means for transmitting the input information to the server; means for registering the received data and creating a user profile; means for receiving and analyzing assessment data provided by educational institutions and utilizing a generative AI model; means for identifying the user's weaknesses and strengths and generating an individualized learning plan; means for transmitting the generated learning plan to a terminal; means for the terminal to display the learning plan to the user; means for the user to input learning progress into the terminal and transmit the progress data to the server; means for evaluating progress and generating a recovery plan; means for transmitting the recovery plan to the terminal; means for the terminal to display the recovery plan; means for suggesting preferred schools based on the user's characteristics, grades, and progress; means for transmitting the preferred school suggestions to the terminal; means for the terminal to display the preferred school suggestions to the user; means for the terminal to display the user's progress and feedback in a dashboard format; and means for generating prompt sentences using a generative AI model to generate an optimal learning plan or recovery plan based on the child's learning progress. This enables users to efficiently and effectively automate a series of learning support tasks, such as creating a learning plan, monitoring progress, providing a recovery plan, and suggesting preferred schools.

[0110] "User" refers to an individual who uses this system to receive services such as creating a study plan, monitoring progress, providing a recovery plan, and suggesting preferred schools.

[0111] "Terminal" refers to a device that a user operates to input information and receive feedback and suggestions from the server, and specifically includes smartphones, tablets, and PCs.

[0112] "Server" refers to a computer system that receives information sent by a user, creates a user profile based on this information, generates a study plan, recovery plan, and suggestions for preferred schools, and sends these to the terminal.

[0113] A "generative AI model" is an artificial intelligence model used by the server to analyze user information and learning data to generate learning plans and recovery plans.

[0114] A "prompt sentence" is an input sentence used to give instructions to a generative AI model, and is used to specify how to generate a specific learning plan or recovery plan.

[0115] A "study plan" refers to a schedule of learning content that is individually created taking into account the user's weaknesses and strengths, and includes specific learning tasks aimed at improving a child's academic ability.

[0116] A "recovery plan" refers to a supplementary learning plan or specific improvement measures that are deemed necessary as a result of analyzing a user's learning progress and performance data.

[0117] "School of choice suggestions" refers to the server recommending the most suitable school based on the user's characteristics, grades, and progress, and includes information for selecting the school that best suits the user's wishes and abilities.

[0118] "Dashboard" refers to an interface that displays on the device a user's learning progress, feedback, recovery plans, suggested schools of choice, and more, all at a glance.

[0119] "User profile" refers to a data set that includes detailed information such as the user's grade, grades, and preferred school, which is created by the server based on information received from the user.

[0120] "Evaluation data" refers to data used to evaluate a user's learning status, such as quizzes and class results provided by educational institutions.

[0121] This system allows users to input necessary information, and a server generates a study plan based on that information, monitors progress, and provides recovery plans as needed. It is designed to support users preparing for junior high school entrance exams, reducing the burden on parents and maximizing the learning outcomes of their children.

[0122] User registration and initial settings

[0123] The user uses a device to input information such as their child's grade, current grades, and desired school. This input data is sent from the device to a server. The device can be a smartphone, tablet, or PC.

[0124] Data collection and analysis

[0125] The server creates a user profile based on the received user information. The server also receives data from educational institutions, such as quizzes and class results, and stores them in a database. This data is analyzed using a generative AI model (e.g., "OpenAI GPT-4") to identify the child's weaknesses and strengths. The server sends the following prompt to the generative AI model:

[0126] text

[0127] Analyze this user's learning data to identify their weaknesses and strengths.

[0128] Generate a lesson plan

[0129] The server uses the generative AI model to generate a personalized learning plan based on the user's characteristics and performance data. This learning plan is sent to the user's device and displayed to the user. The specific prompt is as follows:

[0130] text

[0131] My fifth-grader wants to attend Junior High School A, but he is not good at science. Please create a specific study plan to help him overcome his science weaknesses.

[0132] Learning progression and monitoring

[0133] The user studies daily according to the generated study plan and enters their progress into the device. The entered progress data is sent from the device to the server. The server receives the progress data and evaluates the progress using a monitoring system.

[0134] Feedback and Recovery Plan

[0135] The server re-analyzes the latest learning data and past performance data received and generates a recovery plan as necessary. The generated recovery plan is sent to the user's device and displayed to the user. The specific prompt is as follows:

[0136] text

[0137] Please re-analyze this user's learning data and generate the necessary recovery plan.

[0138] Suggestion of desired school

[0139] The server then proposes the best schools to apply to based on the user's characteristics, grades, and progress data. These proposals are also sent from the server to the device and displayed to the user.

[0140] Dashboard View

[0141] The device displays the user's progress and feedback in the form of a dashboard, allowing the user to see their learning progress at a glance.

[0142] Specific examples

[0143] For example, let's say a user inputs the grade data of their child in the fifth grade of elementary school and indicates that they would like to attend Junior High School A. The user inputs the child's grade, grades, and desired school information into their device and submits it. The server receives this data, and the generative AI model analyzes it to determine that the child is "weak in science." The server then sends the following prompt to the generative AI model and obtains the result:

[0144] text

[0145] My fifth-grader wants to attend Junior High School A, but he is not good at science. Please create a specific study plan to help him overcome his science weaknesses.

[0146] Based on this prompt, the server generates a specific study plan, including "30 minutes of science review every day," and sends it to the device. The user studies according to this study plan and enters their progress into the device. The progress data is sent to the server, which monitors the progress and generates a recovery plan as necessary. Based on the progress data, the server suggests that "Junior High School B" is also suitable, and sends this information to the device and displays it to the user.

[0147] As described above, this system works in cooperation with the user, terminal, and server to support children's learning.

[0148] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0149] System program processing flow

[0150] Step 1: User registration and initial setup

[0151] Specific actions

[0152] Input: The user inputs information such as the child's grade, current grades, and desired school into the terminal.

[0153] How it works: Data entered by the user is sent from the device to the server. When the "Send" button is pressed using the device's application, this data is sent to the server.

[0154] Output: User information data received by the server.

[0155] Step 2: Collect and analyze data

[0156] Specific actions

[0157] Input: The server receives user input data and also receives quiz and class result data from the educational institution.

[0158] How it works: The server creates a user profile based on the data it receives and registers it in a database. It then inputs the received evaluation data into a generative AI model for analysis, identifying the child's weaknesses and strengths. Specifically, it sends the following prompt to the generative AI model:

[0159] text

[0160] Analyze this user's learning data to identify their weaknesses and strengths.

[0161] Output: The analysis results obtained from the generative AI model (child's weaknesses and strengths).

[0162] Step 3: Generate a lesson plan

[0163] Specific actions

[0164] Input: The analysis results received by the server from the generative AI model and the user's initial input data.

[0165] How it works: The server uses a generative AI model to generate a personalized learning plan based on this data. It sends specific prompts to the generative AI model:

[0166] text

[0167] My fifth-grader wants to attend Junior High School A, but he is not good at science. Please create a specific study plan to help him overcome his science weaknesses.

[0168] Output: The generated learning plan. This learning plan is sent from the server to the device.

[0169] Step 4: Progressing and monitoring your learning

[0170] Specific actions

[0171] Input: The user inputs their daily learning progress into the terminal.

[0172] How it works: The user enters progress information into the application on the device and presses the "Send progress" button. This data is sent from the device to the server. The server receives the progress data and evaluates the progress in the monitoring system.

[0173] Output: Learning progress data sent to the server. Evaluation results based on the progress data on the server.

[0174] Step 5: Feedback and recovery plan

[0175] Specific actions

[0176] Input: The server provides the latest learning progress data and past grade data.

[0177] How it works: The server re-analyzes this data and generates a recovery plan if necessary. It sends specific prompts to the generative AI model:

[0178] text

[0179] Please re-analyze this user's learning data and generate the necessary recovery plan.

[0180] Output: The generated recovery plan, which the server sends to the device.

[0181] Step 6: Propose your preferred school

[0182] Specific actions

[0183] Input: The server collects user characteristics, performance, and progress data.

[0184] How it works: The server uses this data to suggest the best schools to apply to. The suggestions are sent from the server to the device and displayed to the user.

[0185] Output: Proposed data for desired schools.

[0186] Step 7: View the dashboard

[0187] Specific actions

[0188] Input: Progress, feedback, recovery plan, and suggested school of choice data sent from the server.

[0189] How it works: The device receives this data and displays it in a dashboard format, allowing users to check their learning progress at a glance.

[0190] Output: Dashboard showing progress, feedback, recovery plan, and school preference suggestions.

[0191] These steps allow the user, terminal, and server to work together to create a system that supports children's learning.

[0192] (Application example 1)

[0193] 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."

[0194] In conventional risk management systems, risk assessment and progress monitoring were not fully automated, requiring personnel to manually organize and understand data. As a result, risk management efficiency was reduced and it was difficult to provide recovery plans quickly. In addition, progress could not be grasped in real time, resulting in delays in risk countermeasures.

[0195] 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.

[0196] In this invention, the server includes means for receiving risk information input by a user and generating a risk management plan using a generative AI model, means for evaluating risk management progress data and generating a recovery plan as necessary, and means for proposing appropriate risk management measures based on the user's risk information and progress. This enables automatic collection and analysis of risk information, automatic generation of management plans, real-time progress monitoring, and rapid provision of recovery plans.

[0197] Key Word Definitions:

[0198] "User"

[0199] is a person or organization that uses a system to input information and receive results.

[0200] "Required Information"

[0201] This refers to data necessary for risk management, such as company information, types of risks, and past security incidents.

[0202] "Terminal"

[0203] is an electronic device used by a user to input information and receive feedback from a system, including smartphones and tablets.

[0204] "server"

[0205] It is a communications device that receives and registers data sent by users, generates risk management plans and recovery plans using generative AI models, and sends them back to the terminal.

[0206] "Generative AI model"

[0207] This refers to algorithms that use machine learning and artificial intelligence techniques to analyze data and generate risk management and recovery plans.

[0208] "Risk Information"

[0209] is data about risk factors that may affect a company or organization, which can be entered by users via a terminal.

[0210] "Risk Management Plan"

[0211] A plan is generated using a generative AI model, which evaluates the risks that users need to address and includes specific countermeasures based on the assessment.

[0212] "Progress Data"

[0213] is data recorded by the user on their progress according to the risk management plan, which is sent to the server and used for evaluation.

[0214] "Recovery Plan"

[0215] A plan is one that includes alternative responses if things do not go as planned or new risks arise.

[0216] Dashboard

[0217] is an interface that visually organizes and displays information so that users can grasp the progress and current status of risk management at a glance.

[0218] "Risk management measures"

[0219] These are specific measures or steps that can be taken to address specific risks, and are proposed by the server based on the user's risk information and progress.

[0220] MODE FOR CARRYING OUT THE INVENTION

[0221] User registration and initial settings

[0222] First, the user uses a terminal to input the necessary information into the input form. This information includes company data related to risk management, risk types, past security incidents, etc. The input information is then sent from the terminal to the server. The terminal can be a smartphone or tablet and provides a user-friendly interface.

[0223] Data collection and analysis

[0224] The server registers the received user information and creates a user profile. The server also receives risk information and incident data from external sources. This data is analyzed by a generative AI model to classify and identify risk factors. The generative AI model is optimized using machine learning and artificial intelligence techniques.

[0225] Generate a risk management plan

[0226] The server uses the generative AI model to generate a personalized risk management plan based on the user's information. The plan includes specific countermeasures and a progress schedule. The risk management plan is then sent from the server to the device and displayed to the user.

[0227] Management Progression and Monitoring

[0228] Users report progress according to the risk management plan. They use their terminals to input progress and the status of countermeasure implementation, and send this to the server. The server monitors the progress data and evaluates the progress.

[0229] Feedback and Recovery Plan

[0230] The server re-analyzes the latest progress data and past risk data and generates a recovery plan as needed. This recovery plan may include additional or new countermeasures for specific risks. The generated recovery plan is sent to the terminal and displayed to the user.

[0231] Proposal of risk management measures

[0232] The server proposes optimal risk management measures based on the user's characteristics, risk information, and progress data, and these proposals are also sent to the terminal and displayed to the user.

[0233] Dashboard View

[0234] The device is equipped with a built-in dashboard displaying user progress and feedback, allowing users to see their risk management progress at a glance.

[0235] Specific examples

[0236] For example, when a user performs security risk management for "Small and Medium-sized Enterprise A," they first enter company information, risk types, and past incident data. The server then uses a generative AI model to create a risk profile for "Small and Medium-sized Enterprise A" and generates a risk management plan. This plan includes specific measures such as "30 minutes of security checks every day" and "data backup once a week." The user reports their progress according to this plan, and the server monitors the progress data. If progress is delayed, the server generates a new recovery plan and sends it to the device.

[0237] Examples of prompt statements

[0238] Here are some example prompts for a generative AI model:

[0239] "Our company is a small to medium-sized enterprise with 50 employees. We have experienced a data breach in the past. We are currently paying particular attention to phishing attacks and internal fraud risks."

[0240] In this way, the user can manage risk efficiently and effectively.

[0241] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0242] Program processing steps

[0243] Processing Steps

[0244] Step 1:

[0245] The user uses a terminal to input the required information, such as company information, risk type, and past security incidents. After the user enters this information into a form, the terminal sends the data to the server. This input data is used as the basis for generating a risk management plan.

[0246] Step 2:

[0247] The server registers the received data and creates a user profile. The server stores the received information in a database and organizes it as a user profile. It also acquires external risk information and past incident data and adds them to the database for analysis by the generative AI model. The input is user information sent from the device, and the output is the generation of a user profile.

[0248] Step 3:

[0249] The server uses a generative AI model to generate a risk management plan. The generative AI model analyzes risk factors based on the input user profile and risk data. This analysis generates an individual risk management plan. The generated plan includes specific countermeasures and progress schedules. The input is the user profile and risk data, and the output is a risk management plan.

[0250] Step 4:

[0251] The server sends the generated risk management plan to the terminal. The terminal displays this risk management plan to the user. The user starts to implement risk management based on the displayed plan. The input is the risk management plan, and the output is the display to the user.

[0252] Step 5:

[0253] The user reports progress according to the risk management plan. Using a terminal, the user inputs the progress status and the implementation status of countermeasures, and sends this data to the server. The input is the user's progress status data, and the output is the transmission of the progress data to the server.

[0254] Step 6:

[0255] The server monitors the progress data and evaluates the progress. The server analyzes the received progress data and identifies the progress and problems of risk management. If necessary, it generates a recovery plan. The input is the progress data, and the output is the generation of a recovery plan.

[0256] Step 7:

[0257] The server sends the generated recovery plan to the terminal, which displays it to the user. The recovery plan includes additional measures and new countermeasures for specific risks. The input is the recovery plan, and the output is the display to the user.

[0258] Step 8:

[0259] The server proposes appropriate risk management measures based on the user's risk information and progress. The generative AI model performs another analysis and proposes optimal management measures. The server sends this proposal to the terminal, which displays it to the user. The input is risk information and progress data, and the output is a risk management measure proposal.

[0260] Step 9:

[0261] The terminal displays the user's progress and feedback in the form of a dashboard. The dashboard displays progress, risk reviews, and feedback, allowing the user to check the situation in real time. The input is progress data and feedback data, and the output is the dashboard display.

[0262] 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.

[0263] This invention is a system to support preparation for junior high school entrance exams, monitoring the user's learning progress and emotional state, and providing a study plan and recovery plan based on this. This aims to reduce the user's burden while maximizing the learning effect.

[0264] This system has the basic functions of allowing users to input the necessary information, and then having the server generate a study plan based on that information, monitor progress, and provide a recovery plan if necessary. In addition, it also includes an emotion engine that recognizes the user's emotions.

[0265] User registration and initial settings

[0266] First, the user inputs the necessary information using the device, including the child's grade, current grades, preferred schools, and emotional data, which is then sent to the server.

[0267] Data collection and analysis

[0268] The server registers the received user information and creates a user profile. The server also receives quizzes and class results provided by the cram school and analyzes them using a generative AI model. Furthermore, the server uses the user's emotional data to adjust study plans and recovery plans.

[0269] Generate a lesson plan

[0270] The server uses a generative AI model to generate a personalized learning plan based on the user's characteristics, grades, and emotional state, which is then sent to the user's device and displayed to them.

[0271] Learning progression and monitoring

[0272] The user studies daily according to the study plan and inputs their learning progress into the device. The device then sends this progress data and emotional data to the server, which monitors and evaluates the progress data and emotional data.

[0273] Feedback and Recovery Plan

[0274] The server evaluates the user's progress based on the latest learning progress data and emotional data, and generates a recovery plan if necessary. The recovery plan is sent to the device and displayed to the user. For example, if the user is feeling stressed, adjustments may be made, such as adding tasks to help them relax.

[0275] Suggestion of desired school

[0276] The server will suggest the most suitable school to apply to based on the user's characteristics, grades, progress, and emotional data. These suggestions are also sent to the device and displayed to the user. It is also possible to select a school that offers a less stressful environment based on emotional data.

[0277] Dashboard View

[0278] The device displays the user's progress, feedback, and emotional data in a dashboard format, allowing the user to see at a glance their emotional changes as well as their learning progress.

[0279] Specific examples

[0280] For example, if a user inputs their child's grades and emotions and indicates their preference for Junior High School A, the server creates a profile based on the user's input. The server analyzes test results provided by the cram school, uses a generative AI model to identify that the child is weak in science, and uses an emotion engine to recognize that the child experiences high stress while studying science. The server then generates a study plan that includes 30 minutes of daily science review and adds a weekly relaxation task to reduce stress. This is sent to the device, and the user follows the study plan and enters their progress and emotional state into the device. The server monitors their progress and emotional state and provides a recovery plan as needed. The server also suggests Junior High School B as a suitable option, sending this information to the device and displaying it to the user.

[0281] As described above, this system works in cooperation with the user, terminal, and server to support the user's learning progress and emotional state.

[0282] The processing flow will be explained below.

[0283] Program processing steps

[0284] Step 1: User registration and initial setup

[0285] 1.1 The user selects the "New Registration" option from the initial screen of the device.

[0286] Specific operation: The user enters information such as "name," "grade," "current grades," "desired school," and "permission to collect emotional data" into an input form.

[0287] 1.2 The terminal sends the entered user information to the server.

[0288] Specific operation: The terminal encodes the input information into JSON format and sends a POST request to the server's API endpoint.

[0289] 1.3 The server registers the received user information in a database and creates a user profile.

[0290] Specific behavior: The server parses the received JSON data and saves it as a new user entry in the database.

[0291] Step 2: Collect and analyze data

[0292] 2.1 The server receives quizzes and class results provided by the cram school.

[0293] Specific operation: The server periodically retrieves test result data from the cram school's system via API.

[0294] 2.2 The server uses the generated AI model to analyze the test results and user performance data.

[0295] Specific operation: The server inputs the acquired test data into the generative AI model to analyze weaknesses and extract areas of strength.

[0296] 2.3 The device monitors the user's emotional state in real time and transmits the emotional data to the server.

[0297] Specific operation: The device uses the built-in camera and microphone to analyze the user's emotions from their facial expressions and voice, and sends the analysis results to the server.

[0298] Step 3: Generate a lesson plan

[0299] 3.1 The server generates an individualized learning plan based on the user's characteristics, performance data, and emotional data.

[0300] Specific operation: The server integrates the user's grades, exam trends at the school of choice, and emotional data to prioritize and schedule learning tasks.

[0301] 3.2 The server sends the generated learning plan to the device.

[0302] Specific operation: The server encodes the learning plan into JSON format and sends it to the device's API endpoint.

[0303] 3.3 The device visualizes the learning plan and displays it to the user.

[0304] Specific operation: The device displays the received study plan in calendar or list format and allows the user to confirm it.

[0305] Step 4: Progressing and monitoring your learning

[0306] 4.1 The user progresses through daily learning and inputs their learning progress into the device.

[0307] Specific operation: When the user finishes learning, he / she selects the content he / she learned on the "Learning Progress" screen of the device and presses the "Complete" button.

[0308] 4.2 The device sends learning progress data and emotion data to the server.

[0309] Specific operation: The device encodes the learning progress data entered by the user and the emotion data collected in real time into JSON format and sends it to the server.

[0310] Step 5: Feedback and recovery plan

[0311] 5.1 The server evaluates the progress based on the latest learning progress data and emotion data.

[0312] Specific operation: The server inputs new learning progress data and emotion data into the generative AI model and evaluates the user's progress.

[0313] 5.2 The server generates a recovery plan as needed.

[0314] Specific operation: Based on the progress assessment results, the server generates a recovery plan for the necessary reinforcement. For example, if the user is feeling stressed about a particular subject, the server will add tasks to reinforce that subject as well as tasks to help them relax.

[0315] 5.3 The server sends the recovery plan to the device.

[0316] Specific operation: The server encodes the generated recovery plan into JSON format and sends it to the terminal.

[0317] 5.4 The terminal visualizes the recovery plan and displays it to the user.

[0318] Specific operation: The device will display the received recovery plan in a pop-up notification or on the dashboard.

[0319] Step 6: Propose your preferred school

[0320] 6.1 The server suggests the best schools to apply to based on the user's characteristics, grades, progress data, and emotional data.

[0321] Specific operation: The server inputs learning data, past data, and emotional data into the generative AI model and creates a list of the most suitable schools for the user.

[0322] 6.2 The server sends the school preference suggestions to the device.

[0323] Specific operation: The server encodes the school preference proposals into JSON format and sends them to the device.

