System

Abstract

A system is provided.SOLUTION: A learning support system comprising: means for inputting and storing student performance data and personality diagnosis results; means for analyzing student performance data and personality diagnosis results and generating learning advice; means for automatically generating and displaying student learning schedules; means for monitoring and reporting student learning progress in real time; and means for sending notifications to students, teachers, and parents.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In the current education system, there is often a lack of time and resources to provide appropriate learning guidance to each student. Students also feel anxious about their own learning progress and have difficulty understanding what and how they should study. Furthermore, parents want to support their children but are unsure of how to get involved. If these issues are left unresolved, not only will students' academic ability not improve, but their motivation to learn may also decline. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means. Specifically, a learning support system is provided that includes: a means for inputting and storing students' academic performance data and personality assessment results using AI; a means for analyzing this data and generating study advice; a means for automatically generating and displaying students' study schedules; a means for monitoring students' study progress and reporting it in real time; and a means for sending notifications to students, teachers, and parents. Furthermore, by including a means for collecting students' daily facial expressions and condition information and a means for adjusting study advice and schedules based on the collected information, optimal learning support tailored to each student's individual condition is possible. Furthermore, by including a means for receiving test results entered by teachers and storing them in a database and a means for notifying teachers and parents of reports on students' study progress, an environment is created in which all educational stakeholders can work together to efficiently support students' academic improvement.

[0006] "Academic performance data" refers to data that includes the evaluation results of student tests, exams, assignments, and other materials.

[0007] "Personality diagnosis results" are data that includes the results of an analysis of a student's personality, behavioral patterns, learning style, etc.

[0008] "Study advice" is the content that suggests optimal study methods and study plans based on the student's academic performance data and personality assessment results.

[0009] A "study schedule" is a plan that includes specific time allocation and learning content to help students study effectively.

[0010] "Progress" is information that indicates the degree to which a student has achieved the learning content that he or she has carried out based on the learning advice and study schedule.

[0011] "Notification" is the means by which the system communicates information about study advice, study schedules, and progress to students, teachers, and parents.

[0012] "Daily expressions" are information that indicates the emotions and condition that can be read from students' facial expressions.

[0013] "Condition information" includes information such as a student's physical condition, mood, and stress level.

[0014] A "data collection module" is a device or program that has the function of collecting students' academic performance data, personality test results, and daily facial expressions and condition information, and storing them in a database.

[0015] A "data analysis module" is a device or program that has the function of analyzing collected data and generating study advice for students.

[0016] The "study schedule generation module" is a device or program that has the function of automatically generating and displaying a student's study schedule based on the analysis results.

[0017] A "progress management system" is a device or program that monitors students' learning progress and reports it to teachers and parents in real time.

[0018] A "notification system" is a device or program that has the ability to send important alerts and announcements to students, teachers, and parents. [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 relates to a learning support system that uses AI to help students improve their grades. The system is used by three parties: a server, a terminal, and users (students, teachers, and parents). Specific embodiments for implementing this invention are described below.

[0041] System configuration

[0042] 1. Server

[0043] The server includes a data collection module, a data analysis module, a study schedule generation module, a progress management system, and a notification system.

[0044] The server centrally manages all performance data, personality test results, daily facial expressions and condition information, and plays a central role in analyzing and notifying users.

[0045] 2. Terminal

[0046] Devices are devices used by students, teachers, and parents, and include computers, tablets, and smartphones.

[0047] The device connects to the server and allows users to enter data, view study advice, report progress, and check notifications.

[0048] 3. Users (students, teachers, parents)

[0049] Each user uses a terminal to perform operations according to his or her role.

[0050] Students review and complete their learning tasks and report their progress.

[0051] Teachers enter performance data and monitor learning progress.

[0052] Parents can monitor and support their students' learning progress and notifications.

[0053] Explanation of program processing

[0054] Data collection and input

[0055] 1. User (Teacher)

[0056] The teacher launches the application on the terminal, enters performance data such as test results, and sends it to the server.

[0057] Personality test results and condition information are updated regularly.

[0058] 2. Terminal

[0059] The entered performance data is received, converted into an appropriate format, and sent to the server.

[0060] To provide a UI that allows users to input information intuitively.

[0061] 3. Server

[0062] The data collection module stores the received performance data and personality test results in a database.

[0063] Accumulates condition information and daily facial expression data.

[0064] Data analysis and generation of learning advice

[0065] 1. Server

[0066] The data analysis module analyzes the information in the database to identify students' learning patterns and problems.

[0067] Based on the results of personality assessment and condition information, optimal learning advice is generated, taking into consideration ways to improve learning performance.

[0068] Generate a study schedule

[0069] 1. Server

[0070] The learning schedule generation module automatically generates learning schedules for each student based on the analysis results.

[0071] The schedule created should have a structure that allows it to be adjusted as needed.

[0072] 2. Terminal

[0073] The learning schedule is sent to the student's device and a list of learning tasks is displayed.

[0074] You can visually check today's tasks, weekly schedule, etc.