[0324] 6.3 The device displays school preference suggestions to the user.

[0325] Specific operation: The device displays the received list of preferred schools on the screen and provides the characteristics of each school and exam information.

[0326] Step 7: View the dashboard

[0327] 7.1 The device displays the user's progress, feedback, and emotional data in a dashboard format.

[0328] Specific operation: The device displays the latest learning data, progress reports, feedback, and emotional fluctuations on a single screen.

[0329] As a result, this system meticulously monitors the user's learning progress and emotional state, and provides effective study plans and appropriate recovery plans, thereby reducing the burden on parents and providing effective support for their children's junior high school entrance exams.

[0330] Example 2

[0331] 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."

[0332] In today's junior high school entrance exams, children are often exposed to hectic study schedules and pressure. This necessitates an efficient and effective learning management system. However, existing systems only monitor academic progress and do not take into account fluctuations in emotional states. This creates challenges, such as inadequate management of children's stress and anxiety, making it difficult to maximize overall learning outcomes.

[0333] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0334] In this invention, the server includes a means for creating a user profile, a means for receiving learning results and emotional data and utilizing a generative AI model for analyzing the data, and a means for generating an individual learning plan based on the user's characteristics and emotional data, thereby enabling a comprehensive learning support system that takes into account not only learning progress but also emotional states.

[0335] "User" refers to a person who utilizes the system to monitor their learning progress and emotional state and to receive learning and recovery plans.

[0336] "Terminal" refers to the electronic device used by the User to input necessary information and check the study plan, progress, recovery plan and suggested schools of choice.

[0337] "Server" refers to the central processing unit that receives and analyzes data sent from the terminal, generates study plans and recovery plans, monitors progress, and suggests preferred schools.

[0338] "Generative AI model" refers to an artificial intelligence model that analyzes learning progress, evaluates emotional state, and generates learning plans based on input data.

[0339] A "prompt sentence" refers to a specific sentence used to input instructions or questions to a generative AI model.

[0340] "Study Plan" refers to a personalized study schedule that is generated based on a user's characteristics, performance, and emotional state.

[0341] A "recovery plan" refers to additional learning tasks or stress reduction tasks that are adjusted as needed, taking into account the user's learning progress and emotional state.

[0342] "Emotion data" refers to information that indicates the user's emotional state, including stress and satisfaction during learning.

[0343] "Study results" refers to data that shows the results of a user's learning, such as test results and class grades.

[0344] "Suggesting a preferred school" refers to the act of the server recommending the most suitable preferred school based on the user's characteristics, grades, and emotional data.

[0345] "Dashboard format" refers to a format that visually displays a user's learning progress, emotional data, and feedback in an easy-to-understand manner.

[0346] This invention is a system to support preparation for junior high school entrance exams, monitoring the user's learning progress and emotional state, and providing a study plan and recovery plan based on this. This aims to reduce the user's burden while maximizing the learning effect.

[0347] User registration and initial settings

[0348] First, the user uses the device to input the necessary information, including the child's grade, current grades, preferred school, and emotional data, which is then sent to the server.

[0349] Data collection and analysis

[0350] The server registers the received user information and creates a user profile. The server also receives quizzes and class results provided by the cram school and analyzes them using a generative AI model. Furthermore, the server uses the user's emotional data to adjust study plans and recovery plans.

[0351] Generate a lesson plan

[0352] The server uses a generative AI model to generate a personalized learning plan based on the user's characteristics, grades, and emotional state, which is then sent to the user's device and displayed to them.

[0353] Learning progression and monitoring

[0354] The user studies daily according to the study plan and inputs their learning progress into the device. The device then sends this progress data and emotional data to the server, which monitors and evaluates the progress data and emotional data.

[0355] Feedback and Recovery Plan

[0356] The server evaluates the user's progress based on the latest learning progress data and emotional data, and generates a recovery plan if necessary. The recovery plan is sent to the device and displayed to the user. For example, if the user is feeling stressed, adjustments may be made, such as adding tasks to help them relax.

[0357] Suggestion of desired school

[0358] The server will suggest the most suitable school to apply to based on the user's characteristics, grades, progress, and emotional data. These suggestions are also sent to the device and displayed to the user. It is also possible to select a school that offers a less stressful environment based on emotional data.

[0359] Dashboard View

[0360] The device displays the user's progress, feedback, and emotional data in a dashboard format, allowing the user to see at a glance their emotional changes as well as their learning progress.

[0361] Specific examples

[0362] For example, if a user inputs their child's grades and emotions and indicates their preference for Junior High School A, the server creates a profile based on the user's input. The server analyzes test results provided by the cram school, uses a generative AI model to identify that the child is weak in science, and uses an emotion engine to recognize that the child experiences high stress while studying science. The server then generates a study plan that includes 30 minutes of daily science review and adds a weekly relaxation task to reduce stress. This is sent to the device, and the user follows the study plan and enters their progress and emotional state into the device. The server monitors their progress and emotional state and provides a recovery plan as needed. The server also suggests Junior High School B as a suitable option, sending this information to the device and displaying it to the user.

[0363] Prompt Sentence Examples

[0364] Examples of prompts to input to a generative AI model include:

[0365] "Your fifth-grade child is performing poorly in science, and emotional data indicates high levels of stress. Please create an appropriate learning plan and recovery plan to reduce stress for this child."

[0366] As described above, this system works in cooperation with the user, terminal, and server to support the user's learning progress and emotional state.

[0367] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0368] Step 1:

[0369] The user uses the terminal to input necessary information such as the child's grade, current grades, desired school, emotional data, etc. Once this input is complete, the terminal transmits this data to the server.

[0370] Step 2:

[0371] The server creates a user profile based on the user information received from the terminal, and registers the user's basic information and initial settings in the database.

[0372] Step 3:

[0373] The server receives quiz and class result data provided by the cram school. This data includes grades for each subject and learning progress. The server inputs this data into the generative AI model.

[0374] Step 4:

[0375] The generative AI model analyzes the learning results and emotional data it receives. The model analyzes grades for each subject and the user's emotional state, such as stress and satisfaction. The analysis results are stored on the server.

[0376] Step 5:

[0377] Based on the analysis results of the AI ​​model, the server generates an individualized study plan that takes into account the user's characteristics, grades, and emotional state. The study plan includes study time and review content for each subject, as well as stress reduction tasks according to emotions.

[0378] Step 6:

[0379] The server sends the generated lesson plan to the terminal, which receives the lesson plan and displays it to the user.

[0380] Step 7:

[0381] The user studies daily based on a study plan. After studying, the user inputs their study progress and emotional state into the device. The input data is sent from the device to the server.

[0382] Step 8:

[0383] The server receives and monitors the progress and emotion data sent by the user, and evaluates the user's learning and emotional fluctuations based on the monitoring results.

[0384] Step 9:

[0385] Based on the progress and emotional state assessment, the server generates a recovery plan as needed, which may include additional learning tasks or relaxation tasks to reduce stress.

[0386] Step 10:

[0387] The server sends the generated recovery plan to the terminal, which receives the recovery plan and displays it to the user.

[0388] Step 11:

[0389] The server selects the most suitable school based on the user's characteristics, grades, progress, and emotional data, and generates a proposal of the school. The proposal is sent from the server to the terminal.

[0390] Step 12:

[0391] The device displays the suggested schools to the user, who can then review them and use them as a reference for making a selection.

[0392] Step 13:

[0393] The device displays the user's learning progress, emotional data, and feedback in the form of a dashboard, allowing the user to visually check their learning progress and emotional fluctuations.

[0394] Specific input and output examples

[0395] For example, suppose a user inputs grade data from their fifth grade (Japanese: 80 points, Math: 70 points, Science: 60 points) and emotional data indicating that they feel high stress while studying science. This data is sent from the device to the server. The server analyzes this data and generates a study plan that includes "30 minutes of science review every day" and "a relaxation task once a week." This study plan is sent to the device and displayed to the user.

[0396] Prompt Sentence Examples

[0397] The prompt to the generative AI model is as follows:

[0398] "Your fifth-grade child is performing poorly in science, and emotional data indicates high levels of stress. Please create an appropriate learning plan and recovery plan to reduce stress for this child."

[0399] Through the above steps, this system works in cooperation with the user, terminal, and server to effectively support the user's learning progress and emotional state.

[0400] (Application example 2)

[0401] 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."

[0402] There is a growing need for a system that can effectively manage the learning progress and emotional fluctuations of junior high school entrance exam students and provide individually optimized learning plans. Conventional methods have made it difficult to timely monitor learning progress and emotional fluctuations and appropriately adjust learning and recovery plans based on that information. Furthermore, the lack of an environment for receiving real-time learning support has made it difficult to reduce students' stress and improve their learning efficiency.

[0403] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0404] In this invention, the server includes a means for monitoring the user's emotional state and transmitting emotional data to the server, a means for adjusting a study plan or recovery plan based on the emotional data, and a means for the terminal to access a virtual store through smart glasses or a head-mounted display and provide learning content in real time. This makes it possible to comprehensively manage the learning progress and emotional state of junior high school entrance exam students and provide individually optimized real-time learning support.

[0405] "Users" refers to junior high school entrance exam students and their parents, who are the entities that use the system.

[0406] "Necessary information" refers to data necessary to generate a study plan and recovery plan, such as grade level, current grades, desired school, and emotional data.

[0407] "Server" means a data processing device that receives, registers, and analyzes data sent by users, and generates and adjusts learning plans and recovery plans.

[0408] "Device" refers to a device used by a user, such as a smartphone, tablet, or PC, that provides an interface for displaying and inputting learning plans and learning data.

[0409] "Generative AI model" refers to an artificial intelligence algorithm that analyzes received data and generates learning and recovery plans.

[0410] A "study plan" is a personalized study schedule and tasks that is generated based on a user's performance data and emotional data.

[0411] A "recovery plan" is a complementary learning plan created to improve learning progress or emotional state when there is a problem.

[0412] "Emotional state" refers to data that indicates the user's mental state, such as stress or excitement, during learning.

[0413] A "virtual store" is a virtual learning environment accessible over the internet that provides real-time learning content through smart glasses or head-mounted displays.

[0414] "Smart glasses" are glasses-type devices that have a built-in display and camera that display information and provide users with an augmented reality (AR) experience.

[0415] A "head-mounted display" is a display device that users can wear to experience virtual reality (VR) and augmented reality (AR).

[0416] "Learning support" refers to a series of support services that monitor the user's learning progress and emotional state and provide optimal learning and recovery plans.

[0417] The present invention is a system that comprehensively manages the learning progress and emotional state of junior high school entrance exam students, providing individually optimized real-time learning support. In this system, the user inputs necessary information, and the server generates and adjusts learning plans and recovery plans based on that information, and provides them to the user via a terminal. Detailed embodiments of the present invention are described below.

[0418] User registration and initial settings

[0419] Users use devices such as smartphones or PCs to input the necessary information, including their grade, current grades, preferred schools, and emotional data. To access the virtual store, users wear smart glasses or a head-mounted display. This information is then sent to a server via the Internet.

[0420] Data collection and analysis

[0421] The server registers the received user information and creates a user profile. At the same time, the server receives data such as quizzes and class results from cram schools and online learning platforms. The generative AI model analyzes this data and identifies the user's weaknesses and strengths. Furthermore, emotion recognition sensors built into the smart glasses or head-mounted display are used to collect user emotion data.

[0422] Generate and deliver lesson plans

[0423] The server uses a generative AI model to automatically generate an individualized study plan based on the user's performance and emotional data. This study plan includes study tasks to address the user's weaknesses and additional tasks to improve performance. The generated study plan is sent to the user's device via the Internet and displayed to the user.

[0424] Learning progression and monitoring

[0425] The user follows the generated study plan as they study daily. Their learning progress and emotional state are entered into the device and periodically sent to the server. The server monitors this data and evaluates their progress. If necessary, a recovery plan is automatically generated and sent to the device.

[0426] Feedback and Recovery Plan

[0427] The server evaluates the progress and emotion data and generates a recovery plan as needed. The recovery plan is adjusted if the user is feeling stressed or if progress is slowing down. This recovery plan is also displayed on the device and provided to the user.

[0428] Suggestion of desired school

[0429] The server suggests the most suitable school to apply to based on the user's characteristics, grades, progress, and emotional data. The suggested schools are sent to the user's device and notified to the user. For example, if the user is in an environment where they are prone to stress, the server can suggest schools that are predicted to be less stressful.

[0430] Use of virtual stores

[0431] Users can access real-time learning content by wearing smart glasses or a head-mounted display and accessing a virtual store. This content is provided by a server and supports the user's learning progress.

[0432] Examples and prompts

[0433] For example, if a user inputs their child's grades and emotions from their fifth-grade elementary school and indicates their preference for a specific junior high school, the server will create a profile based on this data. The server analyzes test results provided by the cram school, using a generative AI model to identify that the child is weak in a particular subject, and an emotion engine to recognize that the child is experiencing high levels of stress while studying that subject. The server then generates a study plan that includes "30 minutes of daily review" and adds a weekly relaxation task. This is then sent to the device, and the user enters their progress and emotional state according to the study plan.

[0434] An example prompt is, "Generate the best individualized learning and recovery plan based on the student's current grades, learning progress, and emotional data."

[0435] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0436] Step 1:

[0437] The user uses a terminal to input necessary information (grade, grades, desired school, emotional data, etc.). This input data is sent to a server via the Internet. For example, the input information might be a current grade of 80 points and the desired school being A Junior High School, and the output is user profile data sent to the server.

[0438] Step 2:

[0439] The server registers the received user information and creates a user profile. The received data is stored on the server and stored in various databases in preparation for analysis by the generative AI model. The input is the user's basic information and performance information, and the output is structured user profile data.

[0440] Step 3:

[0441] The server receives data such as quiz and class results from cram schools and online learning platforms. This learning data is combined with the user's grade data and analyzed. The input is the quiz results and class content data, and the output is data for analysis that is stored in a database on the server.

[0442] Step 4:

[0443] The server uses a generative AI model to analyze the user's performance data and emotional data to identify the user's weaknesses and strengths. The generative AI model processes the received data as input and analyzes learning patterns and emotional fluctuations. The output is a list of each user's weak and strong subjects.

[0444] Step 5:

[0445] The server automatically generates an individualized study plan based on the analysis results. The generative AI model takes into account the user's grades and emotional state to create the optimal study plan. For example, if the user is determined to have a weak point in math, the study plan will include "reviewing math for 30 minutes every day." The input is the analyzed grade data and emotional data, and the output is an individualized study plan.

[0446] Step 6:

[0447] The server sends the generated lesson plan to the terminal via the Internet. The lesson plan is displayed on the user's terminal and lists specific learning tasks. The input is the generated lesson plan, and the output is the lesson plan displayed on the user's terminal.

[0448] Step 7:

[0449] The user uses the device to follow the learning plan and input their learning progress and emotional state. The device collects this data and periodically transmits it to the server. The input is the learning progress data and emotional data entered by the user, and the output is the progress data transmitted to the server.

[0450] Step 8:

[0451] The server monitors progress data and emotion data to evaluate the progress. If there are delays in progress or emotional fluctuations, the generative AI model automatically generates a recovery plan. The input is learning progress data and emotion data, and the output is a recovery plan.

[0452] Step 9:

[0453] The server sends the generated recovery plan to the device via the Internet. The recovery plan is displayed on the device in the same way as a learning plan. For example, if the user is feeling stressed, the recovery plan will include tasks for relaxation. The input is the generated recovery plan, and the output is the recovery plan displayed on the user's device.

[0454] Step 10:

[0455] The server suggests the most suitable schools to apply to based on the user's characteristics, grades, and progress data. The generative AI model analyzes the data and lists the most suitable schools for the user. The input is the user's comprehensive data, and the output is a list of preferred schools.

[0456] Step 11:

[0457] The server sends the information on the schools it recommends to the terminal. The terminal displays this information to the user, including detailed information about the schools and their advantages and disadvantages. The input is the school recommendation data from the server, and the output is a list of schools displayed on the user's terminal.

[0458] Step 12:

[0459] Users wear smart glasses or a head-mounted display and access the virtual store, where they can receive real-time learning content and progress with their studies. The input is the device worn by the user, and the output is the real-time learning content provided by the virtual store.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] [Second embodiment]

[0464] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0465] 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.

[0466] 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).

[0467] 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.

[0468] 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.

[0469] 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).

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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.

[0474] 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.

[0475] 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."

[0476] This invention is a system in which a user inputs necessary information, and a server generates a study plan based on that information, monitors progress, and provides a recovery plan as needed. This system is designed to support users in taking junior high school entrance exams, with the aim of reducing the burden on parents and maximizing the learning effect of their children.

[0477] This system operates in the following steps:

[0478] User registration and initial settings

[0479] First, the user uses a terminal to input the necessary information, including the child's grade, current grades, desired school, etc. This input data is then sent from the terminal to the server.

[0480] Data collection and analysis

[0481] The server registers the received user information and creates a user profile. The server also receives quizzes and class results provided by the cram school. This data is analyzed by the generative AI model to identify the child's weaknesses and strengths.

[0482] Generate a lesson plan

[0483] The server uses the generative AI model to generate a personalized learning plan based on the user's characteristics and performance, which is then sent to the user's device and displayed to them.

[0484] Learning progression and monitoring

[0485] The user studies daily according to the study plan and enters their progress into the device. The device then sends this progress data to the server, which monitors and evaluates the progress data.

[0486] Feedback and Recovery Plan

[0487] The server reanalyzes the latest learning data and past performance data and generates a recovery plan as needed, which is then sent to the device and displayed to the user.

[0488] Suggestion of desired school

[0489] The server then suggests the best schools to apply to based on the user's characteristics, grades, and progress. These suggestions are also sent to the device and displayed to the user.

[0490] Dashboard View

[0491] The device displays the user's progress and feedback in the form of a dashboard, allowing the user to see their learning progress at a glance.

[0492] Specific examples

[0493] For example, if a user inputs the grade data of their fifth-grade child and indicates their preference for Junior High School A, the server creates a profile based on the information the user inputs. The server analyzes test results provided by the cram school and uses a generative AI model to identify that the child is weak in "science." The server then generates a study plan that includes "30 minutes of science review every day" and sends it to the device. The user follows this study plan and enters their progress on the device. The server monitors their progress and provides a recovery plan as needed. The server also suggests to the user that "Junior High School B" is also suitable, and sends this information to the device and displays it to the user.

[0494] As described above, this system works in cooperation with the user, terminal, and server to support children's learning.

[0495] The processing flow will be explained below.

[0496] Program processing steps

[0497] Step 1: User registration and initial setup

[0498] 1.1 The user selects the "New Registration" option from the initial screen of the device.

[0499] Specific operation: The user enters information such as "name," "grade," "current grades," and "desired school" into the input form.

[0500] 1.2 The terminal sends the entered user information to the server.

[0501] Specific operation: The terminal encodes the input information into JSON format and sends a POST request to the server's API endpoint.

[0502] 1.3 The server registers the received user information in a database and creates a user profile.

[0503] Specific behavior: The server parses the received JSON data and saves it as a new user entry in the database.

[0504] Step 2: Collect and analyze data

[0505] 2.1 The server receives quizzes and class results provided by the cram school.

[0506] Specific operation: The server periodically retrieves test result data from the cram school's system via API.

[0507] 2.2 The server uses the generated AI model to analyze the test results and user performance data.

[0508] Specific operation: The server inputs the acquired test data into the generative AI model to analyze weaknesses and extract areas of strength.

[0509] Step 3: Generate a lesson plan

[0510] 3.1 The server generates an individualized learning plan based on the user's characteristics and performance data.

[0511] Specific operation: The server prioritizes and schedules learning tasks based on the user's grades and the question trends of the school of their choice.

[0512] 3.2 The server sends the generated learning plan to the device.

[0513] Specific operation: The server encodes the learning plan into JSON format and sends it to the device's API endpoint.

[0514] 3.3 The device visualizes the learning plan and displays it to the user.

[0515] Specific operation: The device displays the received study plan in calendar or list format and allows the user to confirm it.

[0516] Step 4: Progressing and monitoring your learning

[0517] 4.1 The user progresses through daily learning and inputs their learning progress into the device.

[0518] Specific operation: When the user finishes learning, he / she selects the content he / she learned on the "Learning Progress" screen of the device and presses the "Complete" button.

[0519] 4.2 The device sends the learning progress data to the server.

[0520] Specific operation: The device encodes the learning progress data entered by the user into JSON format and sends it to the server.

[0521] Step 5: Feedback and recovery plan

[0522] 5.1 The server evaluates the progress based on the latest learning progress data.

[0523] Specific operation: The server inputs new learning progress data into the generative AI model and evaluates the user's progress.

[0524] 5.2 The server generates a recovery plan as needed.

[0525] Specific operation: Based on the progress assessment results, the server generates a recovery plan for the required reinforcement.

[0526] 5.3 The server sends the recovery plan to the device.

[0527] Specific operation: The server encodes the generated recovery plan into JSON format and sends it to the terminal.

[0528] 5.4 The terminal visualizes the recovery plan and displays it to the user.

[0529] Specific operation: The device will display the received recovery plan in a pop-up notification or on the dashboard.

[0530] Step 6: Propose your preferred school

[0531] 6.1 The server suggests the most suitable schools to apply to based on the user's characteristics, grades, and progress data.

[0532] Specific operation: The server inputs learning data and past data into the generative AI model and creates a list of the most suitable schools for the user.

[0533] 6.2 The server sends the school preference suggestions to the device.

[0534] Specific operation: The server encodes the school preference proposals into JSON format and sends them to the device.

[0535] 6.3 The device displays school preference suggestions to the user.

[0536] Specific operation: The device displays the received list of preferred schools on the screen and provides the characteristics of each school and exam information.

[0537] Step 7: View the dashboard

[0538] 7.1 The device displays user progress and feedback in a dashboard format.

[0539] How it works: The device displays the latest learning data, progress reports, feedback, and information about the school of choice all on one screen.

[0540] As a result, this system meticulously monitors the user's learning progress and provides effective study plans and appropriate recovery plans, thereby reducing the burden on parents and providing effective support for their children's junior high school entrance exams.

[0541] Example 1

[0542] 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."

[0543] In today's educational environment, it is extremely difficult for parents to effectively manage and support their children's learning progress. Particularly when it comes to exam preparation and improving grades, accurately identifying a child's weaknesses and creating an effective learning plan to overcome those weaknesses requires advanced expertise and continuous monitoring. However, it is not realistic for parents to do this manually in their busy daily lives, and it places a significant burden on them. To solve this problem, a system is needed that can efficiently and effectively automatically generate learning plans and track and improve progress.

[0544] 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.

[0545] In this invention, the server includes: means for a user to input required information; means for transmitting the input information to the server; means for registering the received data and creating a user profile; means for receiving and analyzing assessment data provided by educational institutions and utilizing a generative AI model; means for identifying the user's weaknesses and strengths and generating an individualized learning plan; means for transmitting the generated learning plan to a terminal; means for the terminal to display the learning plan to the user; means for the user to input learning progress into the terminal and transmit the progress data to the server; means for evaluating progress and generating a recovery plan; means for transmitting the recovery plan to the terminal; means for the terminal to display the recovery plan; means for suggesting preferred schools based on the user's characteristics, grades, and progress; means for transmitting the preferred school suggestions to the terminal; means for the terminal to display the preferred school suggestions to the user; means for the terminal to display the user's progress and feedback in a dashboard format; and means for generating prompt sentences using a generative AI model to generate an optimal learning plan or recovery plan based on the child's learning progress. This enables users to efficiently and effectively automate a series of learning support tasks, such as creating a learning plan, monitoring progress, providing a recovery plan, and suggesting preferred schools.

[0546] "User" refers to an individual who uses this system to receive services such as creating a study plan, monitoring progress, providing a recovery plan, and suggesting preferred schools.

[0547] "Terminal" refers to a device that a user operates to input information and receive feedback and suggestions from the server, and specifically includes smartphones, tablets, and PCs.

[0548] "Server" refers to a computer system that receives information sent by a user, creates a user profile based on this information, generates a study plan, recovery plan, and suggestions for preferred schools, and sends these to the terminal.

[0549] A "generative AI model" is an artificial intelligence model used by the server to analyze user information and learning data to generate learning plans and recovery plans.

[0550] A "prompt sentence" is an input sentence used to give instructions to a generative AI model, and is used to specify how to generate a specific learning plan or recovery plan.

[0551] A "study plan" refers to a schedule of learning content that is individually created taking into account the user's weaknesses and strengths, and includes specific learning tasks aimed at improving a child's academic ability.

[0552] A "recovery plan" refers to a supplementary learning plan or specific improvement measures that are deemed necessary as a result of analyzing a user's learning progress and performance data.

[0553] "School of choice suggestions" refers to the server recommending the most suitable school based on the user's characteristics, grades, and progress, and includes information for selecting the school that best suits the user's wishes and abilities.

[0554] "Dashboard" refers to an interface that displays on the device a user's learning progress, feedback, recovery plans, suggested schools of choice, and more, all at a glance.

[0555] "User profile" refers to a data set that includes detailed information such as the user's grade, grades, and preferred school, which is created by the server based on information received from the user.

[0556] "Evaluation data" refers to data used to evaluate a user's learning status, such as quizzes and class results provided by educational institutions.

[0557] This system allows users to input necessary information, and a server generates a study plan based on that information, monitors progress, and provides recovery plans as needed. It is designed to support users preparing for junior high school entrance exams, reducing the burden on parents and maximizing the learning outcomes of their children.

[0558] User registration and initial settings

[0559] The user uses a device to input information such as their child's grade, current grades, and desired school. This input data is sent from the device to a server. The device can be a smartphone, tablet, or PC.

[0560] Data collection and analysis

[0561] The server creates a user profile based on the received user information. The server also receives data from educational institutions, such as quizzes and class results, and stores them in a database. This data is analyzed using a generative AI model (e.g., "OpenAI GPT-4") to identify the child's weaknesses and strengths. The server sends the following prompt to the generative AI model:

[0562] text

[0563] Analyze this user's learning data to identify their weaknesses and strengths.

[0564] Generate a lesson plan

[0565] The server uses the generative AI model to generate a personalized learning plan based on the user's characteristics and performance data. This learning plan is sent to the user's device and displayed to the user. The specific prompt is as follows:

[0566] text

[0567] My fifth-grader wants to attend Junior High School A, but he is not good at science. Please create a specific study plan to help him overcome his science weaknesses.

[0568] Learning progression and monitoring

[0569] The user studies daily according to the generated study plan and enters their progress into the device. The entered progress data is sent from the device to the server. The server receives the progress data and evaluates the progress using a monitoring system.

[0570] Feedback and Recovery Plan

[0571] The server re-analyzes the latest learning data and past performance data received and generates a recovery plan as necessary. The generated recovery plan is sent to the user's device and displayed to the user. The specific prompt is as follows:

[0572] text

[0573] Please re-analyze this user's learning data and generate the necessary recovery plan.

[0574] Suggestion of desired school

[0575] The server then proposes the best schools to apply to based on the user's characteristics, grades, and progress data. These proposals are also sent from the server to the device and displayed to the user.

[0576] Dashboard View

[0577] The device displays the user's progress and feedback in the form of a dashboard, allowing the user to see their learning progress at a glance.

[0578] Specific examples

[0579] For example, let's say a user inputs the grade data of their child in the fifth grade of elementary school and indicates that they would like to attend Junior High School A. The user inputs the child's grade, grades, and desired school information into their device and submits it. The server receives this data, and the generative AI model analyzes it to determine that the child is "weak in science." The server then sends the following prompt to the generative AI model and obtains the result:

[0580] text

[0581] My fifth-grader wants to attend Junior High School A, but he is not good at science. Please create a specific study plan to help him overcome his science weaknesses.

[0582] Based on this prompt, the server generates a specific study plan, including "30 minutes of science review every day," and sends it to the device. The user studies according to this study plan and enters their progress into the device. The progress data is sent to the server, which monitors the progress and generates a recovery plan as necessary. Based on the progress data, the server suggests that "Junior High School B" is also suitable, and sends this information to the device and displays it to the user.

[0583] As described above, this system works in cooperation with the user, terminal, and server to support children's learning.

[0584] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0585] System program processing flow

[0586] Step 1: User registration and initial setup

[0587] Specific actions

[0588] Input: The user inputs information such as the child's grade, current grades, and desired school into the terminal.

[0589] How it works: Data entered by the user is sent from the device to the server. When the "Send" button is pressed using the device's application, this data is sent to the server.

[0590] Output: User information data received by the server.

[0591] Step 2: Collect and analyze data

[0592] Specific actions

[0593] Input: The server receives user input data and also receives quiz and class result data from the educational institution.

[0594] How it works: The server creates a user profile based on the data it receives and registers it in a database. It then inputs the received evaluation data into a generative AI model for analysis, identifying the child's weaknesses and strengths. Specifically, it sends the following prompt to the generative AI model:

[0595] text

[0596] Analyze this user's learning data to identify their weaknesses and strengths.

[0597] Output: The analysis results obtained from the generative AI model (child's weaknesses and strengths).

[0598] Step 3: Generate a lesson plan

[0599] Specific actions

[0600] Input: The analysis results received by the server from the generative AI model and the user's initial input data.

[0601] How it works: The server uses a generative AI model to generate a personalized learning plan based on this data. It sends specific prompts to the generative AI model:

[0602] text

[0603] My fifth-grader wants to attend Junior High School A, but he is not good at science. Please create a specific study plan to help him overcome his science weaknesses.

[0604] Output: The generated learning plan. This learning plan is sent from the server to the device.

[0605] Step 4: Progressing and monitoring your learning

[0606] Specific actions

[0607] Input: The user inputs their daily learning progress into the terminal.

[0608] How it works: The user enters progress information into the application on the device and presses the "Send progress" button. This data is sent from the device to the server. The server receives the progress data and evaluates the progress in the monitoring system.

[0609] Output: Learning progress data sent to the server. Evaluation results based on the progress data on the server.

[0610] Step 5: Feedback and recovery plan

[0611] Specific actions

[0612] Input: The server provides the latest learning progress data and past grade data.

[0613] How it works: The server re-analyzes this data and generates a recovery plan if necessary. It sends specific prompts to the generative AI model:

[0614] text

[0615] Please re-analyze this user's learning data and generate the necessary recovery plan.

[0616] Output: The generated recovery plan, which the server sends to the device.

[0617] Step 6: Propose your preferred school

[0618] Specific actions

[0619] Input: The server collects user characteristics, performance, and progress data.

[0620] How it works: The server uses this data to suggest the best schools to apply to. The suggestions are sent from the server to the device and displayed to the user.

[0621] Output: Proposed data for desired schools.

[0622] Step 7: View the dashboard

[0623] Specific actions

[0624] Input: Progress, feedback, recovery plan, and suggested school of choice data sent from the server.

[0625] How it works: The device receives this data and displays it in a dashboard format, allowing users to check their learning progress at a glance.

[0626] Output: Dashboard showing progress, feedback, recovery plan, and school preference suggestions.

[0627] These steps allow the user, terminal, and server to work together to create a system that supports children's learning.

[0628] (Application example 1)

[0629] 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."

[0630] In conventional risk management systems, risk assessment and progress monitoring were not fully automated, requiring personnel to manually organize and understand data. As a result, risk management efficiency was reduced and it was difficult to provide recovery plans quickly. In addition, progress could not be grasped in real time, resulting in delays in risk countermeasures.

[0631] 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.

[0632] In this invention, the server includes means for receiving risk information input by a user and generating a risk management plan using a generative AI model, means for evaluating risk management progress data and generating a recovery plan as necessary, and means for proposing appropriate risk management measures based on the user's risk information and progress. This enables automatic collection and analysis of risk information, automatic generation of management plans, real-time progress monitoring, and rapid provision of recovery plans.

[0633] Key Word Definitions:

[0634] "User"

[0635] is a person or organization that uses a system to input information and receive results.

[0636] "Required Information"

[0637] This refers to data necessary for risk management, such as company information, types of risks, and past security incidents.

[0638] "Terminal"

[0639] is an electronic device used by a user to input information and receive feedback from a system, including smartphones and tablets.

[0640] "server"

[0641] It is a communications device that receives and registers data sent by users, generates risk management plans and recovery plans using generative AI models, and sends them back to the terminal.

[0642] "Generative AI model"

[0643] This refers to algorithms that use machine learning and artificial intelligence techniques to analyze data and generate risk management and recovery plans.

[0644] "Risk Information"

[0645] is data about risk factors that may affect a company or organization, which can be entered by users via a terminal.

[0646] "Risk Management Plan"

[0647] A plan is generated using a generative AI model, which evaluates the risks that users need to address and includes specific countermeasures based on the assessment.

[0648] "Progress Data"

[0649] is data recorded by the user on their progress according to the risk management plan, which is sent to the server and used for evaluation.

[0650] "Recovery Plan"

[0651] A plan is one that includes alternative responses if things do not go as planned or new risks arise.

[0652] Dashboard

[0653] is an interface that visually organizes and displays information so that users can grasp the progress and current status of risk management at a glance.

[0654] "Risk management measures"

[0655] These are specific measures or steps that can be taken to address specific risks, and are proposed by the server based on the user's risk information and progress.

[0656] MODE FOR CARRYING OUT THE INVENTION

[0657] User registration and initial settings

[0658] First, the user uses a terminal to input the necessary information into the input form. This information includes company data related to risk management, risk types, past security incidents, etc. The input information is then sent from the terminal to the server. The terminal can be a smartphone or tablet and provides a user-friendly interface.

[0659] Data collection and analysis

[0660] The server registers the received user information and creates a user profile. The server also receives risk information and incident data from external sources. This data is analyzed by a generative AI model to classify and identify risk factors. The generative AI model is optimized using machine learning and artificial intelligence techniques.

[0661] Generate a risk management plan

[0662] The server uses the generative AI model to generate a personalized risk management plan based on the user's information. The plan includes specific countermeasures and a progress schedule. The risk management plan is then sent from the server to the device and displayed to the user.

[0663] Management Progression and Monitoring

[0664] Users report progress according to the risk management plan. They use their terminals to input progress and the status of countermeasure implementation, and send this to the server. The server monitors the progress data and evaluates the progress.

[0665] Feedback and Recovery Plan

[0666] The server re-analyzes the latest progress data and past risk data and generates a recovery plan as needed. This recovery plan may include additional or new countermeasures for specific risks. The generated recovery plan is sent to the terminal and displayed to the user.

[0667] Proposal of risk management measures

[0668] The server proposes optimal risk management measures based on the user's characteristics, risk information, and progress data, and these proposals are also sent to the terminal and displayed to the user.

[0669] Dashboard View

[0670] The device is equipped with a built-in dashboard displaying user progress and feedback, allowing users to see their risk management progress at a glance.

[0671] Specific examples

[0672] For example, when a user performs security risk management for "Small and Medium-sized Enterprise A," they first enter company information, risk types, and past incident data. The server then uses a generative AI model to create a risk profile for "Small and Medium-sized Enterprise A" and generates a risk management plan. This plan includes specific measures such as "30 minutes of security checks every day" and "data backup once a week." The user reports their progress according to this plan, and the server monitors the progress data. If progress is delayed, the server generates a new recovery plan and sends it to the device.

[0673] Examples of prompt statements

[0674] Here are some example prompts for a generative AI model:

[0675] "Our company is a small to medium-sized enterprise with 50 employees. We have experienced a data breach in the past. We are currently paying particular attention to phishing attacks and internal fraud risks."

[0676] In this way, the user can manage risk efficiently and effectively.

[0677] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0678] Program processing steps

[0679] Processing Steps

[0680] Step 1:

[0681] The user uses a terminal to input the required information, such as company information, risk type, and past security incidents. After the user enters this information into a form, the terminal sends the data to the server. This input data is used as the basis for generating a risk management plan.

[0682] Step 2:

[0683] The server registers the received data and creates a user profile. The server stores the received information in a database and organizes it as a user profile. It also acquires external risk information and past incident data and adds them to the database for analysis by the generative AI model. The input is user information sent from the device, and the output is the generation of a user profile.

[0684] Step 3:

[0685] The server uses a generative AI model to generate a risk management plan. The generative AI model analyzes risk factors based on the input user profile and risk data. This analysis generates an individual risk management plan. The generated plan includes specific countermeasures and progress schedules. The input is the user profile and risk data, and the output is a risk management plan.

[0686] Step 4:

[0687] The server sends the generated risk management plan to the terminal. The terminal displays this risk management plan to the user. The user starts to implement risk management based on the displayed plan. The input is the risk management plan, and the output is the display to the user.

[0688] Step 5:

[0689] The user reports progress according to the risk management plan. Using a terminal, the user inputs the progress status and the implementation status of countermeasures, and sends this data to the server. The input is the user's progress status data, and the output is the transmission of the progress data to the server.

[0690] Step 6:

[0691] The server monitors the progress data and evaluates the progress. The server analyzes the received progress data and identifies the progress and problems of risk management. If necessary, it generates a recovery plan. The input is the progress data, and the output is the generation of a recovery plan.

[0692] Step 7:

[0693] The server sends the generated recovery plan to the terminal, which displays it to the user. The recovery plan includes additional measures and new countermeasures for specific risks. The input is the recovery plan, and the output is the display to the user.

[0694] Step 8:

[0695] The server proposes appropriate risk management measures based on the user's risk information and progress. The generative AI model performs another analysis and proposes optimal management measures. The server sends this proposal to the terminal, which displays it to the user. The input is risk information and progress data, and the output is a risk management measure proposal.

[0696] Step 9:

[0697] The terminal displays the user's progress and feedback in the form of a dashboard. The dashboard displays progress, risk reviews, and feedback, allowing the user to check the situation in real time. The input is progress data and feedback data, and the output is the dashboard display.

[0698] 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.

[0699] This invention is a system to support preparation for junior high school entrance exams, monitoring the user's learning progress and emotional state, and providing a study plan and recovery plan based on this. This aims to reduce the user's burden while maximizing the learning effect.

[0700] This system has the basic functions of allowing users to input the necessary information, and then having the server generate a study plan based on that information, monitor progress, and provide a recovery plan if necessary. In addition, it also includes an emotion engine that recognizes the user's emotions.

[0701] User registration and initial settings

[0702] First, the user inputs the necessary information using the device, including the child's grade, current grades, preferred schools, and emotional data, which is then sent to the server.

[0703] Data collection and analysis

[0704] The server registers the received user information and creates a user profile. The server also receives quizzes and class results provided by the cram school and analyzes them using a generative AI model. Furthermore, the server uses the user's emotional data to adjust study plans and recovery plans.

[0705] Generate a lesson plan

[0706] The server uses a generative AI model to generate a personalized learning plan based on the user's characteristics, grades, and emotional state, which is then sent to the user's device and displayed to them.

[0707] Learning progression and monitoring

[0708] The user studies daily according to the study plan and inputs their learning progress into the device. The device then sends this progress data and emotional data to the server, which monitors and evaluates the progress data and emotional data.

[0709] Feedback and Recovery Plan

[0710] The server evaluates the user's progress based on the latest learning progress data and emotional data, and generates a recovery plan if necessary. The recovery plan is sent to the device and displayed to the user. For example, if the user is feeling stressed, adjustments may be made, such as adding tasks to help them relax.

[0711] Suggestion of desired school

[0712] The server will suggest the most suitable school to apply to based on the user's characteristics, grades, progress, and emotional data. These suggestions are also sent to the device and displayed to the user. It is also possible to select a school that offers a less stressful environment based on emotional data.

[0713] Dashboard View

[0714] The device displays the user's progress, feedback, and emotional data in a dashboard format, allowing the user to see at a glance their emotional changes as well as their learning progress.

[0715] Specific examples

[0716] For example, if a user inputs their child's grades and emotions and indicates their preference for Junior High School A, the server creates a profile based on the user's input. The server analyzes test results provided by the cram school, uses a generative AI model to identify that the child is weak in science, and uses an emotion engine to recognize that the child experiences high stress while studying science. The server then generates a study plan that includes 30 minutes of daily science review and adds a weekly relaxation task to reduce stress. This is sent to the device, and the user follows the study plan and enters their progress and emotional state into the device. The server monitors their progress and emotional state and provides a recovery plan as needed. The server also suggests Junior High School B as a suitable option, sending this information to the device and displaying it to the user.

[0717] As described above, this system works in cooperation with the user, terminal, and server to support the user's learning progress and emotional state.

[0718] The processing flow will be explained below.

[0719] Program processing steps

[0720] Step 1: User registration and initial setup

[0721] 1.1 The user selects the "New Registration" option from the initial screen of the device.

[0722] Specific operation: The user enters information such as "name," "grade," "current grades," "desired school," and "permission to collect emotional data" into an input form.

[0723] 1.2 The terminal sends the entered user information to the server.

[0724] Specific operation: The terminal encodes the input information into JSON format and sends a POST request to the server's API endpoint.

[0725] 1.3 The server registers the received user information in a database and creates a user profile.

[0726] Specific behavior: The server parses the received JSON data and saves it as a new user entry in the database.

[0727] Step 2: Collect and analyze data

[0728] 2.1 The server receives quizzes and class results provided by the cram school.

[0729] Specific operation: The server periodically retrieves test result data from the cram school's system via API.

[0730] 2.2 The server uses the generated AI model to analyze the test results and user performance data.

[0731] Specific operation: The server inputs the acquired test data into the generative AI model to analyze weaknesses and extract areas of strength.

[0732] 2.3 The device monitors the user's emotional state in real time and transmits the emotional data to the server.

[0733] Specific operation: The device uses the built-in camera and microphone to analyze the user's emotions from their facial expressions and voice, and sends the analysis results to the server.

[0734] Step 3: Generate a lesson plan

[0735] 3.1 The server generates an individualized learning plan based on the user's characteristics, performance data, and emotional data.

[0736] Specific operation: The server integrates the user's grades, exam trends at the school of choice, and emotional data to prioritize and schedule learning tasks.

[0737] 3.2 The server sends the generated learning plan to the device.

[0738] Specific operation: The server encodes the learning plan into JSON format and sends it to the device's API endpoint.

[0739] 3.3 The device visualizes the learning plan and displays it to the user.

[0740] Specific operation: The device displays the received study plan in calendar or list format and allows the user to confirm it.

[0741] Step 4: Progressing and monitoring your learning

[0742] 4.1 The user progresses through daily learning and inputs their learning progress into the device.

[0743] Specific operation: When the user finishes learning, he / she selects the content he / she learned on the "Learning Progress" screen of the device and presses the "Complete" button.

[0744] 4.2 The device sends learning progress data and emotion data to the server.

[0745] Specific operation: The device encodes the learning progress data entered by the user and the emotion data collected in real time into JSON format and sends it to the server.