[0075] Progress monitoring and notifications

[0076] 1. User (Student)

[0077] Students carry out learning tasks and enter progress information into the device after completion.

[0078] 2. Terminal

[0079] Receives progress information and sends it to the server.

[0080] 3. Server

[0081] The progress information received by the progress management system is updated to the database.

[0082] Monitor learning progress and generate and send real-time reports to teachers and parents.

[0083] 4. Server

[0084] A notification system sends important alerts and announcements to students, teachers and parents.

[0085] Specific use cases

[0086] For example, one day, a teacher enters the results of a mathematics test on a device and sends them to the server. The server stores the received score data in a database using a data collection module, and the data analysis module generates study advice based on that data. This advice is specific, such as "It's a good idea to review basic mathematics problems for three hours a week." Next, the study schedule generation module automatically creates a study schedule based on this advice and sends it to the student's device. The student checks this schedule and performs the tasks for the day. Once the tasks are completed, the student enters progress information into the device and sends it to the server. The server updates this information in a progress management system and reports it to teachers and parents in real time. Important progress and alerts are sent to all relevant parties via a notification system, allowing them to take appropriate action.

[0087] The system described above allows students, teachers, and parents to work together to effectively support students' academic improvement. In this way, the present invention realizes a useful and continuous learning support environment.

[0088] The processing flow will be explained below.

[0089] Step 1:

[0090] The user (teacher) starts the application on the terminal and inputs test result data, including the grade data for each student, the number of correct answers, and comments.

[0091] Step 2:

[0092] The terminal receives the entered test results, converts the data into an appropriate format (e.g., JSON format), and sends it to the server.

[0093] Step 3:

[0094] The server's data collection module receives the test result data sent from the terminal, temporarily stores the received data in a cache, and then stores it in a database.

[0095] Step 4:

[0096] The server's data analysis module retrieves new test result data from the database, while also matching the student's previous performance data and personality assessment results.

[0097] Step 5:

[0098] The server's data analysis module uses AI models to analyze academic performance data and personality assessment results, identifying students' weak points in certain subjects and topics, as well as their learning patterns.

[0099] Step 6:

[0100] The server's data analysis module generates personalized study advice based on the analysis results, such as suggesting specific study content such as "review basic math problems for three hours a week."

[0101] Step 7:

[0102] The server's learning schedule generation module receives the generated learning advice and automatically generates a learning schedule for the student based on it, which includes dates, time slots, subjects, specific learning tasks, etc.

[0103] Step 8:

[0104] The server sends the generated learning schedule to the student's terminal, which receives the schedule and displays it on the user interface.

[0105] Step 9:

[0106] The user (student) checks the learning schedule on the terminal and performs the learning tasks according to the schedule. After completing the learning, the student enters the progress information on the terminal.

[0107] Step 10:

[0108] The terminal receives progress information entered by the student and sends it to the server.

[0109] Step 11:

[0110] The progress management system on the server receives the progress data sent from the devices and updates the database, monitors the updated progress, and adjusts the student's schedule as necessary.

[0111] Step 12:

[0112] The server-based progress management system automatically generates and sends updated progress reports to teachers and parents in real time, including progress levels, remaining tasks, and recommended actions.

[0113] Step 13:

[0114] The server's notification system sends important alerts and announcements to students, teachers, and parents, including conference reminders and changes to learning schedules.

[0115] Step 14:

[0116] Users (teachers, parents) can view reports and notifications on their devices and take necessary actions, such as sending praise or planning additional learning support.

[0117] Through the above processing steps, the AI-based learning support system comprehensively supports students in improving their academic ability.

[0118] Example 1

[0119] 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."

[0120] In today's educational environment, there is a lack of systems that effectively support the learning progress of individual students. In particular, there is a need for systems that can comprehensively handle information on students' grades, personalities, and daily conditions, provide appropriate learning advice, and monitor and report individual learning progress in real time. There is also a need for systems that allow teachers and parents to easily check students' progress. To solve these issues, there is a need for the development of an integrated learning support system that allows students, teachers, and parents to work together effectively.

[0121] 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.

[0122] In this invention, the server includes a means for inputting and storing a student's academic performance data and personality assessment results, a means for analyzing the student's academic performance data and personality assessment results and generating study advice, and a means for automatically generating and displaying the student's study schedule. This makes it possible to identify a student's study patterns and problems and provide optimal study advice to each individual. The server also includes a means for monitoring a student's study progress and reporting it in real time, a means for sending notifications to students, teachers, and parents, a terminal means for converting input data into an appropriate format and sending it to the server, an analysis means for identifying study patterns and problems and generating optimal study advice, and a notification means for appropriately sending important alerts and notifications. This enables students, teachers, and parents to work together to effectively support their study progress.

[0123] "Academic performance data" refers to information including the scores and evaluation results obtained by a student on tests and assessments.

[0124] "Personality assessment results" are diagnostic results that are used to evaluate students' personalities and optimize learning methods and approaches.

[0125] "Condition information" includes information about a student's daily health, mood, fatigue level, etc.

[0126] "Study advice" is advice that shows optimal study methods and areas for improvement based on students' academic performance data and personality assessment results.

[0127] A "study schedule" is a schedule that systematically arranges the learning tasks and activities that students should complete within a certain period of time.

[0128] "Learning progress" is information that indicates the completion status and progress of the learning tasks performed by the student.

[0129] "Monitoring" is the act of regularly observing students' learning progress and recording it as data.

[0130] "Notifications" are messages intended to communicate important information or alerts to students, teachers, and parents.

[0131] A "terminal" is a device used by students, teachers, and parents that works in conjunction with a server to input and display data.

[0132] The "data collection module" is a program that has the function of receiving performance data, condition information, etc. and storing it in a database.

[0133] The "data analysis module" is a program that has the function of analyzing received data and identifying students' learning patterns and problems.

[0134] The "study schedule generation module" is a program with the function of automatically generating individual student study schedules.

[0135] A "progress management system" is a system for updating student progress information in a database and monitoring learning progress.

[0136] A "notification system" is a system for sending important information and alerts to students, teachers, and parents.

[0137] This invention relates to a learning support system that uses AI to help students improve their grades. This system is used by three parties: a server, a terminal, and users (students, teachers, and parents). Specific modes for carrying out the invention are described below.

[0138] System configuration

[0139] 1. Server

[0140] The server includes a data collection module, a data analysis module, a study schedule generation module, a progress management system, and a notification system.

[0141] The server centrally manages performance data, personality test results, daily facial expressions and condition information, and plays a central role in analyzing and notifying users.

[0142] 2. Terminal

[0143] Devices are devices used by students, teachers, and parents, and include computers, tablets, and smartphones.

[0144] The device connects to the server and allows users to enter data, view study advice, report progress, and check notifications.

[0145] 3. Users (students, teachers, parents)

[0146] Each user uses a terminal to perform operations according to his or her role.

[0147] Students review and complete their learning tasks and report their progress.

[0148] Teachers enter performance data and monitor learning progress.

[0149] Parents can monitor and support their students' learning progress and notifications.

[0150] Explanation of program processing

[0151] Data collection and input

[0152] User (Teacher)

[0153] The teacher starts a dedicated application on the device and inputs test results, evaluations, personality test results, and condition information. The device receives this data, converts it into an appropriate format, and sends it to the server.

[0154] For example, a teacher enters math test results into an application and clicks a "submit" button, sending the data to a server.

[0155] Receiving and storing data

[0156] server

[0157] The server receives the data sent from the device using a data collection module and stores it in a database, allowing for centralized management of performance data, personality assessment results, condition information, and other data.

[0158] For example, the server may store the received math test results in a database for further analysis.

[0159] Data analysis and generation of learning advice

[0160] server

[0161] The data analysis module analyzes academic performance data and personality assessment results to identify students' learning patterns and problems. Based on the analysis results, a generative AI model is used to generate optimal learning advice.

[0162] For example, the generated advice could be specific, such as "It would be good to review basic math problems for three hours a week."

[0163] Generate and submit a study schedule

[0164] server

[0165] The learning schedule generation module automatically generates individual learning schedules for each student based on the analysis results. The generated schedules are sent to the student's device, where they can review them and proceed with their studies.

[0166] For example, the generated schedule may include a specific task list such as "Monday: 2 hours of math, Tuesday: 1 hour of English."

[0167] Complete learning tasks and submit progress information

[0168] User (student)

[0169] Students check their study schedule on their devices and proceed with their studies according to the plan. When they complete a task, they enter their progress information into the device and send it to the server.

[0170] For example, on the "Today's Tasks" screen, check the completed tasks and click the "Submit" button.

[0171] Progress updates and reporting

[0172] server

[0173] The progress management system updates the received progress information into a database, monitors learning progress, and creates and sends real-time reports to teachers and parents.

[0174] For example, student progress updates are sent to teachers and parents via email and push notifications.

[0175] Sending notifications

[0176] server

[0177] The notification system sends important alerts and announcements to students, teachers and parents, allowing them to take timely action.

[0178] For example, notifications are sent when there is significant improvement in performance or when a particular learning task is not completed.

[0179] Specific use cases

[0180] For example, one day, a teacher enters the results of a math test into a device and sends them to the server. The server's data collection module stores the received grade data in a database, and the data analysis module analyzes the data and generates study advice. This advice is specific, such as "It's a good idea to review basic math problems for three hours a week." The study schedule generation module then automatically creates a study schedule based on this advice and sends it to the student's device. The student checks this schedule and performs the tasks for the day. Once the tasks are completed, the student enters progress information into the device and sends it to the server. The server updates this information in the progress management system and reports it to teachers and parents in real time. Important progress and alerts are sent to all relevant parties via the notification system, allowing them to take appropriate action.

[0181] Example prompts for generative AI models

[0182] Q: What is the specific process from when a teacher enters test results to when study advice is generated?