[0746] Step 5: Feedback and recovery plan

[0747] 5.1 The server evaluates the progress based on the latest learning progress data and emotion data.

[0748] Specific operation: The server inputs new learning progress data and emotion data into the generative AI model and evaluates the user's progress.

[0749] 5.2 The server generates a recovery plan as needed.

[0750] Specific operation: Based on the progress assessment results, the server generates a recovery plan for the necessary reinforcement. For example, if the user is feeling stressed about a particular subject, the server will add tasks to reinforce that subject as well as tasks to help them relax.

[0751] 5.3 The server sends the recovery plan to the device.

[0752] Specific operation: The server encodes the generated recovery plan into JSON format and sends it to the terminal.

[0753] 5.4 The terminal visualizes the recovery plan and displays it to the user.

[0754] Specific operation: The device will display the received recovery plan in a pop-up notification or on the dashboard.

[0755] Step 6: Propose your preferred school

[0756] 6.1 The server suggests the best schools to apply to based on the user's characteristics, grades, progress data, and emotional data.

[0757] Specific operation: The server inputs learning data, past data, and emotional data into the generative AI model and creates a list of the most suitable schools for the user.

[0758] 6.2 The server sends the school preference suggestions to the device.

[0759] Specific operation: The server encodes the school preference proposals into JSON format and sends them to the device.

[0760] 6.3 The device displays school preference suggestions to the user.

[0761] Specific operation: The device displays the received list of preferred schools on the screen and provides the characteristics of each school and exam information.

[0762] Step 7: View the dashboard

[0763] 7.1 The device displays the user's progress, feedback, and emotional data in a dashboard format.

[0764] Specific operation: The device displays the latest learning data, progress reports, feedback, and emotional fluctuations on a single screen.

[0765] As a result, this system meticulously monitors the user's learning progress and emotional state, and provides effective study plans and appropriate recovery plans, thereby reducing the burden on parents and providing effective support for their children's junior high school entrance exams.

[0766] Example 2

[0767] 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."

[0768] In today's junior high school entrance exams, children are often exposed to hectic study schedules and pressure. This necessitates an efficient and effective learning management system. However, existing systems only monitor academic progress and do not take into account fluctuations in emotional states. This creates challenges, such as inadequate management of children's stress and anxiety, making it difficult to maximize overall learning outcomes.

[0769] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0770] In this invention, the server includes a means for creating a user profile, a means for receiving learning results and emotional data and utilizing a generative AI model for analyzing the data, and a means for generating an individual learning plan based on the user's characteristics and emotional data, thereby enabling a comprehensive learning support system that takes into account not only learning progress but also emotional states.

[0771] "User" refers to a person who utilizes the system to monitor their learning progress and emotional state and to receive learning and recovery plans.

[0772] "Terminal" refers to the electronic device used by the User to input necessary information and check the study plan, progress, recovery plan and suggested schools of choice.

[0773] "Server" refers to the central processing unit that receives and analyzes data sent from the terminal, generates study plans and recovery plans, monitors progress, and suggests preferred schools.

[0774] "Generative AI model" refers to an artificial intelligence model that analyzes learning progress, evaluates emotional state, and generates learning plans based on input data.

[0775] A "prompt sentence" refers to a specific sentence used to input instructions or questions to a generative AI model.

[0776] "Study Plan" refers to a personalized study schedule that is generated based on a user's characteristics, performance, and emotional state.

[0777] A "recovery plan" refers to additional learning tasks or stress reduction tasks that are adjusted as needed, taking into account the user's learning progress and emotional state.

[0778] "Emotion data" refers to information that indicates the user's emotional state, including stress and satisfaction during learning.

[0779] "Study results" refers to data that shows the results of a user's learning, such as test results and class grades.

[0780] "Suggesting a preferred school" refers to the act of the server recommending the most suitable preferred school based on the user's characteristics, grades, and emotional data.

[0781] "Dashboard format" refers to a format that visually displays a user's learning progress, emotional data, and feedback in an easy-to-understand manner.

[0782] This invention is a system to support preparation for junior high school entrance exams, monitoring the user's learning progress and emotional state, and providing a study plan and recovery plan based on this. This aims to reduce the user's burden while maximizing the learning effect.

[0783] User registration and initial settings

[0784] First, the user uses the device to input the necessary information, including the child's grade, current grades, preferred school, and emotional data, which is then sent to the server.

[0785] Data collection and analysis

[0786] The server registers the received user information and creates a user profile. The server also receives quizzes and class results provided by the cram school and analyzes them using a generative AI model. Furthermore, the server uses the user's emotional data to adjust study plans and recovery plans.

[0787] Generate a lesson plan

[0788] The server uses a generative AI model to generate a personalized learning plan based on the user's characteristics, grades, and emotional state, which is then sent to the user's device and displayed to them.

[0789] Learning progression and monitoring

[0790] The user studies daily according to the study plan and inputs their learning progress into the device. The device then sends this progress data and emotional data to the server, which monitors and evaluates the progress data and emotional data.

[0791] Feedback and Recovery Plan

[0792] The server evaluates the user's progress based on the latest learning progress data and emotional data, and generates a recovery plan if necessary. The recovery plan is sent to the device and displayed to the user. For example, if the user is feeling stressed, adjustments may be made, such as adding tasks to help them relax.

[0793] Suggestion of desired school

[0794] The server will suggest the most suitable school to apply to based on the user's characteristics, grades, progress, and emotional data. These suggestions are also sent to the device and displayed to the user. It is also possible to select a school that offers a less stressful environment based on emotional data.

[0795] Dashboard View

[0796] The device displays the user's progress, feedback, and emotional data in a dashboard format, allowing the user to see at a glance their emotional changes as well as their learning progress.

[0797] Specific examples

[0798] For example, if a user inputs their child's grades and emotions and indicates their preference for Junior High School A, the server creates a profile based on the user's input. The server analyzes test results provided by the cram school, uses a generative AI model to identify that the child is weak in science, and uses an emotion engine to recognize that the child experiences high stress while studying science. The server then generates a study plan that includes 30 minutes of daily science review and adds a weekly relaxation task to reduce stress. This is sent to the device, and the user follows the study plan and enters their progress and emotional state into the device. The server monitors their progress and emotional state and provides a recovery plan as needed. The server also suggests Junior High School B as a suitable option, sending this information to the device and displaying it to the user.

[0799] Prompt Sentence Examples

[0800] Examples of prompts to input to a generative AI model include:

[0801] "Your fifth-grade child is performing poorly in science, and emotional data indicates high levels of stress. Please create an appropriate learning plan and recovery plan to reduce stress for this child."

[0802] As described above, this system works in cooperation with the user, terminal, and server to support the user's learning progress and emotional state.

[0803] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0804] Step 1:

[0805] The user uses the terminal to input necessary information such as the child's grade, current grades, desired school, emotional data, etc. Once this input is complete, the terminal transmits this data to the server.

[0806] Step 2:

[0807] The server creates a user profile based on the user information received from the terminal, and registers the user's basic information and initial settings in the database.

[0808] Step 3:

[0809] The server receives quiz and class result data provided by the cram school. This data includes grades for each subject and learning progress. The server inputs this data into the generative AI model.

[0810] Step 4:

[0811] The generative AI model analyzes the learning results and emotional data it receives. The model analyzes grades for each subject and the user's emotional state, such as stress and satisfaction. The analysis results are stored on the server.

[0812] Step 5:

[0813] Based on the analysis results of the AI ​​model, the server generates an individualized study plan that takes into account the user's characteristics, grades, and emotional state. The study plan includes study time and review content for each subject, as well as stress reduction tasks according to emotions.

[0814] Step 6:

[0815] The server sends the generated lesson plan to the terminal, which receives the lesson plan and displays it to the user.

[0816] Step 7:

[0817] The user studies daily based on a study plan. After studying, the user inputs their study progress and emotional state into the device. The input data is sent from the device to the server.

[0818] Step 8:

[0819] The server receives and monitors the progress and emotion data sent by the user, and evaluates the user's learning and emotional fluctuations based on the monitoring results.

[0820] Step 9:

[0821] Based on the progress and emotional state assessment, the server generates a recovery plan as needed, which may include additional learning tasks or relaxation tasks to reduce stress.

[0822] Step 10:

[0823] The server sends the generated recovery plan to the terminal, which receives the recovery plan and displays it to the user.

[0824] Step 11:

[0825] The server selects the most suitable school based on the user's characteristics, grades, progress, and emotional data, and generates a proposal of the school. The proposal is sent from the server to the terminal.

[0826] Step 12:

[0827] The device displays the suggested schools to the user, who can then review them and use them as a reference for making a selection.

[0828] Step 13:

[0829] The device displays the user's learning progress, emotional data, and feedback in the form of a dashboard, allowing the user to visually check their learning progress and emotional fluctuations.

[0830] Specific input and output examples

[0831] For example, suppose a user inputs grade data from their fifth grade (Japanese: 80 points, Math: 70 points, Science: 60 points) and emotional data indicating that they feel high stress while studying science. This data is sent from the device to the server. The server analyzes this data and generates a study plan that includes "30 minutes of science review every day" and "a relaxation task once a week." This study plan is sent to the device and displayed to the user.

[0832] Prompt Sentence Examples

[0833] The prompt to the generative AI model is as follows:

[0834] "Your fifth-grade child is performing poorly in science, and emotional data indicates high levels of stress. Please create an appropriate learning plan and recovery plan to reduce stress for this child."

[0835] Through the above steps, this system works in cooperation with the user, terminal, and server to effectively support the user's learning progress and emotional state.

[0836] (Application example 2)

[0837] 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."

[0838] There is a growing need for a system that can effectively manage the learning progress and emotional fluctuations of junior high school entrance exam students and provide individually optimized learning plans. Conventional methods have made it difficult to timely monitor learning progress and emotional fluctuations and appropriately adjust learning and recovery plans based on that information. Furthermore, the lack of an environment for receiving real-time learning support has made it difficult to reduce students' stress and improve their learning efficiency.

[0839] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0840] In this invention, the server includes a means for monitoring the user's emotional state and transmitting emotional data to the server, a means for adjusting a study plan or recovery plan based on the emotional data, and a means for the terminal to access a virtual store through smart glasses or a head-mounted display and provide learning content in real time. This makes it possible to comprehensively manage the learning progress and emotional state of junior high school entrance exam students and provide individually optimized real-time learning support.

[0841] "Users" refers to junior high school entrance exam students and their parents, who are the entities that use the system.

[0842] "Necessary information" refers to data necessary to generate a study plan and recovery plan, such as grade level, current grades, desired school, and emotional data.

[0843] "Server" means a data processing device that receives, registers, and analyzes data sent by users, and generates and adjusts learning plans and recovery plans.

[0844] "Device" refers to a device used by a user, such as a smartphone, tablet, or PC, that provides an interface for displaying and inputting learning plans and learning data.

[0845] "Generative AI model" refers to an artificial intelligence algorithm that analyzes received data and generates learning and recovery plans.

[0846] A "study plan" is a personalized study schedule and tasks that is generated based on a user's performance data and emotional data.

[0847] A "recovery plan" is a complementary learning plan created to improve learning progress or emotional state when there is a problem.

[0848] "Emotional state" refers to data that indicates the user's mental state, such as stress or excitement, during learning.

[0849] A "virtual store" is a virtual learning environment accessible over the internet that provides real-time learning content through smart glasses or head-mounted displays.

[0850] "Smart glasses" are glasses-type devices that have a built-in display and camera that display information and provide users with an augmented reality (AR) experience.

[0851] A "head-mounted display" is a display device that users can wear to experience virtual reality (VR) and augmented reality (AR).

[0852] "Learning support" refers to a series of support services that monitor the user's learning progress and emotional state and provide optimal learning and recovery plans.

[0853] The present invention is a system that comprehensively manages the learning progress and emotional state of junior high school entrance exam students, providing individually optimized real-time learning support. In this system, the user inputs necessary information, and the server generates and adjusts learning plans and recovery plans based on that information, and provides them to the user via a terminal. Detailed embodiments of the present invention are described below.

[0854] User registration and initial settings

[0855] Users use devices such as smartphones or PCs to input the necessary information, including their grade, current grades, preferred schools, and emotional data. To access the virtual store, users wear smart glasses or a head-mounted display. This information is then sent to a server via the Internet.

[0856] Data collection and analysis

[0857] The server registers the received user information and creates a user profile. At the same time, the server receives data such as quizzes and class results from cram schools and online learning platforms. The generative AI model analyzes this data and identifies the user's weaknesses and strengths. Furthermore, emotion recognition sensors built into the smart glasses or head-mounted display are used to collect user emotion data.

[0858] Generate and deliver lesson plans

[0859] The server uses a generative AI model to automatically generate an individualized study plan based on the user's performance and emotional data. This study plan includes study tasks to address the user's weaknesses and additional tasks to improve performance. The generated study plan is sent to the user's device via the Internet and displayed to the user.

[0860] Learning progression and monitoring

[0861] The user follows the generated study plan as they study daily. Their learning progress and emotional state are entered into the device and periodically sent to the server. The server monitors this data and evaluates their progress. If necessary, a recovery plan is automatically generated and sent to the device.

[0862] Feedback and Recovery Plan

[0863] The server evaluates the progress and emotion data and generates a recovery plan as needed. The recovery plan is adjusted if the user is feeling stressed or if progress is slowing down. This recovery plan is also displayed on the device and provided to the user.

[0864] Suggestion of desired school

[0865] The server suggests the most suitable school to apply to based on the user's characteristics, grades, progress, and emotional data. The suggested schools are sent to the user's device and notified to the user. For example, if the user is in an environment where they are prone to stress, the server can suggest schools that are predicted to be less stressful.

[0866] Use of virtual stores

[0867] Users can access real-time learning content by wearing smart glasses or a head-mounted display and accessing a virtual store. This content is provided by a server and supports the user's learning progress.

[0868] Examples and prompts

[0869] For example, if a user inputs their child's grades and emotions from their fifth-grade elementary school and indicates their preference for a specific junior high school, the server will create a profile based on this data. The server analyzes test results provided by the cram school, using a generative AI model to identify that the child is weak in a particular subject, and an emotion engine to recognize that the child is experiencing high levels of stress while studying that subject. The server then generates a study plan that includes "30 minutes of daily review" and adds a weekly relaxation task. This is then sent to the device, and the user enters their progress and emotional state according to the study plan.

[0870] An example prompt is, "Generate the best individualized learning and recovery plan based on the student's current grades, learning progress, and emotional data."

[0871] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0872] Step 1:

[0873] The user uses a terminal to input necessary information (grade, grades, desired school, emotional data, etc.). This input data is sent to a server via the Internet. For example, the input information might be a current grade of 80 points and the desired school being A Junior High School, and the output is user profile data sent to the server.

[0874] Step 2:

[0875] The server registers the received user information and creates a user profile. The received data is stored on the server and stored in various databases in preparation for analysis by the generative AI model. The input is the user's basic information and performance information, and the output is structured user profile data.

[0876] Step 3:

[0877] The server receives data such as quiz and class results from cram schools and online learning platforms. This learning data is combined with the user's grade data and analyzed. The input is the quiz results and class content data, and the output is data for analysis that is stored in a database on the server.

[0878] Step 4:

[0879] The server uses a generative AI model to analyze the user's performance data and emotional data to identify the user's weaknesses and strengths. The generative AI model processes the received data as input and analyzes learning patterns and emotional fluctuations. The output is a list of each user's weak and strong subjects.

[0880] Step 5:

[0881] The server automatically generates an individualized study plan based on the analysis results. The generative AI model takes into account the user's grades and emotional state to create the optimal study plan. For example, if the user is determined to have a weak point in math, the study plan will include "reviewing math for 30 minutes every day." The input is the analyzed grade data and emotional data, and the output is an individualized study plan.

[0882] Step 6:

[0883] The server sends the generated lesson plan to the terminal via the Internet. The lesson plan is displayed on the user's terminal and lists specific learning tasks. The input is the generated lesson plan, and the output is the lesson plan displayed on the user's terminal.

[0884] Step 7:

[0885] The user uses the device to follow the learning plan and input their learning progress and emotional state. The device collects this data and periodically transmits it to the server. The input is the learning progress data and emotional data entered by the user, and the output is the progress data transmitted to the server.

[0886] Step 8:

[0887] The server monitors progress data and emotion data to evaluate the progress. If there are delays in progress or emotional fluctuations, the generative AI model automatically generates a recovery plan. The input is learning progress data and emotion data, and the output is a recovery plan.

[0888] Step 9:

[0889] The server sends the generated recovery plan to the device via the Internet. The recovery plan is displayed on the device in the same way as a learning plan. For example, if the user is feeling stressed, the recovery plan will include tasks for relaxation. The input is the generated recovery plan, and the output is the recovery plan displayed on the user's device.

[0890] Step 10:

[0891] The server suggests the most suitable schools to apply to based on the user's characteristics, grades, and progress data. The generative AI model analyzes the data and lists the most suitable schools for the user. The input is the user's comprehensive data, and the output is a list of preferred schools.

[0892] Step 11:

[0893] The server sends the information on the schools it recommends to the terminal. The terminal displays this information to the user, including detailed information about the schools and their advantages and disadvantages. The input is the school recommendation data from the server, and the output is a list of schools displayed on the user's terminal.

[0894] Step 12:

[0895] Users wear smart glasses or a head-mounted display and access the virtual store, where they can receive real-time learning content and progress with their studies. The input is the device worn by the user, and the output is the real-time learning content provided by the virtual store.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] [Third embodiment]

[0900] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0901] 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.

[0902] 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).

[0903] 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.

[0904] 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.

[0905] 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).

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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.

[0911] 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."

[0912] This invention is a system in which a user inputs necessary information, and a server generates a study plan based on that information, monitors progress, and provides a recovery plan as needed. This system is designed to support users in taking junior high school entrance exams, with the aim of reducing the burden on parents and maximizing the learning effect of their children.

[0913] This system operates in the following steps:

[0914] User registration and initial settings

[0915] First, the user uses a terminal to input the necessary information, including the child's grade, current grades, desired school, etc. This input data is then sent from the terminal to the server.

[0916] Data collection and analysis

[0917] The server registers the received user information and creates a user profile. The server also receives quizzes and class results provided by the cram school. This data is analyzed by the generative AI model to identify the child's weaknesses and strengths.

[0918] Generate a lesson plan

[0919] The server uses the generative AI model to generate a personalized learning plan based on the user's characteristics and performance, which is then sent to the user's device and displayed to them.

[0920] Learning progression and monitoring

[0921] The user studies daily according to the study plan and enters their progress into the device. The device then sends this progress data to the server, which monitors and evaluates the progress data.

[0922] Feedback and Recovery Plan

[0923] The server reanalyzes the latest learning data and past performance data and generates a recovery plan as needed, which is then sent to the device and displayed to the user.

[0924] Suggestion of desired school

[0925] The server then suggests the best schools to apply to based on the user's characteristics, grades, and progress. These suggestions are also sent to the device and displayed to the user.

[0926] Dashboard View

[0927] The device displays the user's progress and feedback in the form of a dashboard, allowing the user to see their learning progress at a glance.

[0928] Specific examples

[0929] For example, if a user inputs the grade data of their fifth-grade child and indicates their preference for Junior High School A, the server creates a profile based on the information the user inputs. The server analyzes test results provided by the cram school and uses a generative AI model to identify that the child is weak in "science." The server then generates a study plan that includes "30 minutes of science review every day" and sends it to the device. The user follows this study plan and enters their progress on the device. The server monitors their progress and provides a recovery plan as needed. The server also suggests to the user that "Junior High School B" is also suitable, and sends this information to the device and displays it to the user.

[0930] As described above, this system works in cooperation with the user, terminal, and server to support children's learning.

[0931] The processing flow will be explained below.

[0932] Program processing steps

[0933] Step 1: User registration and initial setup

[0934] 1.1 The user selects the "New Registration" option from the initial screen of the device.

[0935] Specific operation: The user enters information such as "name," "grade," "current grades," and "desired school" into the input form.

[0936] 1.2 The terminal sends the entered user information to the server.

[0937] Specific operation: The terminal encodes the input information into JSON format and sends a POST request to the server's API endpoint.

[0938] 1.3 The server registers the received user information in a database and creates a user profile.

[0939] Specific behavior: The server parses the received JSON data and saves it as a new user entry in the database.

[0940] Step 2: Collect and analyze data

[0941] 2.1 The server receives quizzes and class results provided by the cram school.

[0942] Specific operation: The server periodically retrieves test result data from the cram school's system via API.

[0943] 2.2 The server uses the generated AI model to analyze the test results and user performance data.

[0944] Specific operation: The server inputs the acquired test data into the generative AI model to analyze weaknesses and extract areas of strength.

[0945] Step 3: Generate a lesson plan

[0946] 3.1 The server generates an individualized learning plan based on the user's characteristics and performance data.

[0947] Specific operation: The server prioritizes and schedules learning tasks based on the user's grades and the question trends of the school of their choice.

[0948] 3.2 The server sends the generated learning plan to the device.

[0949] Specific operation: The server encodes the learning plan into JSON format and sends it to the device's API endpoint.

[0950] 3.3 The device visualizes the learning plan and displays it to the user.

[0951] Specific operation: The device displays the received study plan in calendar or list format and allows the user to confirm it.

[0952] Step 4: Progressing and monitoring your learning

[0953] 4.1 The user progresses through daily learning and inputs their learning progress into the device.