[0183] Answer: The teacher launches the application on the device, enters the test results, and sends them to the server. The device receives the performance data, converts it into the appropriate format, and sends it to the server. The server's data collection module receives the data and stores it in a database. The data analysis module then analyzes the performance data and personality assessment results and generates study advice using a generative AI model. This advice is used to create an individualized study schedule.

[0184] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0185] Step 1: Enter and submit grade data

[0186] User (Teacher)

[0187] The teacher launches a dedicated application on the device.

[0188] Enter test results, evaluations, personality test results, and condition information.

[0189] This performs the operation to send the data entered by the teacher to the server.

[0190] Input: Test results, evaluation, personality test results, condition information

[0191] Output: The formatted data sent to the server

[0192] Specific behavior:

[0193] Launch the application on your device.

[0194] Enter the score in the Test Result field.

[0195] Enter your personality test results and condition information in the corresponding fields.

[0196] Click the "Send" button.

[0197] Step 2: Receiving and storing data

[0198] Terminal

[0199] The terminal receives the input data and converts it into the appropriate format.

[0200] The converted data is sent to the server.

[0201] server

[0202] The server stores the data received by the data collection module in a database.

[0203] Input: Formatted data (grade data, personality test results, condition information)

[0204] Output: Data stored in the database

[0205] Specific behavior:

[0206] The terminal converts the data into JSON format.

[0207] Sends data to the server via an HTTP POST request.

[0208] On the server side, the data collection module receives the request and stores it in a database.

[0209] Step 3: Analyze the data and generate learning advice

[0210] server

[0211] The data analysis module analyzes the academic performance data and personality test results in the database.

[0212] Identify learning patterns and identify pain points.

[0213] Generative AI models are used to generate optimal learning advice.

[0214] Input: Academic data in the database, personality test results

[0215] Output: The generated study advice

[0216] Specific behavior:

[0217] The data analysis module extracts the required data from the database using SQL queries.

[0218] Use generative AI models (e.g., GPT-3) to generate personalized advice from analyzed data.

[0219] Step 4: Generate and submit your study schedule

[0220] server

[0221] The learning schedule generation module automatically generates individual learning schedules for each student based on the analysis results.

[0222] The learning schedule is sent to the device, and a visual learning task list is displayed on the student's device.

[0223] Input: Analysis results, generated learning advice

[0224] Output: Study schedule sent to student devices

[0225] Specific behavior:

[0226] The learning schedule generation module creates the schedule in JSON format.

[0227] Send the schedule to the device using an HTTP POST request.

[0228] The schedule is displayed on the device in calendar or list format.

[0229] Step 5: Complete the learning task and submit progress information

[0230] User (student)

[0231] Students check their study schedule on their devices and proceed with their studies according to the plan.

[0232] Once the learning task is completed, progress information is entered into the terminal and sent to the server.

[0233] Input: Progress information for completed learning tasks

[0234] Output: Progress information sent to the server

[0235] Specific behavior:

[0236] Open the "Today's Tasks" screen on your device.

[0237] Complete each task and click the "Done" button when complete.

[0238] After entering the progress information, click the "Submit" button.

[0239] Step 6: Update and report progress

[0240] Terminal

[0241] The device receives the progress information entered by the student and sends it to the server.

[0242] server

[0243] The progress management system updates the received progress information in the database.

[0244] The system monitors progress and generates and sends real-time reports to teachers and parents.

[0245] Input: Progress information sent from the device to the server

[0246] Output: Progress information updated in database, reports to teachers and parents

[0247] Specific behavior:

[0248] The device converts the progress data into JSON format.

[0249] Sends progress data to the server via HTTP POST request.

[0250] The progress management system updates the database on the server side and sends notification emails to teachers and parents.

[0251] Step 7: Sending notifications

[0252] server

[0253] A notification system sends important alerts and announcements to students, teachers and parents.

[0254] Input: Critical alerts and notifications

[0255] Output: Message to notify all involved parties

[0256] Specific behavior:

[0257] The notification system periodically checks for alert conditions.

[0258] When an alert occurs, push notifications and emails are sent to students, teachers, and parents.

[0259] (Application example 1)

[0260] 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."

[0261] To improve the safety and efficiency of autonomous vehicle operations, a system is needed that collects and analyzes vehicle driving data and environmental data in real time and provides optimal driving advice and schedules. However, current systems do not adequately collect and analyze driving data, and there is insufficient cooperation between drivers, supervisors, and maintenance personnel, making it difficult to properly manage operations and respond quickly.

[0262] 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.

[0263] In this invention, the server includes means for inputting and storing vehicle driving data and environmental data, means for analyzing the vehicle driving data and environmental data and generating driving advice, and means for automatically generating and displaying an optimal driving schedule. This enables real-time monitoring of the operation of autonomous vehicles, simultaneously improving safety and efficiency, and enabling quick responses between drivers, supervisors, and maintenance personnel.

[0264] "Vehicle" means a means of movement that is mechanically powered and is used to transport people or goods.