[0954] Specific operation: When the user finishes learning, he / she selects the content he / she learned on the "Learning Progress" screen of the device and presses the "Complete" button.

[0955] 4.2 The device sends the learning progress data to the server.

[0956] Specific operation: The device encodes the learning progress data entered by the user into JSON format and sends it to the server.

[0957] Step 5: Feedback and recovery plan

[0958] 5.1 The server evaluates the progress based on the latest learning progress data.

[0959] Specific operation: The server inputs new learning progress data into the generative AI model and evaluates the user's progress.

[0960] 5.2 The server generates a recovery plan as needed.

[0961] Specific operation: Based on the progress assessment results, the server generates a recovery plan for the required reinforcement.

[0962] 5.3 The server sends the recovery plan to the device.

[0963] Specific operation: The server encodes the generated recovery plan into JSON format and sends it to the terminal.

[0964] 5.4 The terminal visualizes the recovery plan and displays it to the user.

[0965] Specific operation: The device will display the received recovery plan in a pop-up notification or on the dashboard.

[0966] Step 6: Propose your preferred school

[0967] 6.1 The server suggests the most suitable schools to apply to based on the user's characteristics, grades, and progress data.

[0968] Specific operation: The server inputs learning data and past data into the generative AI model and creates a list of the most suitable schools for the user.

[0969] 6.2 The server sends the school preference suggestions to the device.

[0970] Specific operation: The server encodes the school preference proposals into JSON format and sends them to the device.

[0971] 6.3 The device displays school preference suggestions to the user.

[0972] Specific operation: The device displays the received list of preferred schools on the screen and provides the characteristics of each school and exam information.

[0973] Step 7: View the dashboard

[0974] 7.1 The device displays user progress and feedback in a dashboard format.

[0975] How it works: The device displays the latest learning data, progress reports, feedback, and information about the school of choice all on one screen.

[0976] As a result, this system meticulously monitors the user's learning progress and provides effective study plans and appropriate recovery plans, thereby reducing the burden on parents and providing effective support for their children's junior high school entrance exams.

[0977] Example 1

[0978] 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."

[0979] In today's educational environment, it is extremely difficult for parents to effectively manage and support their children's learning progress. Particularly when it comes to exam preparation and improving grades, accurately identifying a child's weaknesses and creating an effective learning plan to overcome those weaknesses requires advanced expertise and continuous monitoring. However, it is not realistic for parents to do this manually in their busy daily lives, and it places a significant burden on them. To solve this problem, a system is needed that can efficiently and effectively automatically generate learning plans and track and improve progress.

[0980] 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.

[0981] In this invention, the server includes: means for a user to input required information; means for transmitting the input information to the server; means for registering the received data and creating a user profile; means for receiving and analyzing assessment data provided by educational institutions and utilizing a generative AI model; means for identifying the user's weaknesses and strengths and generating an individualized learning plan; means for transmitting the generated learning plan to a terminal; means for the terminal to display the learning plan to the user; means for the user to input learning progress into the terminal and transmit the progress data to the server; means for evaluating progress and generating a recovery plan; means for transmitting the recovery plan to the terminal; means for the terminal to display the recovery plan; means for suggesting preferred schools based on the user's characteristics, grades, and progress; means for transmitting the preferred school suggestions to the terminal; means for the terminal to display the preferred school suggestions to the user; means for the terminal to display the user's progress and feedback in a dashboard format; and means for generating prompt sentences using a generative AI model to generate an optimal learning plan or recovery plan based on the child's learning progress. This enables users to efficiently and effectively automate a series of learning support tasks, such as creating a learning plan, monitoring progress, providing a recovery plan, and suggesting preferred schools.

[0982] "User" refers to an individual who uses this system to receive services such as creating a study plan, monitoring progress, providing a recovery plan, and suggesting preferred schools.

[0983] "Terminal" refers to a device that a user operates to input information and receive feedback and suggestions from the server, and specifically includes smartphones, tablets, and PCs.

[0984] "Server" refers to a computer system that receives information sent by a user, creates a user profile based on this information, generates a study plan, recovery plan, and suggestions for preferred schools, and sends these to the terminal.

[0985] A "generative AI model" is an artificial intelligence model used by the server to analyze user information and learning data to generate learning plans and recovery plans.

[0986] A "prompt sentence" is an input sentence used to give instructions to a generative AI model, and is used to specify how to generate a specific learning plan or recovery plan.

[0987] A "study plan" refers to a schedule of learning content that is individually created taking into account the user's weaknesses and strengths, and includes specific learning tasks aimed at improving a child's academic ability.

[0988] A "recovery plan" refers to a supplementary learning plan or specific improvement measures that are deemed necessary as a result of analyzing a user's learning progress and performance data.

[0989] "School of choice suggestions" refers to the server recommending the most suitable school based on the user's characteristics, grades, and progress, and includes information for selecting the school that best suits the user's wishes and abilities.

[0990] "Dashboard" refers to an interface that displays on the device a user's learning progress, feedback, recovery plans, suggested schools of choice, and more, all at a glance.

[0991] "User profile" refers to a data set that includes detailed information such as the user's grade, grades, and preferred school, which is created by the server based on information received from the user.

[0992] "Evaluation data" refers to data used to evaluate a user's learning status, such as quizzes and class results provided by educational institutions.

[0993] This system allows users to input necessary information, and a server generates a study plan based on that information, monitors progress, and provides recovery plans as needed. It is designed to support users preparing for junior high school entrance exams, reducing the burden on parents and maximizing the learning outcomes of their children.

[0994] User registration and initial settings

[0995] The user uses a device to input information such as their child's grade, current grades, and desired school. This input data is sent from the device to a server. The device can be a smartphone, tablet, or PC.

[0996] Data collection and analysis

[0997] The server creates a user profile based on the received user information. The server also receives data from educational institutions, such as quizzes and class results, and stores them in a database. This data is analyzed using a generative AI model (e.g., "OpenAI GPT-4") to identify the child's weaknesses and strengths. The server sends the following prompt to the generative AI model:

[0998] text

[0999] Analyze this user's learning data to identify their weaknesses and strengths.

[1000] Generate a lesson plan

[1001] The server uses the generative AI model to generate a personalized learning plan based on the user's characteristics and performance data. This learning plan is sent to the user's device and displayed to the user. The specific prompt is as follows:

[1002] text

[1003] My fifth-grader wants to attend Junior High School A, but he is not good at science. Please create a specific study plan to help him overcome his science weaknesses.

[1004] Learning progression and monitoring

[1005] The user studies daily according to the generated study plan and enters their progress into the device. The entered progress data is sent from the device to the server. The server receives the progress data and evaluates the progress using a monitoring system.

[1006] Feedback and Recovery Plan

[1007] The server re-analyzes the latest learning data and past performance data received and generates a recovery plan as necessary. The generated recovery plan is sent to the user's device and displayed to the user. The specific prompt is as follows:

[1008] text

[1009] Please re-analyze this user's learning data and generate the necessary recovery plan.

[1010] Suggestion of desired school

[1011] The server then proposes the best schools to apply to based on the user's characteristics, grades, and progress data. These proposals are also sent from the server to the device and displayed to the user.

[1012] Dashboard View

[1013] The device displays the user's progress and feedback in the form of a dashboard, allowing the user to see their learning progress at a glance.

[1014] Specific examples

[1015] For example, let's say a user inputs the grade data of their child in the fifth grade of elementary school and indicates that they would like to attend Junior High School A. The user inputs the child's grade, grades, and desired school information into their device and submits it. The server receives this data, and the generative AI model analyzes it to determine that the child is "weak in science." The server then sends the following prompt to the generative AI model and obtains the result:

[1016] text

[1017] My fifth-grader wants to attend Junior High School A, but he is not good at science. Please create a specific study plan to help him overcome his science weaknesses.

[1018] Based on this prompt, the server generates a specific study plan, including "30 minutes of science review every day," and sends it to the device. The user studies according to this study plan and enters their progress into the device. The progress data is sent to the server, which monitors the progress and generates a recovery plan as necessary. Based on the progress data, the server suggests that "Junior High School B" is also suitable, and sends this information to the device and displays it to the user.

[1019] As described above, this system works in cooperation with the user, terminal, and server to support children's learning.

[1020] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1021] System program processing flow

[1022] Step 1: User registration and initial setup

[1023] Specific actions

[1024] Input: The user inputs information such as the child's grade, current grades, and desired school into the terminal.

[1025] How it works: Data entered by the user is sent from the device to the server. When the "Send" button is pressed using the device's application, this data is sent to the server.

[1026] Output: User information data received by the server.

[1027] Step 2: Collect and analyze data

[1028] Specific actions

[1029] Input: The server receives user input data and also receives quiz and class result data from the educational institution.

[1030] How it works: The server creates a user profile based on the data it receives and registers it in a database. It then inputs the received evaluation data into a generative AI model for analysis, identifying the child's weaknesses and strengths. Specifically, it sends the following prompt to the generative AI model:

[1031] text

[1032] Analyze this user's learning data to identify their weaknesses and strengths.

[1033] Output: The analysis results obtained from the generative AI model (child's weaknesses and strengths).

[1034] Step 3: Generate a lesson plan

[1035] Specific actions

[1036] Input: The analysis results received by the server from the generative AI model and the user's initial input data.

[1037] How it works: The server uses a generative AI model to generate a personalized learning plan based on this data. It sends specific prompts to the generative AI model:

[1038] text

[1039] My fifth-grader wants to attend Junior High School A, but he is not good at science. Please create a specific study plan to help him overcome his science weaknesses.

[1040] Output: The generated learning plan. This learning plan is sent from the server to the device.

[1041] Step 4: Progressing and monitoring your learning

[1042] Specific actions

[1043] Input: The user inputs their daily learning progress into the terminal.

[1044] How it works: The user enters progress information into the application on the device and presses the "Send progress" button. This data is sent from the device to the server. The server receives the progress data and evaluates the progress in the monitoring system.

[1045] Output: Learning progress data sent to the server. Evaluation results based on the progress data on the server.

[1046] Step 5: Feedback and recovery plan

[1047] Specific actions

[1048] Input: The server provides the latest learning progress data and past grade data.

[1049] How it works: The server re-analyzes this data and generates a recovery plan if necessary. It sends specific prompts to the generative AI model:

[1050] text

[1051] Please re-analyze this user's learning data and generate the necessary recovery plan.

[1052] Output: The generated recovery plan, which the server sends to the device.

[1053] Step 6: Propose your preferred school

[1054] Specific actions

[1055] Input: The server collects user characteristics, performance, and progress data.

[1056] How it works: The server uses this data to suggest the best schools to apply to. The suggestions are sent from the server to the device and displayed to the user.

[1057] Output: Proposed data for desired schools.

[1058] Step 7: View the dashboard

[1059] Specific actions

[1060] Input: Progress, feedback, recovery plan, and suggested school of choice data sent from the server.

[1061] How it works: The device receives this data and displays it in a dashboard format, allowing users to check their learning progress at a glance.

[1062] Output: Dashboard showing progress, feedback, recovery plan, and school preference suggestions.

[1063] These steps allow the user, terminal, and server to work together to create a system that supports children's learning.

[1064] (Application example 1)

[1065] 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."

[1066] In conventional risk management systems, risk assessment and progress monitoring were not fully automated, requiring personnel to manually organize and understand data. As a result, risk management efficiency was reduced and it was difficult to provide recovery plans quickly. In addition, progress could not be grasped in real time, resulting in delays in risk countermeasures.

[1067] 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.

[1068] In this invention, the server includes means for receiving risk information input by a user and generating a risk management plan using a generative AI model, means for evaluating risk management progress data and generating a recovery plan as necessary, and means for proposing appropriate risk management measures based on the user's risk information and progress. This enables automatic collection and analysis of risk information, automatic generation of management plans, real-time progress monitoring, and rapid provision of recovery plans.

[1069] Key Word Definitions:

[1070] "User"

[1071] is a person or organization that uses a system to input information and receive results.

[1072] "Required Information"

[1073] This refers to data necessary for risk management, such as company information, types of risks, and past security incidents.

[1074] "Terminal"

[1075] is an electronic device used by a user to input information and receive feedback from a system, including smartphones and tablets.

[1076] "server"

[1077] It is a communications device that receives and registers data sent by users, generates risk management plans and recovery plans using generative AI models, and sends them back to the terminal.

[1078] "Generative AI model"

[1079] This refers to algorithms that use machine learning and artificial intelligence techniques to analyze data and generate risk management and recovery plans.

[1080] "Risk Information"

[1081] is data about risk factors that may affect a company or organization, which can be entered by users via a terminal.

[1082] "Risk Management Plan"

[1083] A plan is generated using a generative AI model, which evaluates the risks that users need to address and includes specific countermeasures based on the assessment.

[1084] "Progress Data"

[1085] is data recorded by the user on their progress according to the risk management plan, which is sent to the server and used for evaluation.

[1086] "Recovery Plan"

[1087] A plan is one that includes alternative responses if things do not go as planned or new risks arise.

[1088] Dashboard

[1089] is an interface that visually organizes and displays information so that users can grasp the progress and current status of risk management at a glance.

[1090] "Risk management measures"

[1091] These are specific measures or steps that can be taken to address specific risks, and are proposed by the server based on the user's risk information and progress.

[1092] MODE FOR CARRYING OUT THE INVENTION

[1093] User registration and initial settings

[1094] First, the user uses a terminal to input the necessary information into the input form. This information includes company data related to risk management, risk types, past security incidents, etc. The input information is then sent from the terminal to the server. The terminal can be a smartphone or tablet and provides a user-friendly interface.

[1095] Data collection and analysis

[1096] The server registers the received user information and creates a user profile. The server also receives risk information and incident data from external sources. This data is analyzed by a generative AI model to classify and identify risk factors. The generative AI model is optimized using machine learning and artificial intelligence techniques.

[1097] Generate a risk management plan

[1098] The server uses the generative AI model to generate a personalized risk management plan based on the user's information. The plan includes specific countermeasures and a progress schedule. The risk management plan is then sent from the server to the device and displayed to the user.

[1099] Management Progression and Monitoring

[1100] Users report progress according to the risk management plan. They use their terminals to input progress and the status of countermeasure implementation, and send this to the server. The server monitors the progress data and evaluates the progress.

[1101] Feedback and Recovery Plan

[1102] The server re-analyzes the latest progress data and past risk data and generates a recovery plan as needed. This recovery plan may include additional or new countermeasures for specific risks. The generated recovery plan is sent to the terminal and displayed to the user.

[1103] Proposal of risk management measures

[1104] The server proposes optimal risk management measures based on the user's characteristics, risk information, and progress data, and these proposals are also sent to the terminal and displayed to the user.

[1105] Dashboard View

[1106] The device is equipped with a built-in dashboard displaying user progress and feedback, allowing users to see their risk management progress at a glance.

[1107] Specific examples

[1108] For example, when a user performs security risk management for "Small and Medium-sized Enterprise A," they first enter company information, risk types, and past incident data. The server then uses a generative AI model to create a risk profile for "Small and Medium-sized Enterprise A" and generates a risk management plan. This plan includes specific measures such as "30 minutes of security checks every day" and "data backup once a week." The user reports their progress according to this plan, and the server monitors the progress data. If progress is delayed, the server generates a new recovery plan and sends it to the device.

[1109] Examples of prompt statements

[1110] Here are some example prompts for a generative AI model:

[1111] "Our company is a small to medium-sized enterprise with 50 employees. We have experienced a data breach in the past. We are currently paying particular attention to phishing attacks and internal fraud risks."

[1112] In this way, the user can manage risk efficiently and effectively.

[1113] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1114] Program processing steps

[1115] Processing Steps

[1116] Step 1:

[1117] The user uses a terminal to input the required information, such as company information, risk type, and past security incidents. After the user enters this information into a form, the terminal sends the data to the server. This input data is used as the basis for generating a risk management plan.

[1118] Step 2:

[1119] The server registers the received data and creates a user profile. The server stores the received information in a database and organizes it as a user profile. It also acquires external risk information and past incident data and adds them to the database for analysis by the generative AI model. The input is user information sent from the device, and the output is the generation of a user profile.

[1120] Step 3:

[1121] The server uses a generative AI model to generate a risk management plan. The generative AI model analyzes risk factors based on the input user profile and risk data. This analysis generates an individual risk management plan. The generated plan includes specific countermeasures and progress schedules. The input is the user profile and risk data, and the output is a risk management plan.

[1122] Step 4:

[1123] The server sends the generated risk management plan to the terminal. The terminal displays this risk management plan to the user. The user starts to implement risk management based on the displayed plan. The input is the risk management plan, and the output is the display to the user.

[1124] Step 5:

[1125] The user reports progress according to the risk management plan. Using a terminal, the user inputs the progress status and the implementation status of countermeasures, and sends this data to the server. The input is the user's progress status data, and the output is the transmission of the progress data to the server.

[1126] Step 6:

[1127] The server monitors the progress data and evaluates the progress. The server analyzes the received progress data and identifies the progress and problems of risk management. If necessary, it generates a recovery plan. The input is the progress data, and the output is the generation of a recovery plan.

[1128] Step 7:

[1129] The server sends the generated recovery plan to the terminal, which displays it to the user. The recovery plan includes additional measures and new countermeasures for specific risks. The input is the recovery plan, and the output is the display to the user.

[1130] Step 8:

[1131] The server proposes appropriate risk management measures based on the user's risk information and progress. The generative AI model performs another analysis and proposes optimal management measures. The server sends this proposal to the terminal, which displays it to the user. The input is risk information and progress data, and the output is a risk management measure proposal.

[1132] Step 9:

[1133] The terminal displays the user's progress and feedback in the form of a dashboard. The dashboard displays progress, risk reviews, and feedback, allowing the user to check the situation in real time. The input is progress data and feedback data, and the output is the dashboard display.

[1134] 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.

[1135] This invention is a system to support preparation for junior high school entrance exams, monitoring the user's learning progress and emotional state, and providing a study plan and recovery plan based on this. This aims to reduce the user's burden while maximizing the learning effect.

[1136] This system has the basic functions of allowing users to input the necessary information, and then having the server generate a study plan based on that information, monitor progress, and provide a recovery plan if necessary. In addition, it also includes an emotion engine that recognizes the user's emotions.

[1137] User registration and initial settings

[1138] First, the user inputs the necessary information using the device, including the child's grade, current grades, preferred schools, and emotional data, which is then sent to the server.

[1139] Data collection and analysis

[1140] The server registers the received user information and creates a user profile. The server also receives quizzes and class results provided by the cram school and analyzes them using a generative AI model. Furthermore, the server uses the user's emotional data to adjust study plans and recovery plans.

[1141] Generate a lesson plan

[1142] The server uses a generative AI model to generate a personalized learning plan based on the user's characteristics, grades, and emotional state, which is then sent to the user's device and displayed to them.

[1143] Learning progression and monitoring

[1144] The user studies daily according to the study plan and inputs their learning progress into the device. The device then sends this progress data and emotional data to the server, which monitors and evaluates the progress data and emotional data.

[1145] Feedback and Recovery Plan

[1146] The server evaluates the user's progress based on the latest learning progress data and emotional data, and generates a recovery plan if necessary. The recovery plan is sent to the device and displayed to the user. For example, if the user is feeling stressed, adjustments may be made, such as adding tasks to help them relax.

[1147] Suggestion of desired school

[1148] The server will suggest the most suitable school to apply to based on the user's characteristics, grades, progress, and emotional data. These suggestions are also sent to the device and displayed to the user. It is also possible to select a school that offers a less stressful environment based on emotional data.

[1149] Dashboard View

[1150] The device displays the user's progress, feedback, and emotional data in a dashboard format, allowing the user to see at a glance their emotional changes as well as their learning progress.

[1151] Specific examples

[1152] For example, if a user inputs their child's grades and emotions and indicates their preference for Junior High School A, the server creates a profile based on the user's input. The server analyzes test results provided by the cram school, uses a generative AI model to identify that the child is weak in science, and uses an emotion engine to recognize that the child experiences high stress while studying science. The server then generates a study plan that includes 30 minutes of daily science review and adds a weekly relaxation task to reduce stress. This is sent to the device, and the user follows the study plan and enters their progress and emotional state into the device. The server monitors their progress and emotional state and provides a recovery plan as needed. The server also suggests Junior High School B as a suitable option, sending this information to the device and displaying it to the user.

[1153] As described above, this system works in cooperation with the user, terminal, and server to support the user's learning progress and emotional state.

[1154] The processing flow will be explained below.

[1155] Program processing steps

[1156] Step 1: User registration and initial setup

[1157] 1.1 The user selects the "New Registration" option from the initial screen of the device.

[1158] Specific operation: The user enters information such as "name," "grade," "current grades," "desired school," and "permission to collect emotional data" into an input form.

[1159] 1.2 The terminal sends the entered user information to the server.

[1160] Specific operation: The terminal encodes the input information into JSON format and sends a POST request to the server's API endpoint.

[1161] 1.3 The server registers the received user information in a database and creates a user profile.

[1162] Specific behavior: The server parses the received JSON data and saves it as a new user entry in the database.

[1163] Step 2: Collect and analyze data

[1164] 2.1 The server receives quizzes and class results provided by the cram school.

[1165] Specific operation: The server periodically retrieves test result data from the cram school's system via API.

[1166] 2.2 The server uses the generated AI model to analyze the test results and user performance data.

[1167] Specific operation: The server inputs the acquired test data into the generative AI model to analyze weaknesses and extract areas of strength.

[1168] 2.3 The device monitors the user's emotional state in real time and transmits the emotional data to the server.

[1169] Specific operation: The device uses the built-in camera and microphone to analyze the user's emotions from their facial expressions and voice, and sends the analysis results to the server.

[1170] Step 3: Generate a lesson plan

[1171] 3.1 The server generates an individualized learning plan based on the user's characteristics, performance data, and emotional data.

[1172] Specific operation: The server integrates the user's grades, exam trends at the school of choice, and emotional data to prioritize and schedule learning tasks.

[1173] 3.2 The server sends the generated learning plan to the device.

[1174] Specific operation: The server encodes the learning plan into JSON format and sends it to the device's API endpoint.

[1175] 3.3 The device visualizes the learning plan and displays it to the user.

[1176] Specific operation: The device displays the received study plan in calendar or list format and allows the user to confirm it.

[1177] Step 4: Progressing and monitoring your learning

[1178] 4.1 The user progresses through daily learning and inputs their learning progress into the device.

[1179] Specific operation: When the user finishes learning, he / she selects the content he / she learned on the "Learning Progress" screen of the device and presses the "Complete" button.

[1180] 4.2 The device sends learning progress data and emotion data to the server.

[1181] Specific operation: The device encodes the learning progress data entered by the user and the emotion data collected in real time into JSON format and sends it to the server.

[1182] Step 5: Feedback and recovery plan

[1183] 5.1 The server evaluates the progress based on the latest learning progress data and emotion data.

[1184] Specific operation: The server inputs new learning progress data and emotion data into the generative AI model and evaluates the user's progress.

[1185] 5.2 The server generates a recovery plan as needed.

[1186] Specific operation: Based on the progress assessment results, the server generates a recovery plan for the necessary reinforcement. For example, if the user is feeling stressed about a particular subject, the server will add tasks to reinforce that subject as well as tasks to help them relax.

[1187] 5.3 The server sends the recovery plan to the device.

[1188] Specific operation: The server encodes the generated recovery plan into JSON format and sends it to the terminal.

[1189] 5.4 The terminal visualizes the recovery plan and displays it to the user.

[1190] Specific operation: The device will display the received recovery plan in a pop-up notification or on the dashboard.

[1191] Step 6: Propose your preferred school

[1192] 6.1 The server suggests the best schools to apply to based on the user's characteristics, grades, progress data, and emotional data.

[1193] Specific operation: The server inputs learning data, past data, and emotional data into the generative AI model and creates a list of the most suitable schools for the user.

[1194] 6.2 The server sends the school preference suggestions to the device.

[1195] Specific operation: The server encodes the school preference proposals into JSON format and sends them to the device.

[1196] 6.3 The device displays school preference suggestions to the user.

[1197] Specific operation: The device displays the received list of preferred schools on the screen and provides the characteristics of each school and exam information.

[1198] Step 7: View the dashboard

[1199] 7.1 The device displays the user's progress, feedback, and emotional data in a dashboard format.

[1200] Specific operation: The device displays the latest learning data, progress reports, feedback, and emotional fluctuations on a single screen.

[1201] As a result, this system meticulously monitors the user's learning progress and emotional state, and provides effective study plans and appropriate recovery plans, thereby reducing the burden on parents and providing effective support for their children's junior high school entrance exams.

[1202] Example 2

[1203] 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."

[1204] In today's junior high school entrance exams, children are often exposed to hectic study schedules and pressure. This necessitates an efficient and effective learning management system. However, existing systems only monitor academic progress and do not take into account fluctuations in emotional states. This creates challenges, such as inadequate management of children's stress and anxiety, making it difficult to maximize overall learning outcomes.

[1205] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1206] In this invention, the server includes a means for creating a user profile, a means for receiving learning results and emotional data and utilizing a generative AI model for analyzing the data, and a means for generating an individual learning plan based on the user's characteristics and emotional data, thereby enabling a comprehensive learning support system that takes into account not only learning progress but also emotional states.

[1207] "User" refers to a person who utilizes the system to monitor their learning progress and emotional state and to receive learning and recovery plans.

[1208] "Terminal" refers to the electronic device used by the User to input necessary information and check the study plan, progress, recovery plan and suggested schools of choice.

[1209] "Server" refers to the central processing unit that receives and analyzes data sent from the terminal, generates study plans and recovery plans, monitors progress, and suggests preferred schools.

[1210] "Generative AI model" refers to an artificial intelligence model that analyzes learning progress, evaluates emotional state, and generates learning plans based on input data.

[1211] A "prompt sentence" refers to a specific sentence used to input instructions or questions to a generative AI model.

[1212] "Study Plan" refers to a personalized study schedule that is generated based on a user's characteristics, performance, and emotional state.

[1213] A "recovery plan" refers to additional learning tasks or stress reduction tasks that are adjusted as needed, taking into account the user's learning progress and emotional state.

[1214] "Emotion data" refers to information that indicates the user's emotional state, including stress and satisfaction during learning.

[1215] "Study results" refers to data that shows the results of a user's learning, such as test results and class grades.

[1216] "Suggesting a preferred school" refers to the act of the server recommending the most suitable preferred school based on the user's characteristics, grades, and emotional data.

[1217] "Dashboard format" refers to a format that visually displays a user's learning progress, emotional data, and feedback in an easy-to-understand manner.

[1218] This invention is a system to support preparation for junior high school entrance exams, monitoring the user's learning progress and emotional state, and providing a study plan and recovery plan based on this. This aims to reduce the user's burden while maximizing the learning effect.

[1219] User registration and initial settings

[1220] First, the user uses the device to input the necessary information, including the child's grade, current grades, preferred school, and emotional data, which is then sent to the server.

[1221] Data collection and analysis

[1222] The server registers the received user information and creates a user profile. The server also receives quizzes and class results provided by the cram school and analyzes them using a generative AI model. Furthermore, the server uses the user's emotional data to adjust study plans and recovery plans.

[1223] Generate a lesson plan

[1224] The server uses a generative AI model to generate a personalized learning plan based on the user's characteristics, grades, and emotional state, which is then sent to the user's device and displayed to them.

[1225] Learning progression and monitoring

[1226] The user studies daily according to the study plan and inputs their learning progress into the device. The device then sends this progress data and emotional data to the server, which monitors and evaluates the progress data and emotional data.

[1227] Feedback and Recovery Plan

[1228] The server evaluates the user's progress based on the latest learning progress data and emotional data, and generates a recovery plan if necessary. The recovery plan is sent to the device and displayed to the user. For example, if the user is feeling stressed, adjustments may be made, such as adding tasks to help them relax.

[1229] Suggestion of desired school

[1230] The server will suggest the most suitable school to apply to based on the user's characteristics, grades, progress, and emotional data. These suggestions are also sent to the device and displayed to the user. It is also possible to select a school that offers a less stressful environment based on emotional data.

[1231] Dashboard View

[1232] The device displays the user's progress, feedback, and emotional data in a dashboard format, allowing the user to see at a glance their emotional changes as well as their learning progress.

[1233] Specific examples

[1234] For example, if a user inputs their child's grades and emotions and indicates their preference for Junior High School A, the server creates a profile based on the user's input. The server analyzes test results provided by the cram school, uses a generative AI model to identify that the child is weak in science, and uses an emotion engine to recognize that the child experiences high stress while studying science. The server then generates a study plan that includes 30 minutes of daily science review and adds a weekly relaxation task to reduce stress. This is sent to the device, and the user follows the study plan and enters their progress and emotional state into the device. The server monitors their progress and emotional state and provides a recovery plan as needed. The server also suggests Junior High School B as a suitable option, sending this information to the device and displaying it to the user.

[1235] Prompt Sentence Examples

[1236] Examples of prompts to input to a generative AI model include:

[1237] "Your fifth-grade child is performing poorly in science, and emotional data indicates high levels of stress. Please create an appropriate learning plan and recovery plan to reduce stress for this child."

[1238] As described above, this system works in cooperation with the user, terminal, and server to support the user's learning progress and emotional state.

[1239] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1240] Step 1:

[1241] The user uses the terminal to input necessary information such as the child's grade, current grades, desired school, emotional data, etc. Once this input is complete, the terminal transmits this data to the server.

[1242] Step 2:

[1243] The server creates a user profile based on the user information received from the terminal, and registers the user's basic information and initial settings in the database.

[1244] Step 3:

[1245] The server receives quiz and class result data provided by the cram school. This data includes grades for each subject and learning progress. The server inputs this data into the generative AI model.

[1246] Step 4:

[1247] The generative AI model analyzes the learning results and emotional data it receives. The model analyzes grades for each subject and the user's emotional state, such as stress and satisfaction. The analysis results are stored on the server.

[1248] Step 5:

[1249] Based on the analysis results of the AI ​​model, the server generates an individualized study plan that takes into account the user's characteristics, grades, and emotional state. The study plan includes study time and review content for each subject, as well as stress reduction tasks according to emotions.

[1250] Step 6:

[1251] The server sends the generated lesson plan to the terminal, which receives the lesson plan and displays it to the user.

[1252] Step 7:

[1253] The user studies daily based on a study plan. After studying, the user inputs their study progress and emotional state into the device. The input data is sent from the device to the server.

[1254] Step 8:

[1255] The server receives and monitors the progress and emotion data sent by the user, and evaluates the user's learning and emotional fluctuations based on the monitoring results.

[1256] Step 9:

[1257] Based on the progress and emotional state assessment, the server generates a recovery plan as needed, which may include additional learning tasks or relaxation tasks to reduce stress.

[1258] Step 10:

[1259] The server sends the generated recovery plan to the terminal, which receives the recovery plan and displays it to the user.

[1260] Step 11:

[1261] The server selects the most suitable school based on the user's characteristics, grades, progress, and emotional data, and generates a proposal of the school. The proposal is sent from the server to the terminal.

[1262] Step 12:

[1263] The device displays the suggested schools to the user, who can then review them and use them as a reference for making a selection.

[1264] Step 13:

[1265] The device displays the user's learning progress, emotional data, and feedback in the form of a dashboard, allowing the user to visually check their learning progress and emotional fluctuations.

[1266] Specific input and output examples

[1267] For example, suppose a user inputs grade data from their fifth grade (Japanese: 80 points, Math: 70 points, Science: 60 points) and emotional data indicating that they feel high stress while studying science. This data is sent from the device to the server. The server analyzes this data and generates a study plan that includes "30 minutes of science review every day" and "a relaxation task once a week." This study plan is sent to the device and displayed to the user.

[1268] Prompt Sentence Examples

[1269] The prompt to the generative AI model is as follows:

[1270] "Your fifth-grade child is performing poorly in science, and emotional data indicates high levels of stress. Please create an appropriate learning plan and recovery plan to reduce stress for this child."

[1271] Through the above steps, this system works in cooperation with the user, terminal, and server to effectively support the user's learning progress and emotional state.

[1272] (Application example 2)

[1273] 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."

[1274] There is a growing need for a system that can effectively manage the learning progress and emotional fluctuations of junior high school entrance exam students and provide individually optimized learning plans. Conventional methods have made it difficult to timely monitor learning progress and emotional fluctuations and appropriately adjust learning and recovery plans based on that information. Furthermore, the lack of an environment for receiving real-time learning support has made it difficult to reduce students' stress and improve their learning efficiency.

[1275] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1276] In this invention, the server includes a means for monitoring the user's emotional state and transmitting emotional data to the server, a means for adjusting a study plan or recovery plan based on the emotional data, and a means for the terminal to access a virtual store through smart glasses or a head-mounted display and provide learning content in real time. This makes it possible to comprehensively manage the learning progress and emotional state of junior high school entrance exam students and provide individually optimized real-time learning support.

[1277] "Users" refers to junior high school entrance exam students and their parents, who are the entities that use the system.

[1278] "Necessary information" refers to data necessary to generate a study plan and recovery plan, such as grade level, current grades, desired school, and emotional data.

[1279] "Server" means a data processing device that receives, registers, and analyzes data sent by users, and generates and adjusts learning plans and recovery plans.

[1280] "Device" refers to a device used by a user, such as a smartphone, tablet, or PC, that provides an interface for displaying and inputting learning plans and learning data.

[1281] "Generative AI model" refers to an artificial intelligence algorithm that analyzes received data and generates learning and recovery plans.

[1282] A "study plan" is a personalized study schedule and tasks that is generated based on a user's performance data and emotional data.

[1283] A "recovery plan" is a complementary learning plan created to improve learning progress or emotional state when there is a problem.

[1284] "Emotional state" refers to data that indicates the user's mental state, such as stress or excitement, during learning.

[1285] A "virtual store" is a virtual learning environment accessible over the internet that provides real-time learning content through smart glasses or head-mounted displays.

[1286] "Smart glasses" are glasses-type devices that have a built-in display and camera that display information and provide users with an augmented reality (AR) experience.

[1287] A "head-mounted display" is a display device that users can wear to experience virtual reality (VR) and augmented reality (AR).

[1288] "Learning support" refers to a series of support services that monitor the user's learning progress and emotional state and provide optimal learning and recovery plans.

[1289] The present invention is a system that comprehensively manages the learning progress and emotional state of junior high school entrance exam students, providing individually optimized real-time learning support. In this system, the user inputs necessary information, and the server generates and adjusts learning plans and recovery plans based on that information, and provides them to the user via a terminal. Detailed embodiments of the present invention are described below.

[1290] User registration and initial settings

[1291] Users use devices such as smartphones or PCs to input the necessary information, including their grade, current grades, preferred schools, and emotional data. To access the virtual store, users wear smart glasses or a head-mounted display. This information is then sent to a server via the Internet.

[1292] Data collection and analysis

[1293] The server registers the received user information and creates a user profile. At the same time, the server receives data such as quizzes and class results from cram schools and online learning platforms. The generative AI model analyzes this data and identifies the user's weaknesses and strengths. Furthermore, emotion recognition sensors built into the smart glasses or head-mounted display are used to collect user emotion data.

[1294] Generate and deliver lesson plans

[1295] The server uses a generative AI model to automatically generate an individualized study plan based on the user's performance and emotional data. This study plan includes study tasks to address the user's weaknesses and additional tasks to improve performance. The generated study plan is sent to the user's device via the Internet and displayed to the user.

[1296] Learning progression and monitoring

[1297] The user follows the generated study plan as they study daily. Their learning progress and emotional state are entered into the device and periodically sent to the server. The server monitors this data and evaluates their progress. If necessary, a recovery plan is automatically generated and sent to the device.

[1298] Feedback and Recovery Plan

[1299] The server evaluates the progress and emotion data and generates a recovery plan as needed. The recovery plan is adjusted if the user is feeling stressed or if progress is slowing down. This recovery plan is also displayed on the device and provided to the user.

[1300] Suggestion of desired school

[1301] The server suggests the most suitable school to apply to based on the user's characteristics, grades, progress, and emotional data. The suggested schools are sent to the user's device and notified to the user. For example, if the user is in an environment where they are prone to stress, the server can suggest schools that are predicted to be less stressful.

[1302] Use of virtual stores

[1303] Users can access real-time learning content by wearing smart glasses or a head-mounted display and accessing a virtual store. This content is provided by a server and supports the user's learning progress.

[1304] Examples and prompts

[1305] For example, if a user inputs their child's grades and emotions from their fifth-grade elementary school and indicates their preference for a specific junior high school, the server will create a profile based on this data. The server analyzes test results provided by the cram school, using a generative AI model to identify that the child is weak in a particular subject, and an emotion engine to recognize that the child is experiencing high levels of stress while studying that subject. The server then generates a study plan that includes "30 minutes of daily review" and adds a weekly relaxation task. This is then sent to the device, and the user enters their progress and emotional state according to the study plan.

[1306] An example prompt is, "Generate the best individualized learning and recovery plan based on the student's current grades, learning progress, and emotional data."

[1307] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1308] Step 1:

[1309] The user uses a terminal to input necessary information (grade, grades, desired school, emotional data, etc.). This input data is sent to a server via the Internet. For example, the input information might be a current grade of 80 points and the desired school being A Junior High School, and the output is user profile data sent to the server.

[1310] Step 2:

[1311] The server registers the received user information and creates a user profile. The received data is stored on the server and stored in various databases in preparation for analysis by the generative AI model. The input is the user's basic information and performance information, and the output is structured user profile data.

[1312] Step 3:

[1313] The server receives data such as quiz and class results from cram schools and online learning platforms. This learning data is combined with the user's grade data and analyzed. The input is the quiz results and class content data, and the output is data for analysis that is stored in a database on the server.

[1314] Step 4:

[1315] The server uses a generative AI model to analyze the user's performance data and emotional data to identify the user's weaknesses and strengths. The generative AI model processes the received data as input and analyzes learning patterns and emotional fluctuations. The output is a list of each user's weak and strong subjects.

[1316] Step 5:

[1317] The server automatically generates an individualized study plan based on the analysis results. The generative AI model takes into account the user's grades and emotional state to create the optimal study plan. For example, if the user is determined to have a weak point in math, the study plan will include "reviewing math for 30 minutes every day." The input is the analyzed grade data and emotional data, and the output is an individualized study plan.

[1318] Step 6:

[1319] The server sends the generated lesson plan to the terminal via the Internet. The lesson plan is displayed on the user's terminal and lists specific learning tasks. The input is the generated lesson plan, and the output is the lesson plan displayed on the user's terminal.

[1320] Step 7:

[1321] The user uses the device to follow the learning plan and input their learning progress and emotional state. The device collects this data and periodically transmits it to the server. The input is the learning progress data and emotional data entered by the user, and the output is the progress data transmitted to the server.

[1322] Step 8:

[1323] The server monitors progress data and emotion data to evaluate the progress. If there are delays in progress or emotional fluctuations, the generative AI model automatically generates a recovery plan. The input is learning progress data and emotion data, and the output is a recovery plan.

[1324] Step 9:

[1325] The server sends the generated recovery plan to the device via the Internet. The recovery plan is displayed on the device in the same way as a learning plan. For example, if the user is feeling stressed, the recovery plan will include tasks for relaxation. The input is the generated recovery plan, and the output is the recovery plan displayed on the user's device.

[1326] Step 10:

[1327] The server suggests the most suitable schools to apply to based on the user's characteristics, grades, and progress data. The generative AI model analyzes the data and lists the most suitable schools for the user. The input is the user's comprehensive data, and the output is a list of preferred schools.

[1328] Step 11:

[1329] The server sends the information on the schools it recommends to the terminal. The terminal displays this information to the user, including detailed information about the schools and their advantages and disadvantages. The input is the school recommendation data from the server, and the output is a list of schools displayed on the user's terminal.

[1330] Step 12:

[1331] Users wear smart glasses or a head-mounted display and access the virtual store, where they can receive real-time learning content and progress with their studies. The input is the device worn by the user, and the output is the real-time learning content provided by the virtual store.

[1332] 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.

[1333] 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.

[1334] 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.

[1335] [Fourth embodiment]

[1336] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1337] 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.

[1338] 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).

[1339] 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.

[1340] 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.

[1341] 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).

[1342] 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.

[1343] 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.

[1344] 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.

[1345] 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.

[1346] 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.

[1347] 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.

[1348] 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."

[1349] This invention is a system in which a user inputs necessary information, and a server generates a study plan based on that information, monitors progress, and provides a recovery plan as needed. This system is designed to support users in taking junior high school entrance exams, with the aim of reducing the burden on parents and maximizing the learning effect of their children.

[1350] This system operates in the following steps:

[1351] User registration and initial settings

[1352] First, the user uses a terminal to input the necessary information, including the child's grade, current grades, desired school, etc. This input data is then sent from the terminal to the server.

[1353] Data collection and analysis

[1354] The server registers the received user information and creates a user profile. The server also receives quizzes and class results provided by the cram school. This data is analyzed by the generative AI model to identify the child's weaknesses and strengths.

[1355] Generate a lesson plan

[1356] The server uses the generative AI model to generate a personalized learning plan based on the user's characteristics and performance, which is then sent to the user's device and displayed to them.

[1357] Learning progression and monitoring

[1358] The user studies daily according to the study plan and enters their progress into the device. The device then sends this progress data to the server, which monitors and evaluates the progress data.

[1359] Feedback and Recovery Plan

[1360] The server reanalyzes the latest learning data and past performance data and generates a recovery plan as needed, which is then sent to the device and displayed to the user.

[1361] Suggestion of desired school

[1362] The server then suggests the best schools to apply to based on the user's characteristics, grades, and progress. These suggestions are also sent to the device and displayed to the user.

[1363] Dashboard View

[1364] The device displays the user's progress and feedback in the form of a dashboard, allowing the user to see their learning progress at a glance.

[1365] Specific examples

[1366] For example, if a user inputs the grade data of their fifth-grade child and indicates their preference for Junior High School A, the server creates a profile based on the information the user inputs. The server analyzes test results provided by the cram school and uses a generative AI model to identify that the child is weak in "science." The server then generates a study plan that includes "30 minutes of science review every day" and sends it to the device. The user follows this study plan and enters their progress on the device. The server monitors their progress and provides a recovery plan as needed. The server also suggests to the user that "Junior High School B" is also suitable, and sends this information to the device and displays it to the user.

[1367] As described above, this system works in cooperation with the user, terminal, and server to support children's learning.

[1368] The processing flow will be explained below.

[1369] Program processing steps

[1370] Step 1: User registration and initial setup

[1371] 1.1 The user selects the "New Registration" option from the initial screen of the device.

[1372] Specific operation: The user enters information such as "name," "grade," "current grades," and "desired school" into the input form.