[0265] "Driving data" is a general term for information acquired during driving, such as the vehicle's speed, position, direction, and acceleration.

[0266] "Environmental data" refers to data such as meteorological information, road conditions, and traffic information that is collected to understand the situation around the vehicle.

[0267] "Server" means a central computer system for collecting, analyzing, and storing data and sending necessary notifications and instructions.

[0268] "Driving advice" is specific instructions or suggestions provided to improve vehicle safety or efficiency.

[0269] A "service schedule" is a plan for a vehicle to complete a specific route or task within a specific time frame.

[0270] "Monitoring" is the act of monitoring the current status and progress of a vehicle in real time.

[0271] "Notifications" are messages or alerts that convey important information or warnings to operators, supervisors, and maintenance personnel.

[0272] A "supervisor" is a person whose role is to manage the vehicle's operation status and ensure that it is being driven appropriately.

[0273] A "maintenance technician" is a person who performs maintenance and repairs on vehicles and supports their safe and normal operation.

[0274] "Progress" is a status indicating the degree of accomplishment of the task currently being performed by the vehicle.

[0275] This invention relates to a system that utilizes AI to effectively support the operation of autonomous vehicles. The system is used by three parties: a server, a terminal, and users (drivers, supervisors, and maintenance personnel). Specific embodiments for implementing this invention are described below.

[0276] System configuration

[0277] 1. Server

[0278] The server includes a driving data collection module, a data analysis module, a driving schedule generation module, a progress management system, and a notification system.

[0279] The server plays a central role in centrally managing all operational data, environmental data, daily performance information, etc., and analyzing and reporting them.

[0280] 2. Terminal

[0281] Terminals are devices used by drivers, supervisors, and maintenance personnel, and include PCs, tablets, smartphones, etc.

[0282] The device connects to the server and can input data, display operational advice, report progress, and check notifications.

[0283] 3. Users (operators, supervisors, maintenance personnel)

[0284] Each user uses a terminal to perform operations according to his or her role.

[0285] The driver checks and executes operational tasks and reports on progress.

[0286] The supervisor inputs operational data and monitors operational progress.

[0287] The maintenance staff checks and updates the vehicle's maintenance information.

[0288] Explanation of program processing

[0289] 1. Data collection and input

[0290] The server collects driving and environmental data from the vehicle's sensors through a driving data collection module, including speed, location, and obstacle detection information.

[0291] The supervisor starts the application on the terminal, inputs the operation results and sends them to the server, along with daily performance information.

[0292] 2. Data analysis and operational advice generation

[0293] The server's data analysis module uses collected data to identify driving patterns and issues, using machine learning models such as TensorFlow.

[0294] It generates optimal driving advice, providing specific instructions such as "slow down when turning right at the next intersection."

[0295] 3. Generate operation schedule

[0296] The server's operation schedule generation module automatically generates operation schedules for each vehicle based on the analysis results.

[0297] The created schedule is structured so that it can be adjusted as needed and is sent to the driver's terminal.

[0298] 4. Operation progress monitoring and notification

[0299] The progress management system in the server receives the progress information sent from the driver and updates the database.

[0300] The server monitors the operation progress and generates and sends real-time reports to supervisors and maintainers.

[0301] The server's notification system sends important alerts and announcements to operators, supervisors, and maintenance personnel.

[0302] Specific use cases

[0303] For example, one day, a supervisor enters driving results from a terminal and sends the results to a server. The server stores the data received by the driving data collection module in a database, and the data analysis module generates driving advice based on that data. This advice is specific, such as "slow down when turning right at the next intersection." Next, the operation schedule generation module automatically creates an operation schedule based on this advice and sends it to the driver's terminal. The driver checks this schedule and carries out the operation tasks for the day. Once the task is completed, the progress information is entered into the terminal and sent to the server. The server updates this information in the progress management system and reports it to supervisors and maintenance personnel in real time. Important progress and alerts are sent to all relevant parties via the notification system, allowing them to take appropriate action.

[0304] Prompt Sentence Examples

[0305] Monitor the operation status of vehicle ID 'ABC123', generate advice and send necessary notifications to help it reach its destination safely and efficiently.

[0306] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0307] Step 1:

[0308] The server collects driving and environmental data obtained from the vehicle's sensors through the driving data collection module, including speed, position, obstacle detection information, etc. The input is real-time data from the vehicle's sensors, and the output is storing the collected data in a database.

[0309] Step 2:

[0310] The user, a supervisor, launches the application on the terminal, inputs the operation results, and sends them to the server. Daily performance information is also input. The input is the supervisor's operation results and performance data, and the output is the transmission and storage of all input data to the server.

[0311] Step 3:

[0312] The server's data analysis module identifies driving patterns and problems based on the collected data. This uses a generative AI model such as TensorFlow. The input is stored driving data and performance data, and the output is analysis results and driving advice.

[0313] Step 4:

[0314] The server's operation schedule generation module automatically generates operation schedules for each vehicle based on the analysis results. The inputs are the analysis results and driving advice, and the output is the generated operation schedule.

[0315] Step 5:

[0316] The server sends the generated operation schedule to the driver's terminal, and the driver confirms it and starts the actual operation task. The input is the generated operation schedule, and the output is the transmission and display of the schedule to the driver's terminal.

[0317] Step 6:

[0318] The driver, who is the user, performs the operation task and, after completing it, inputs progress information into the terminal and sends it to the server. The input is the progress information entered by the driver, and the output is transmission to the server and storage.

[0319] Step 7:

[0320] The progress management system on the server receives the progress information sent by the driver and updates the database. The input is the progress information, and the output is the update of the database.

[0321] Step 8:

[0322] The server monitors the operation progress and generates and sends real-time reports to supervisors and maintenance personnel based on the analysis results. The input is the updated progress data, and the output is the generated reports and notification messages.

[0323] Step 9:

[0324] The server's notification system sends important alerts and notifications to operators, supervisors, and maintenance personnel. The input is important information or alerts, and the output is the sending of notification messages.

[0325] 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.

[0326] This invention relates to a learning support system that monitors students' learning progress in real time and provides appropriate learning advice. Furthermore, by combining an emotion engine that recognizes the user's emotions, it is possible to provide flexible advice and adjust learning plans based on emotion information. Specific embodiments of this invention are described below.

[0327] System configuration

[0328] 1. Server

[0329] The server includes a data collection module, a data analysis module, a learning schedule generation module, a progress management system, a notification system, and an emotion engine.

[0330] The server centrally manages performance data, personality test results, daily facial expressions, condition information, emotional information, etc., and plays a central role in analyzing and notifying users.

[0331] 2. Terminal

[0332] Devices are devices used by students, teachers, and parents, and include computers, tablets, and smartphones.

[0333] The device connects to the server and allows users to enter data, view study advice, report progress, and check notifications.

[0334] 3. Users (students, teachers, parents)

[0335] Each user uses a terminal to perform operations according to his or her role.

[0336] Students review and complete their learning tasks and report their progress.

[0337] Teachers enter performance data and monitor learning progress.

[0338] Parents can monitor and support their students' learning progress and notifications.

[0339] Applying the Emotion Engine

[0340] Collecting and inputting emotional information

[0341] 1. User (Student)

[0342] As students study on their devices, facial and voice data is collected in real time through the device's camera and microphone.

[0343] 2. Terminal

[0344] The collected facial and voice data is sent to the emotion engine.

[0345] 3. Server

[0346] The emotion engine analyzes the received data and recognizes the student's emotions, for example, identifying emotional states such as happiness, stress, and anxiety.

[0347] Learning advice and schedule adjustment based on emotion information

[0348] 1. Server

[0349] The emotion information identified by the emotion engine is received by a data analysis module.

[0350] Based on emotional information, the system will provide learning advice and optimize the study schedule. For example, if a student is feeling stressed, the system will adjust the schedule to allow more time for relaxation.

[0351] 2. Server

[0352] The learning schedule generation module automatically generates a learning schedule based on the emotional information and transmits it to the terminal.

[0353] 3. Terminal

[0354] The new study schedule will be displayed on the student's device, and advice based on their emotional state will be provided.

[0355] Emotion-based notifications and alerts

[0356] 1. Server

[0357] If the emotion engine identifies certain negative emotions (e.g., high stress or anxiety), a notification system will send alerts to teachers and parents.

[0358] 2. Terminal

[0359] Alerts will appear on teachers' and parents' devices, informing them of the student's emotional state and recommended actions to take.

[0360] Specific use cases

[0361] For example, one day, a student enters their math test results into their device and sends them to the server. The server stores the received score data in a database using a data collection module, and a data analysis module generates study advice based on that data. The emotion engine then analyzes the student's facial and voice data while studying and identifies whether the student is feeling stressed. Based on this, the study schedule generation module automatically generates a new study schedule that includes time for relaxation and sends it to the student's device. Furthermore, if the emotion engine identifies high stress, the server's notification system sends an alert to teachers and parents and provides guidance on how to deal with the situation.

[0362] In this way, by combining the emotion engine, it is possible to flexibly adjust study advice and schedules according to the user's emotional state, maximizing the student's learning efficiency. It also enables a quick response to negative emotional states and helps reduce the student's psychological stress. This invention provides a learning support system that allows students, teachers, and parents to work together to comprehensively support the improvement of students' academic ability and psychological health.

[0363] The processing flow will be explained below.

[0364] Step 1:

[0365] The user (teacher) starts the application on the terminal and inputs test result data, including the grade data for each student, the number of correct answers, and comments.

[0366] Step 2:

[0367] The terminal receives the entered test results, converts the data into an appropriate format (e.g., JSON format), and sends it to the server.

[0368] Step 3:

[0369] The server's data collection module receives the test result data sent from the terminal, temporarily stores the received data in a cache, and then stores it in a database.