[1373] 1.2 The terminal sends the entered user information to the server.

[1374] Specific operation: The terminal encodes the input information into JSON format and sends a POST request to the server's API endpoint.

[1375] 1.3 The server registers the received user information in a database and creates a user profile.

[1376] Specific behavior: The server parses the received JSON data and saves it as a new user entry in the database.

[1377] Step 2: Collect and analyze data

[1378] 2.1 The server receives quizzes and class results provided by the cram school.

[1379] Specific operation: The server periodically retrieves test result data from the cram school's system via API.

[1380] 2.2 The server uses the generated AI model to analyze the test results and user performance data.

[1381] Specific operation: The server inputs the acquired test data into the generative AI model to analyze weaknesses and extract areas of strength.

[1382] Step 3: Generate a lesson plan

[1383] 3.1 The server generates an individualized learning plan based on the user's characteristics and performance data.

[1384] Specific operation: The server prioritizes and schedules learning tasks based on the user's grades and the question trends of the school of their choice.

[1385] 3.2 The server sends the generated learning plan to the device.

[1386] Specific operation: The server encodes the learning plan into JSON format and sends it to the device's API endpoint.

[1387] 3.3 The device visualizes the learning plan and displays it to the user.

[1388] Specific operation: The device displays the received study plan in calendar or list format and allows the user to confirm it.

[1389] Step 4: Progressing and monitoring your learning

[1390] 4.1 The user progresses through daily learning and inputs their learning progress into the device.

[1391] Specific operation: When the user finishes learning, he / she selects the content he / she learned on the "Learning Progress" screen of the device and presses the "Complete" button.

[1392] 4.2 The device sends the learning progress data to the server.

[1393] Specific operation: The device encodes the learning progress data entered by the user into JSON format and sends it to the server.

[1394] Step 5: Feedback and recovery plan

[1395] 5.1 The server evaluates the progress based on the latest learning progress data.

[1396] Specific operation: The server inputs new learning progress data into the generative AI model and evaluates the user's progress.

[1397] 5.2 The server generates a recovery plan as needed.

[1398] Specific operation: Based on the progress assessment results, the server generates a recovery plan for the required reinforcement.

[1399] 5.3 The server sends the recovery plan to the device.

[1400] Specific operation: The server encodes the generated recovery plan into JSON format and sends it to the terminal.

[1401] 5.4 The terminal visualizes the recovery plan and displays it to the user.

[1402] Specific operation: The device will display the received recovery plan in a pop-up notification or on the dashboard.

[1403] Step 6: Propose your preferred school

[1404] 6.1 The server suggests the most suitable schools to apply to based on the user's characteristics, grades, and progress data.

[1405] Specific operation: The server inputs learning data and past data into the generative AI model and creates a list of the most suitable schools for the user.

[1406] 6.2 The server sends the school preference suggestions to the device.

[1407] Specific operation: The server encodes the school preference proposals into JSON format and sends them to the device.

[1408] 6.3 The device displays school preference suggestions to the user.

[1409] Specific operation: The device displays the received list of preferred schools on the screen and provides the characteristics of each school and exam information.

[1410] Step 7: View the dashboard

[1411] 7.1 The device displays user progress and feedback in a dashboard format.

[1412] How it works: The device displays the latest learning data, progress reports, feedback, and information about the school of choice all on one screen.

[1413] As a result, this system meticulously monitors the user's learning progress and provides effective study plans and appropriate recovery plans, thereby reducing the burden on parents and providing effective support for their children's junior high school entrance exams.

[1414] Example 1

[1415] 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."

[1416] In today's educational environment, it is extremely difficult for parents to effectively manage and support their children's learning progress. Particularly when it comes to exam preparation and improving grades, accurately identifying a child's weaknesses and creating an effective learning plan to overcome those weaknesses requires advanced expertise and continuous monitoring. However, it is not realistic for parents to do this manually in their busy daily lives, and it places a significant burden on them. To solve this problem, a system is needed that can efficiently and effectively automatically generate learning plans and track and improve progress.

[1417] 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.

[1418] In this invention, the server includes: means for a user to input required information; means for transmitting the input information to the server; means for registering the received data and creating a user profile; means for receiving and analyzing assessment data provided by educational institutions and utilizing a generative AI model; means for identifying the user's weaknesses and strengths and generating an individualized learning plan; means for transmitting the generated learning plan to a terminal; means for the terminal to display the learning plan to the user; means for the user to input learning progress into the terminal and transmit the progress data to the server; means for evaluating progress and generating a recovery plan; means for transmitting the recovery plan to the terminal; means for the terminal to display the recovery plan; means for suggesting preferred schools based on the user's characteristics, grades, and progress; means for transmitting the preferred school suggestions to the terminal; means for the terminal to display the preferred school suggestions to the user; means for the terminal to display the user's progress and feedback in a dashboard format; and means for generating prompt sentences using a generative AI model to generate an optimal learning plan or recovery plan based on the child's learning progress. This enables users to efficiently and effectively automate a series of learning support tasks, such as creating a learning plan, monitoring progress, providing a recovery plan, and suggesting preferred schools.

[1419] "User" refers to an individual who uses this system to receive services such as creating a study plan, monitoring progress, providing a recovery plan, and suggesting preferred schools.

[1420] "Terminal" refers to a device that a user operates to input information and receive feedback and suggestions from the server, and specifically includes smartphones, tablets, and PCs.

[1421] "Server" refers to a computer system that receives information sent by a user, creates a user profile based on this information, generates a study plan, recovery plan, and suggestions for preferred schools, and sends these to the terminal.

[1422] A "generative AI model" is an artificial intelligence model used by the server to analyze user information and learning data to generate learning plans and recovery plans.

[1423] A "prompt sentence" is an input sentence used to give instructions to a generative AI model, and is used to specify how to generate a specific learning plan or recovery plan.

[1424] A "study plan" refers to a schedule of learning content that is individually created taking into account the user's weaknesses and strengths, and includes specific learning tasks aimed at improving a child's academic ability.

[1425] A "recovery plan" refers to a supplementary learning plan or specific improvement measures that are deemed necessary as a result of analyzing a user's learning progress and performance data.

[1426] "School of choice suggestions" refers to the server recommending the most suitable school based on the user's characteristics, grades, and progress, and includes information for selecting the school that best suits the user's wishes and abilities.

[1427] "Dashboard" refers to an interface that displays on the device a user's learning progress, feedback, recovery plans, suggested schools of choice, and more, all at a glance.

[1428] "User profile" refers to a data set that includes detailed information such as the user's grade, grades, and preferred school, which is created by the server based on information received from the user.

[1429] "Evaluation data" refers to data used to evaluate a user's learning status, such as quizzes and class results provided by educational institutions.

[1430] This system allows users to input necessary information, and a server generates a study plan based on that information, monitors progress, and provides recovery plans as needed. It is designed to support users preparing for junior high school entrance exams, reducing the burden on parents and maximizing the learning outcomes of their children.

[1431] User registration and initial settings

[1432] The user uses a device to input information such as their child's grade, current grades, and desired school. This input data is sent from the device to a server. The device can be a smartphone, tablet, or PC.

[1433] Data collection and analysis

[1434] The server creates a user profile based on the received user information. The server also receives data from educational institutions, such as quizzes and class results, and stores them in a database. This data is analyzed using a generative AI model (e.g., "OpenAI GPT-4") to identify the child's weaknesses and strengths. The server sends the following prompt to the generative AI model:

[1435] text

[1436] Analyze this user's learning data to identify their weaknesses and strengths.

[1437] Generate a lesson plan

[1438] The server uses the generative AI model to generate a personalized learning plan based on the user's characteristics and performance data. This learning plan is sent to the user's device and displayed to the user. The specific prompt is as follows:

[1439] text

[1440] My fifth-grader wants to attend Junior High School A, but he is not good at science. Please create a specific study plan to help him overcome his science weaknesses.

[1441] Learning progression and monitoring

[1442] The user studies daily according to the generated study plan and enters their progress into the device. The entered progress data is sent from the device to the server. The server receives the progress data and evaluates the progress using a monitoring system.

[1443] Feedback and Recovery Plan

[1444] The server re-analyzes the latest learning data and past performance data received and generates a recovery plan as necessary. The generated recovery plan is sent to the user's device and displayed to the user. The specific prompt is as follows:

[1445] text

[1446] Please re-analyze this user's learning data and generate the necessary recovery plan.

[1447] Suggestion of desired school

[1448] The server then proposes the best schools to apply to based on the user's characteristics, grades, and progress data. These proposals are also sent from the server to the device and displayed to the user.

[1449] Dashboard View

[1450] The device displays the user's progress and feedback in the form of a dashboard, allowing the user to see their learning progress at a glance.

[1451] Specific examples

[1452] For example, let's say a user inputs the grade data of their child in the fifth grade of elementary school and indicates that they would like to attend Junior High School A. The user inputs the child's grade, grades, and desired school information into their device and submits it. The server receives this data, and the generative AI model analyzes it to determine that the child is "weak in science." The server then sends the following prompt to the generative AI model and obtains the result:

[1453] text

[1454] My fifth-grader wants to attend Junior High School A, but he is not good at science. Please create a specific study plan to help him overcome his science weaknesses.

[1455] Based on this prompt, the server generates a specific study plan, including "30 minutes of science review every day," and sends it to the device. The user studies according to this study plan and enters their progress into the device. The progress data is sent to the server, which monitors the progress and generates a recovery plan as necessary. Based on the progress data, the server suggests that "Junior High School B" is also suitable, and sends this information to the device and displays it to the user.

[1456] As described above, this system works in cooperation with the user, terminal, and server to support children's learning.

[1457] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1458] System program processing flow

[1459] Step 1: User registration and initial setup

[1460] Specific actions

[1461] Input: The user inputs information such as the child's grade, current grades, and desired school into the terminal.

[1462] How it works: Data entered by the user is sent from the device to the server. When the "Send" button is pressed using the device's application, this data is sent to the server.

[1463] Output: User information data received by the server.

[1464] Step 2: Collect and analyze data

[1465] Specific actions

[1466] Input: The server receives user input data and also receives quiz and class result data from the educational institution.

[1467] How it works: The server creates a user profile based on the data it receives and registers it in a database. It then inputs the received evaluation data into a generative AI model for analysis, identifying the child's weaknesses and strengths. Specifically, it sends the following prompt to the generative AI model:

[1468] text

[1469] Analyze this user's learning data to identify their weaknesses and strengths.

[1470] Output: The analysis results obtained from the generative AI model (child's weaknesses and strengths).

[1471] Step 3: Generate a lesson plan

[1472] Specific actions

[1473] Input: The analysis results received by the server from the generative AI model and the user's initial input data.

[1474] How it works: The server uses a generative AI model to generate a personalized learning plan based on this data. It sends specific prompts to the generative AI model:

[1475] text

[1476] My fifth-grader wants to attend Junior High School A, but he is not good at science. Please create a specific study plan to help him overcome his science weaknesses.

[1477] Output: The generated learning plan. This learning plan is sent from the server to the device.

[1478] Step 4: Progressing and monitoring your learning

[1479] Specific actions

[1480] Input: The user inputs their daily learning progress into the terminal.

[1481] How it works: The user enters progress information into the application on the device and presses the "Send progress" button. This data is sent from the device to the server. The server receives the progress data and evaluates the progress in the monitoring system.

[1482] Output: Learning progress data sent to the server. Evaluation results based on the progress data on the server.

[1483] Step 5: Feedback and recovery plan

[1484] Specific actions

[1485] Input: The server provides the latest learning progress data and past grade data.

[1486] How it works: The server re-analyzes this data and generates a recovery plan if necessary. It sends specific prompts to the generative AI model:

[1487] text

[1488] Please re-analyze this user's learning data and generate the necessary recovery plan.

[1489] Output: The generated recovery plan, which the server sends to the device.

[1490] Step 6: Propose your preferred school

[1491] Specific actions

[1492] Input: The server collects user characteristics, performance, and progress data.

[1493] How it works: The server uses this data to suggest the best schools to apply to. The suggestions are sent from the server to the device and displayed to the user.

[1494] Output: Proposed data for desired schools.

[1495] Step 7: View the dashboard

[1496] Specific actions

[1497] Input: Progress, feedback, recovery plan, and suggested school of choice data sent from the server.

[1498] How it works: The device receives this data and displays it in a dashboard format, allowing users to check their learning progress at a glance.

[1499] Output: Dashboard showing progress, feedback, recovery plan, and school preference suggestions.

[1500] These steps allow the user, terminal, and server to work together to create a system that supports children's learning.

[1501] (Application example 1)

[1502] 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."

[1503] In conventional risk management systems, risk assessment and progress monitoring were not fully automated, requiring personnel to manually organize and understand data. As a result, risk management efficiency was reduced and it was difficult to provide recovery plans quickly. In addition, progress could not be grasped in real time, resulting in delays in risk countermeasures.

[1504] 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.

[1505] In this invention, the server includes means for receiving risk information input by a user and generating a risk management plan using a generative AI model, means for evaluating risk management progress data and generating a recovery plan as necessary, and means for proposing appropriate risk management measures based on the user's risk information and progress. This enables automatic collection and analysis of risk information, automatic generation of management plans, real-time progress monitoring, and rapid provision of recovery plans.

[1506] Key Word Definitions:

[1507] "User"

[1508] is a person or organization that uses a system to input information and receive results.

[1509] "Required Information"

[1510] This refers to data necessary for risk management, such as company information, types of risks, and past security incidents.

[1511] "Terminal"

[1512] is an electronic device used by a user to input information and receive feedback from a system, including smartphones and tablets.

[1513] "server"

[1514] It is a communications device that receives and registers data sent by users, generates risk management plans and recovery plans using generative AI models, and sends them back to the terminal.

[1515] "Generative AI model"

[1516] This refers to algorithms that use machine learning and artificial intelligence techniques to analyze data and generate risk management and recovery plans.

[1517] "Risk Information"

[1518] is data about risk factors that may affect a company or organization, which can be entered by users via a terminal.

[1519] "Risk Management Plan"

[1520] A plan is generated using a generative AI model, which evaluates the risks that users need to address and includes specific countermeasures based on the assessment.

[1521] "Progress Data"

[1522] is data recorded by the user on their progress according to the risk management plan, which is sent to the server and used for evaluation.

[1523] "Recovery Plan"

[1524] A plan is one that includes alternative responses if things do not go as planned or new risks arise.

[1525] Dashboard

[1526] is an interface that visually organizes and displays information so that users can grasp the progress and current status of risk management at a glance.

[1527] "Risk management measures"

[1528] These are specific measures or steps that can be taken to address specific risks, and are proposed by the server based on the user's risk information and progress.

[1529] MODE FOR CARRYING OUT THE INVENTION

[1530] User registration and initial settings

[1531] First, the user uses a terminal to input the necessary information into the input form. This information includes company data related to risk management, risk types, past security incidents, etc. The input information is then sent from the terminal to the server. The terminal can be a smartphone or tablet and provides a user-friendly interface.

[1532] Data collection and analysis

[1533] The server registers the received user information and creates a user profile. The server also receives risk information and incident data from external sources. This data is analyzed by a generative AI model to classify and identify risk factors. The generative AI model is optimized using machine learning and artificial intelligence techniques.

[1534] Generate a risk management plan

[1535] The server uses the generative AI model to generate a personalized risk management plan based on the user's information. The plan includes specific countermeasures and a progress schedule. The risk management plan is then sent from the server to the device and displayed to the user.

[1536] Management Progression and Monitoring

[1537] Users report progress according to the risk management plan. They use their terminals to input progress and the status of countermeasure implementation, and send this to the server. The server monitors the progress data and evaluates the progress.

[1538] Feedback and Recovery Plan

[1539] The server re-analyzes the latest progress data and past risk data and generates a recovery plan as needed. This recovery plan may include additional or new countermeasures for specific risks. The generated recovery plan is sent to the terminal and displayed to the user.

[1540] Proposal of risk management measures

[1541] The server proposes optimal risk management measures based on the user's characteristics, risk information, and progress data, and these proposals are also sent to the terminal and displayed to the user.

[1542] Dashboard View

[1543] The device is equipped with a built-in dashboard displaying user progress and feedback, allowing users to see their risk management progress at a glance.

[1544] Specific examples

[1545] For example, when a user performs security risk management for "Small and Medium-sized Enterprise A," they first enter company information, risk types, and past incident data. The server then uses a generative AI model to create a risk profile for "Small and Medium-sized Enterprise A" and generates a risk management plan. This plan includes specific measures such as "30 minutes of security checks every day" and "data backup once a week." The user reports their progress according to this plan, and the server monitors the progress data. If progress is delayed, the server generates a new recovery plan and sends it to the device.

[1546] Examples of prompt statements

[1547] Here are some example prompts for a generative AI model:

[1548] "Our company is a small to medium-sized enterprise with 50 employees. We have experienced a data breach in the past. We are currently paying particular attention to phishing attacks and internal fraud risks."

[1549] In this way, the user can manage risk efficiently and effectively.

[1550] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1551] Program processing steps

[1552] Processing Steps

[1553] Step 1:

[1554] The user uses a terminal to input the required information, such as company information, risk type, and past security incidents. After the user enters this information into a form, the terminal sends the data to the server. This input data is used as the basis for generating a risk management plan.

[1555] Step 2:

[1556] The server registers the received data and creates a user profile. The server stores the received information in a database and organizes it as a user profile. It also acquires external risk information and past incident data and adds them to the database for analysis by the generative AI model. The input is user information sent from the device, and the output is the generation of a user profile.

[1557] Step 3:

[1558] The server uses a generative AI model to generate a risk management plan. The generative AI model analyzes risk factors based on the input user profile and risk data. This analysis generates an individual risk management plan. The generated plan includes specific countermeasures and progress schedules. The input is the user profile and risk data, and the output is a risk management plan.

[1559] Step 4:

[1560] The server sends the generated risk management plan to the terminal. The terminal displays this risk management plan to the user. The user starts to implement risk management based on the displayed plan. The input is the risk management plan, and the output is the display to the user.

[1561] Step 5:

[1562] The user reports progress according to the risk management plan. Using a terminal, the user inputs the progress status and the implementation status of countermeasures, and sends this data to the server. The input is the user's progress status data, and the output is the transmission of the progress data to the server.

[1563] Step 6:

[1564] The server monitors the progress data and evaluates the progress. The server analyzes the received progress data and identifies the progress and problems of risk management. If necessary, it generates a recovery plan. The input is the progress data, and the output is the generation of a recovery plan.

[1565] Step 7:

[1566] The server sends the generated recovery plan to the terminal, which displays it to the user. The recovery plan includes additional measures and new countermeasures for specific risks. The input is the recovery plan, and the output is the display to the user.

[1567] Step 8:

[1568] The server proposes appropriate risk management measures based on the user's risk information and progress. The generative AI model performs another analysis and proposes optimal management measures. The server sends this proposal to the terminal, which displays it to the user. The input is risk information and progress data, and the output is a risk management measure proposal.

[1569] Step 9:

[1570] The terminal displays the user's progress and feedback in the form of a dashboard. The dashboard displays progress, risk reviews, and feedback, allowing the user to check the situation in real time. The input is progress data and feedback data, and the output is the dashboard display.

[1571] 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.

[1572] This invention is a system to support preparation for junior high school entrance exams, monitoring the user's learning progress and emotional state, and providing a study plan and recovery plan based on this. This aims to reduce the user's burden while maximizing the learning effect.

[1573] This system has the basic functions of allowing users to input the necessary information, and then having the server generate a study plan based on that information, monitor progress, and provide a recovery plan if necessary. In addition, it also includes an emotion engine that recognizes the user's emotions.

[1574] User registration and initial settings

[1575] First, the user inputs the necessary information using the device, including the child's grade, current grades, preferred schools, and emotional data, which is then sent to the server.

[1576] Data collection and analysis

[1577] The server registers the received user information and creates a user profile. The server also receives quizzes and class results provided by the cram school and analyzes them using a generative AI model. Furthermore, the server uses the user's emotional data to adjust study plans and recovery plans.

[1578] Generate a lesson plan

[1579] The server uses a generative AI model to generate a personalized learning plan based on the user's characteristics, grades, and emotional state, which is then sent to the user's device and displayed to them.

[1580] Learning progression and monitoring

[1581] The user studies daily according to the study plan and inputs their learning progress into the device. The device then sends this progress data and emotional data to the server, which monitors and evaluates the progress data and emotional data.

[1582] Feedback and Recovery Plan

[1583] The server evaluates the user's progress based on the latest learning progress data and emotional data, and generates a recovery plan if necessary. The recovery plan is sent to the device and displayed to the user. For example, if the user is feeling stressed, adjustments may be made, such as adding tasks to help them relax.

[1584] Suggestion of desired school

[1585] The server will suggest the most suitable school to apply to based on the user's characteristics, grades, progress, and emotional data. These suggestions are also sent to the device and displayed to the user. It is also possible to select a school that offers a less stressful environment based on emotional data.

[1586] Dashboard View

[1587] The device displays the user's progress, feedback, and emotional data in a dashboard format, allowing the user to see at a glance their emotional changes as well as their learning progress.

[1588] Specific examples

[1589] For example, if a user inputs their child's grades and emotions and indicates their preference for Junior High School A, the server creates a profile based on the user's input. The server analyzes test results provided by the cram school, uses a generative AI model to identify that the child is weak in science, and uses an emotion engine to recognize that the child experiences high stress while studying science. The server then generates a study plan that includes 30 minutes of daily science review and adds a weekly relaxation task to reduce stress. This is sent to the device, and the user follows the study plan and enters their progress and emotional state into the device. The server monitors their progress and emotional state and provides a recovery plan as needed. The server also suggests Junior High School B as a suitable option, sending this information to the device and displaying it to the user.