[0370] Step 4:

[0371] The server's data analysis module retrieves new test result data from the database, while also matching the student's previous performance data and personality assessment results.

[0372] Step 5:

[0373] The server's data analysis module uses AI models to analyze academic performance data and personality assessment results, identifying student weaknesses and learning patterns.

[0374] Step 6:

[0375] The server's data analysis module generates personalized study advice based on the analysis results, such as suggesting specific study content such as "review basic math problems for three hours a week."

[0376] Step 7:

[0377] As users (students) study on their devices, facial and voice data is collected in real time through the device's camera and microphone.

[0378] Step 8:

[0379] The terminal transmits the collected facial expression and voice data to the emotion engine.

[0380] Step 9:

[0381] The server's emotion engine analyzes the received data and recognizes the student's emotions, for example, identifying emotional states such as happiness, stress, and anxiety.

[0382] Step 10:

[0383] The emotion information identified by the emotion engine is received by a data analysis module of the server.

[0384] Step 11:

[0385] Based on the emotional information, the server's data analysis module will provide learning advice and optimize the learning schedule. For example, if a student is feeling stressed, the server will adjust the schedule to allow more time for relaxation.

[0386] Step 12:

[0387] The server's learning schedule generation module automatically generates a learning schedule based on the emotion information and sends it to the terminal.

[0388] Step 13:

[0389] The device will display the new study schedule on the student's device and provide advice based on their emotional state.

[0390] Step 14:

[0391] The user (student) checks the learning schedule on the terminal and performs the learning tasks according to the schedule. After completing the learning, the student enters the progress information on the terminal.

[0392] Step 15:

[0393] The terminal receives progress information entered by the student and sends it to the server.

[0394] Step 16:

[0395] The progress management system on the server receives the progress data sent from the devices and updates the database, monitors the updated progress, and adjusts the student's schedule as necessary.

[0396] Step 17:

[0397] If the emotion engine identifies certain negative emotions (e.g., high stress or anxiety), a notification system will send alerts to teachers and parents.

[0398] Step 18:

[0399] The server's progress management system automatically generates updated progress reports and sends them to teachers and parents in real time.

[0400] Step 19:

[0401] Alerts will be displayed on teachers' and parents' devices, informing them of the student's emotional state and recommended actions to take.

[0402] Step 20:

[0403] Users (teachers, parents) can view reports and notifications on their devices and take necessary actions, such as sending praise or planning additional learning support.

[0404] Through the above processing steps, a learning support system that combines an emotion engine can comprehensively support students' academic improvement and psychological health.

[0405] Example 2

[0406] 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."

[0407] While conventional learning support systems provide learning advice based on students' academic performance data and personality assessment results, they lack the ability to flexibly adjust learning schedules that take into account students' real-time emotional states, or the ability to quickly grasp and respond to students' psychological states. As a result, students may continue studying while feeling stressed, which can lead to reduced learning efficiency. Another issue is that it is difficult for teachers and parents to grasp students' emotional states, resulting in delayed provision of appropriate support.

[0408] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means including an emotion engine that receives and analyzes facial expression and voice data collected from the student's device, a means for adjusting study advice and study schedules based on the analyzed emotion information, and a means for identifying specific emotional states such as severe stress and sending alerts to teachers and parents. This makes it possible to optimize study schedules taking into account the student's real-time emotional state and set aside time for stress reduction. It also enables teachers and parents to quickly grasp the student's emotional state and take appropriate measures.

[0409] "Performance Data" means data including the scores and grades a student receives on exams and tests.

[0410] "Personality assessment results" are data including results obtained from a diagnostic tool for assessing a student's personality and behavioral characteristics.

[0411] "Study advice" is a specific suggestion on study methods and areas for improvement that is generated by analyzing students' academic performance data and personality assessment results.

[0412] A "study schedule" is a plan of a student's learning activities organized along a timeline, including study time and break time.

[0413] "Progress" is data that indicates a student's progress toward a learning goal, including the status of completed and incomplete tasks.

[0414] "Device" refers to a device used by a student, teacher, or parent, including a PC, tablet, or smartphone.

[0415] "Facial expression data" is digital data about students' facial expressions collected through the device's camera.

[0416] "Audio data" refers to digital data relating to students' voices and speech collected through the device's microphone.

[0417] The "emotion engine" is software that analyzes collected facial and voice data to identify a student's emotional state.

[0418] The "data analysis module" is software that analyzes collected data and generates study advice and schedules based on the results.

[0419] The "notification system" is a system for sending notifications about students' emotional state and learning progress to teachers' and parents' devices.

[0420] An "alert" is a warning message sent when the emotion engine identifies a specific negative emotion, intended to alert teachers and parents.

[0421] This invention is a learning support system that monitors students' learning progress in real time and provides appropriate learning advice. By combining it with an emotion engine that recognizes emotions, it realizes flexible advice and adjustment of learning plans based on emotional information.