[1590] As described above, this system works in cooperation with the user, terminal, and server to support the user's learning progress and emotional state.

[1591] The processing flow will be explained below.

[1592] Program processing steps

[1593] Step 1: User registration and initial setup

[1594] 1.1 The user selects the "New Registration" option from the initial screen of the device.

[1595] Specific operation: The user enters information such as "name," "grade," "current grades," "desired school," and "permission to collect emotional data" into an input form.

[1596] 1.2 The terminal sends the entered user information to the server.

[1597] Specific operation: The terminal encodes the input information into JSON format and sends a POST request to the server's API endpoint.

[1598] 1.3 The server registers the received user information in a database and creates a user profile.

[1599] Specific behavior: The server parses the received JSON data and saves it as a new user entry in the database.

[1600] Step 2: Collect and analyze data

[1601] 2.1 The server receives quizzes and class results provided by the cram school.

[1602] Specific operation: The server periodically retrieves test result data from the cram school's system via API.

[1603] 2.2 The server uses the generated AI model to analyze the test results and user performance data.

[1604] Specific operation: The server inputs the acquired test data into the generative AI model to analyze weaknesses and extract areas of strength.

[1605] 2.3 The device monitors the user's emotional state in real time and transmits the emotional data to the server.

[1606] Specific operation: The device uses the built-in camera and microphone to analyze the user's emotions from their facial expressions and voice, and sends the analysis results to the server.

[1607] Step 3: Generate a lesson plan

[1608] 3.1 The server generates an individualized learning plan based on the user's characteristics, performance data, and emotional data.

[1609] Specific operation: The server integrates the user's grades, exam trends at the school of choice, and emotional data to prioritize and schedule learning tasks.

[1610] 3.2 The server sends the generated learning plan to the device.

[1611] Specific operation: The server encodes the learning plan into JSON format and sends it to the device's API endpoint.

[1612] 3.3 The device visualizes the learning plan and displays it to the user.

[1613] Specific operation: The device displays the received study plan in calendar or list format and allows the user to confirm it.

[1614] Step 4: Progressing and monitoring your learning

[1615] 4.1 The user progresses through daily learning and inputs their learning progress into the device.

[1616] Specific operation: When the user finishes learning, he / she selects the content he / she learned on the "Learning Progress" screen of the device and presses the "Complete" button.

[1617] 4.2 The device sends learning progress data and emotion data to the server.

[1618] Specific operation: The device encodes the learning progress data entered by the user and the emotion data collected in real time into JSON format and sends it to the server.

[1619] Step 5: Feedback and recovery plan

[1620] 5.1 The server evaluates the progress based on the latest learning progress data and emotion data.

[1621] Specific operation: The server inputs new learning progress data and emotion data into the generative AI model and evaluates the user's progress.

[1622] 5.2 The server generates a recovery plan as needed.

[1623] Specific operation: Based on the progress assessment results, the server generates a recovery plan for the necessary reinforcement. For example, if the user is feeling stressed about a particular subject, the server will add tasks to reinforce that subject as well as tasks to help them relax.

[1624] 5.3 The server sends the recovery plan to the device.

[1625] Specific operation: The server encodes the generated recovery plan into JSON format and sends it to the terminal.

[1626] 5.4 The terminal visualizes the recovery plan and displays it to the user.

[1627] Specific operation: The device will display the received recovery plan in a pop-up notification or on the dashboard.

[1628] Step 6: Propose your preferred school

[1629] 6.1 The server suggests the best schools to apply to based on the user's characteristics, grades, progress data, and emotional data.

[1630] Specific operation: The server inputs learning data, past data, and emotional data into the generative AI model and creates a list of the most suitable schools for the user.

[1631] 6.2 The server sends the school preference suggestions to the device.

[1632] Specific operation: The server encodes the school preference proposals into JSON format and sends them to the device.

[1633] 6.3 The device displays school preference suggestions to the user.

[1634] Specific operation: The device displays the received list of preferred schools on the screen and provides the characteristics of each school and exam information.

[1635] Step 7: View the dashboard

[1636] 7.1 The device displays the user's progress, feedback, and emotional data in a dashboard format.

[1637] Specific operation: The device displays the latest learning data, progress reports, feedback, and emotional fluctuations on a single screen.

[1638] As a result, this system meticulously monitors the user's learning progress and emotional state, and provides effective study plans and appropriate recovery plans, thereby reducing the burden on parents and providing effective support for their children's junior high school entrance exams.

[1639] Example 2

[1640] 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."

[1641] In today's junior high school entrance exams, children are often exposed to hectic study schedules and pressure. This necessitates an efficient and effective learning management system. However, existing systems only monitor academic progress and do not take into account fluctuations in emotional states. This creates challenges, such as inadequate management of children's stress and anxiety, making it difficult to maximize overall learning outcomes.

[1642] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1643] In this invention, the server includes a means for creating a user profile, a means for receiving learning results and emotional data and utilizing a generative AI model for analyzing the data, and a means for generating an individual learning plan based on the user's characteristics and emotional data, thereby enabling a comprehensive learning support system that takes into account not only learning progress but also emotional states.

[1644] "User" refers to a person who utilizes the system to monitor their learning progress and emotional state and to receive learning and recovery plans.

[1645] "Terminal" refers to the electronic device used by the User to input necessary information and check the study plan, progress, recovery plan and suggested schools of choice.

[1646] "Server" refers to the central processing unit that receives and analyzes data sent from the terminal, generates study plans and recovery plans, monitors progress, and suggests preferred schools.

[1647] "Generative AI model" refers to an artificial intelligence model that analyzes learning progress, evaluates emotional state, and generates learning plans based on input data.

[1648] A "prompt sentence" refers to a specific sentence used to input instructions or questions to a generative AI model.

[1649] "Study Plan" refers to a personalized study schedule that is generated based on a user's characteristics, performance, and emotional state.

[1650] A "recovery plan" refers to additional learning tasks or stress reduction tasks that are adjusted as needed, taking into account the user's learning progress and emotional state.

[1651] "Emotion data" refers to information that indicates the user's emotional state, including stress and satisfaction during learning.

[1652] "Study results" refers to data that shows the results of a user's learning, such as test results and class grades.

[1653] "Suggesting a preferred school" refers to the act of the server recommending the most suitable preferred school based on the user's characteristics, grades, and emotional data.

[1654] "Dashboard format" refers to a format that visually displays a user's learning progress, emotional data, and feedback in an easy-to-understand manner.

[1655] This invention is a system to support preparation for junior high school entrance exams, monitoring the user's learning progress and emotional state, and providing a study plan and recovery plan based on this. This aims to reduce the user's burden while maximizing the learning effect.

[1656] User registration and initial settings

[1657] First, the user uses the device to input the necessary information, including the child's grade, current grades, preferred school, and emotional data, which is then sent to the server.

[1658] Data collection and analysis

[1659] The server registers the received user information and creates a user profile. The server also receives quizzes and class results provided by the cram school and analyzes them using a generative AI model. Furthermore, the server uses the user's emotional data to adjust study plans and recovery plans.

[1660] Generate a lesson plan

[1661] The server uses a generative AI model to generate a personalized learning plan based on the user's characteristics, grades, and emotional state, which is then sent to the user's device and displayed to them.

[1662] Learning progression and monitoring

[1663] The user studies daily according to the study plan and inputs their learning progress into the device. The device then sends this progress data and emotional data to the server, which monitors and evaluates the progress data and emotional data.

[1664] Feedback and Recovery Plan

[1665] The server evaluates the user's progress based on the latest learning progress data and emotional data, and generates a recovery plan if necessary. The recovery plan is sent to the device and displayed to the user. For example, if the user is feeling stressed, adjustments may be made, such as adding tasks to help them relax.

[1666] Suggestion of desired school

[1667] The server will suggest the most suitable school to apply to based on the user's characteristics, grades, progress, and emotional data. These suggestions are also sent to the device and displayed to the user. It is also possible to select a school that offers a less stressful environment based on emotional data.

[1668] Dashboard View

[1669] The device displays the user's progress, feedback, and emotional data in a dashboard format, allowing the user to see at a glance their emotional changes as well as their learning progress.

[1670] Specific examples

[1671] For example, if a user inputs their child's grades and emotions and indicates their preference for Junior High School A, the server creates a profile based on the user's input. The server analyzes test results provided by the cram school, uses a generative AI model to identify that the child is weak in science, and uses an emotion engine to recognize that the child experiences high stress while studying science. The server then generates a study plan that includes 30 minutes of daily science review and adds a weekly relaxation task to reduce stress. This is sent to the device, and the user follows the study plan and enters their progress and emotional state into the device. The server monitors their progress and emotional state and provides a recovery plan as needed. The server also suggests Junior High School B as a suitable option, sending this information to the device and displaying it to the user.

[1672] Prompt Sentence Examples

[1673] Examples of prompts to input to a generative AI model include:

[1674] "Your fifth-grade child is performing poorly in science, and emotional data indicates high levels of stress. Please create an appropriate learning plan and recovery plan to reduce stress for this child."

[1675] As described above, this system works in cooperation with the user, terminal, and server to support the user's learning progress and emotional state.

[1676] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1677] Step 1:

[1678] The user uses the terminal to input necessary information such as the child's grade, current grades, desired school, emotional data, etc. Once this input is complete, the terminal transmits this data to the server.

[1679] Step 2:

[1680] The server creates a user profile based on the user information received from the terminal, and registers the user's basic information and initial settings in the database.

[1681] Step 3:

[1682] The server receives quiz and class result data provided by the cram school. This data includes grades for each subject and learning progress. The server inputs this data into the generative AI model.

[1683] Step 4:

[1684] The generative AI model analyzes the learning results and emotional data it receives. The model analyzes grades for each subject and the user's emotional state, such as stress and satisfaction. The analysis results are stored on the server.

[1685] Step 5:

[1686] Based on the analysis results of the AI ​​model, the server generates an individualized study plan that takes into account the user's characteristics, grades, and emotional state. The study plan includes study time and review content for each subject, as well as stress reduction tasks according to emotions.

[1687] Step 6:

[1688] The server sends the generated lesson plan to the terminal, which receives the lesson plan and displays it to the user.

[1689] Step 7:

[1690] The user studies daily based on a study plan. After studying, the user inputs their study progress and emotional state into the device. The input data is sent from the device to the server.

[1691] Step 8:

[1692] The server receives and monitors the progress and emotion data sent by the user, and evaluates the user's learning and emotional fluctuations based on the monitoring results.

[1693] Step 9:

[1694] Based on the progress and emotional state assessment, the server generates a recovery plan as needed, which may include additional learning tasks or relaxation tasks to reduce stress.

[1695] Step 10:

[1696] The server sends the generated recovery plan to the terminal, which receives the recovery plan and displays it to the user.

[1697] Step 11:

[1698] The server selects the most suitable school based on the user's characteristics, grades, progress, and emotional data, and generates a proposal of the school. The proposal is sent from the server to the terminal.

[1699] Step 12:

[1700] The device displays the suggested schools to the user, who can then review them and use them as a reference for making a selection.

[1701] Step 13:

[1702] The device displays the user's learning progress, emotional data, and feedback in the form of a dashboard, allowing the user to visually check their learning progress and emotional fluctuations.

[1703] Specific input and output examples

[1704] For example, suppose a user inputs grade data from their fifth grade (Japanese: 80 points, Math: 70 points, Science: 60 points) and emotional data indicating that they feel high stress while studying science. This data is sent from the device to the server. The server analyzes this data and generates a study plan that includes "30 minutes of science review every day" and "a relaxation task once a week." This study plan is sent to the device and displayed to the user.

[1705] Prompt Sentence Examples

[1706] The prompt to the generative AI model is as follows:

[1707] "Your fifth-grade child is performing poorly in science, and emotional data indicates high levels of stress. Please create an appropriate learning plan and recovery plan to reduce stress for this child."

[1708] Through the above steps, this system works in cooperation with the user, terminal, and server to effectively support the user's learning progress and emotional state.

[1709] (Application example 2)

[1710] 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 robot 414 will be referred to as a "terminal."

[1711] There is a growing need for a system that can effectively manage the learning progress and emotional fluctuations of junior high school entrance exam students and provide individually optimized learning plans. Conventional methods have made it difficult to timely monitor learning progress and emotional fluctuations and appropriately adjust learning and recovery plans based on that information. Furthermore, the lack of an environment for receiving real-time learning support has made it difficult to reduce students' stress and improve their learning efficiency.

[1712] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1713] In this invention, the server includes a means for monitoring the user's emotional state and transmitting emotional data to the server, a means for adjusting a study plan or recovery plan based on the emotional data, and a means for the terminal to access a virtual store through smart glasses or a head-mounted display and provide learning content in real time. This makes it possible to comprehensively manage the learning progress and emotional state of junior high school entrance exam students and provide individually optimized real-time learning support.

[1714] "Users" refers to junior high school entrance exam students and their parents, who are the entities that use the system.

[1715] "Necessary information" refers to data necessary to generate a study plan and recovery plan, such as grade level, current grades, desired school, and emotional data.

[1716] "Server" means a data processing device that receives, registers, and analyzes data sent by users, and generates and adjusts learning plans and recovery plans.

[1717] "Device" refers to a device used by a user, such as a smartphone, tablet, or PC, that provides an interface for displaying and inputting learning plans and learning data.

[1718] "Generative AI model" refers to an artificial intelligence algorithm that analyzes received data and generates learning and recovery plans.

[1719] A "study plan" is a personalized study schedule and tasks that is generated based on a user's performance data and emotional data.

[1720] A "recovery plan" is a complementary learning plan created to improve learning progress or emotional state when there is a problem.

[1721] "Emotional state" refers to data that indicates the user's mental state, such as stress or excitement, during learning.

[1722] A "virtual store" is a virtual learning environment accessible over the internet that provides real-time learning content through smart glasses or head-mounted displays.

[1723] "Smart glasses" are glasses-type devices that have a built-in display and camera that display information and provide users with an augmented reality (AR) experience.

[1724] A "head-mounted display" is a display device that users can wear to experience virtual reality (VR) and augmented reality (AR).

[1725] "Learning support" refers to a series of support services that monitor the user's learning progress and emotional state and provide optimal learning and recovery plans.

[1726] The present invention is a system that comprehensively manages the learning progress and emotional state of junior high school entrance exam students, providing individually optimized real-time learning support. In this system, the user inputs necessary information, and the server generates and adjusts learning plans and recovery plans based on that information, and provides them to the user via a terminal. Detailed embodiments of the present invention are described below.

[1727] User registration and initial settings

[1728] Users use devices such as smartphones or PCs to input the necessary information, including their grade, current grades, preferred schools, and emotional data. To access the virtual store, users wear smart glasses or a head-mounted display. This information is then sent to a server via the Internet.

[1729] Data collection and analysis

[1730] The server registers the received user information and creates a user profile. At the same time, the server receives data such as quizzes and class results from cram schools and online learning platforms. The generative AI model analyzes this data and identifies the user's weaknesses and strengths. Furthermore, emotion recognition sensors built into the smart glasses or head-mounted display are used to collect user emotion data.

[1731] Generate and deliver lesson plans

[1732] The server uses a generative AI model to automatically generate an individualized study plan based on the user's performance and emotional data. This study plan includes study tasks to address the user's weaknesses and additional tasks to improve performance. The generated study plan is sent to the user's device via the Internet and displayed to the user.

[1733] Learning progression and monitoring

[1734] The user follows the generated study plan as they study daily. Their learning progress and emotional state are entered into the device and periodically sent to the server. The server monitors this data and evaluates their progress. If necessary, a recovery plan is automatically generated and sent to the device.

[1735] Feedback and Recovery Plan

[1736] The server evaluates the progress and emotion data and generates a recovery plan as needed. The recovery plan is adjusted if the user is feeling stressed or if progress is slowing down. This recovery plan is also displayed on the device and provided to the user.

[1737] Suggestion of desired school

[1738] The server suggests the most suitable school to apply to based on the user's characteristics, grades, progress, and emotional data. The suggested schools are sent to the user's device and notified to the user. For example, if the user is in an environment where they are prone to stress, the server can suggest schools that are predicted to be less stressful.

[1739] Use of virtual stores

[1740] Users can access real-time learning content by wearing smart glasses or a head-mounted display and accessing a virtual store. This content is provided by a server and supports the user's learning progress.

[1741] Examples and prompts

[1742] For example, if a user inputs their child's grades and emotions from their fifth-grade elementary school and indicates their preference for a specific junior high school, the server will create a profile based on this data. The server analyzes test results provided by the cram school, using a generative AI model to identify that the child is weak in a particular subject, and an emotion engine to recognize that the child is experiencing high levels of stress while studying that subject. The server then generates a study plan that includes "30 minutes of daily review" and adds a weekly relaxation task. This is then sent to the device, and the user enters their progress and emotional state according to the study plan.

[1743] An example prompt is, "Generate the best individualized learning and recovery plan based on the student's current grades, learning progress, and emotional data."

[1744] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1745] Step 1:

[1746] The user uses a terminal to input necessary information (grade, grades, desired school, emotional data, etc.). This input data is sent to a server via the Internet. For example, the input information might be a current grade of 80 points and the desired school being A Junior High School, and the output is user profile data sent to the server.

[1747] Step 2:

[1748] The server registers the received user information and creates a user profile. The received data is stored on the server and stored in various databases in preparation for analysis by the generative AI model. The input is the user's basic information and performance information, and the output is structured user profile data.

[1749] Step 3:

[1750] The server receives data such as quiz and class results from cram schools and online learning platforms. This learning data is combined with the user's grade data and analyzed. The input is the quiz results and class content data, and the output is data for analysis that is stored in a database on the server.

[1751] Step 4:

[1752] The server uses a generative AI model to analyze the user's performance data and emotional data to identify the user's weaknesses and strengths. The generative AI model processes the received data as input and analyzes learning patterns and emotional fluctuations. The output is a list of each user's weak and strong subjects.

[1753] Step 5:

[1754] The server automatically generates an individualized study plan based on the analysis results. The generative AI model takes into account the user's grades and emotional state to create the optimal study plan. For example, if the user is determined to have a weak point in math, the study plan will include "reviewing math for 30 minutes every day." The input is the analyzed grade data and emotional data, and the output is an individualized study plan.

[1755] Step 6:

[1756] The server sends the generated lesson plan to the terminal via the Internet. The lesson plan is displayed on the user's terminal and lists specific learning tasks. The input is the generated lesson plan, and the output is the lesson plan displayed on the user's terminal.

[1757] Step 7:

[1758] The user uses the device to follow the learning plan and input their learning progress and emotional state. The device collects this data and periodically transmits it to the server. The input is the learning progress data and emotional data entered by the user, and the output is the progress data transmitted to the server.

[1759] Step 8:

[1760] The server monitors progress data and emotion data to evaluate the progress. If there are delays in progress or emotional fluctuations, the generative AI model automatically generates a recovery plan. The input is learning progress data and emotion data, and the output is a recovery plan.

[1761] Step 9:

[1762] The server sends the generated recovery plan to the device via the Internet. The recovery plan is displayed on the device in the same way as a learning plan. For example, if the user is feeling stressed, the recovery plan will include tasks for relaxation. The input is the generated recovery plan, and the output is the recovery plan displayed on the user's device.

[1763] Step 10:

[1764] The server suggests the most suitable schools to apply to based on the user's characteristics, grades, and progress data. The generative AI model analyzes the data and lists the most suitable schools for the user. The input is the user's comprehensive data, and the output is a list of preferred schools.

[1765] Step 11:

[1766] The server sends the information on the schools it recommends to the terminal. The terminal displays this information to the user, including detailed information about the schools and their advantages and disadvantages. The input is the school recommendation data from the server, and the output is a list of schools displayed on the user's terminal.

[1767] Step 12:

[1768] Users wear smart glasses or a head-mounted display and access the virtual store, where they can receive real-time learning content and progress with their studies. The input is the device worn by the user, and the output is the real-time learning content provided by the virtual store.

[1769] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1770] 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.

[1771] 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 robot 414.

[1772] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1773] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1774] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1775] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1776] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1777] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1778] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are c...

Claims

1. a means for the user to input required information; means for transmitting the input information to a server; means for registering the received data by the server and creating a user profile; A server receives quizzes and class results and uses generative AI models to analyze the data. a means for the server to identify the user's weaknesses and strengths and generate a personalized learning plan; A means for the server to transmit the generated learning plan to the terminal; means for the terminal to display the learning plan to the user; A means for a user to input learning progress into a terminal, and the terminal transmits the progress data to a server; a means for the server to assess progress and generate a recovery plan; A means for the server to transmit the recovery plan to the terminal; a means for the terminal to display a recovery plan; A means for the server to suggest schools of choice based on the user's characteristics, grades, and progress; A means for the server to transmit the preferred school suggestions to the terminal; a means for the terminal to display preferred school suggestions to the user; means for the device to display the user's progress and feedback in a dashboard format; A system including:

2. 10. The system of claim 1, wherein the server further comprises means for reanalyzing the latest learning data and past performance data and generating additional learning tasks for the user.

3. 2. The system of claim 1, wherein the terminal includes means for recording the user's learning progress and periodically transmitting the progress to the server.

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