[0422] Hardware / Software used

[0423] 1. Server

[0424] Data collection module, data analysis module, learning schedule generation module, progress management system, notification system, emotion engine

[0425] 2. Terminal

[0426] Devices including computers, tablets, and smartphones

[0427] 3. Emotion Engine

[0428] Software for analyzing student emotions

[0429] Data processing / data calculation details

[0430] 1. Data Collection

[0431] Students use a device to study. The device is equipped with a camera and microphone, which collects facial expression and voice data in real time. The collected data is sent to a server.

[0432] 2. Emotion analysis

[0433] The server's emotion engine analyzes the received facial and voice data to identify the student's emotional state. Using machine learning models, emotions such as happiness, stress, and anxiety can be identified. The analysis results are stored in a database.

[0434] 3. Study advice and schedule adjustment

[0435] The server's data analysis module analyzes the collected academic performance data and personality assessment results, as well as the emotional data identified by the emotion engine. Based on the analysis results, it generates optimal study advice for students. Furthermore, the study schedule generation module automatically generates a study schedule that reflects the emotional information and sends it to the student's device.

[0436] 4. Notification System

[0437] The server's notification system sends alerts to teachers and parents when the emotion engine identifies specific negative emotions (e.g., high stress or anxiety). These notifications are displayed on the device, providing information about the student's emotional state and recommended actions.

[0438] Specific examples

[0439] For example, a student enters the results of a math test on their device and sends them to the server. At this time, the device's camera captures their facial expressions and the microphone records their voice. The server analyzes this data using an emotion engine to identify whether the student is feeling stressed. Based on the low test score and the emotional data of stress, the data analysis module generates study advice such as "Review the basics for the next test." The study schedule generation module creates a new study schedule that includes breaks and sends it to the student's device. Furthermore, since high stress has been detected, the server sends an alert to the teacher and parents to urge them to take action.

[0440] Example prompts for generative AI models

[0441] "Please tell me how to generate optimal study advice and schedules based on student performance data and emotional information."

[0442] "Give an example of an algorithm that analyzes a student's facial and voice data to recognize stress and adjust their learning plan."

[0443] This will enable the realization of a system that provides learning advice and schedules that correspond to the user's emotional state, maximizing students' learning efficiency and reducing psychological stress.

[0444] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0445] Step 1:

[0446] Data collection

[0447] Users (students) use devices to study. As input, they enter their operation data during study, test results, and study time into the device. The device's camera captures the student's facial expression, and the microphone collects audio. As output, this data is sent to the server. Specifically, it includes real-time facial expression images and audio clips captured by the device, as well as test scores and study logs entered by the student.

[0448] Step 2:

[0449] Emotional Data Analysis

[0450] The server inputs the facial and voice data received from the device into the emotion engine, which analyzes this data and identifies the student's emotional state. Specifically, the machine learning model classifies emotions such as happiness, stress, and anxiety from facial expressions and identifies similar emotional states from voice tone and speed. The output is stored in the server's database as a dataset of emotion identification results.

[0451] Step 3:

[0452] Study advice generation

[0453] The server's data analysis module receives collected grade data, personality assessment results, and emotion data identified by the emotion engine as input. Based on the input data, it analyzes the student's learning tendencies and psychological state and generates personalized study advice. Specifically, it combines low test scores with stress detection to generate advice such as "Review the basics for the next test." The output is the generated study advice.

[0454] Step 4:

[0455] Schedule adjustment

[0456] The server's learning schedule generation module receives the generated learning advice and emotion data as input and adjusts the student's learning schedule. Specifically, if stress levels are high, it creates a schedule that includes more breaks and sets short-term goals to encourage successful experiences. The output is sent to the student's device as a new learning schedule.

[0457] Step 5:

[0458] Notifications and Alerts

[0459] The server's notification system sends alerts to teachers and parents when the emotion engine identifies specific negative emotions (e.g., strong stress or anxiety). The input is the emotion engine's identification result data. Specific operations include generating an alert message and sending a notification directly to the teacher's or parent's device. The output is an alert message that is displayed on the teacher's or parent's device.

[0460] This will allow for optimizing learning schedules based on students' real-time emotional state, allocating time to reduce stress, and enabling teachers and parents to quickly grasp students' emotional state and take appropriate measures.

[0461] (Application example 2)

[0462] 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."

[0463] In production sites, there is a need for a comprehensive support system that can maximize the work efficiency of factory workers while reducing their psychological stress. In particular, there is a lack of a system that can grasp the real-time emotional state of workers and respond immediately based on that, making it difficult to balance productivity and worker safety.

[0464] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting and storing student grade data and personality assessment results, means for analyzing student grade data and personality assessment results and generating study advice, means for automatically generating and displaying student study schedules, means for monitoring user progress and reporting in real time, means for collecting user facial expression and voice data and analyzing the user's emotional state, means for adjusting and displaying a work schedule based on the emotional information, and means for sending notifications to users, managers, and supervisors. This makes it possible to grasp the worker's progress and emotional state in real time and provide appropriate work advice, suggest breaks, or promptly notify the manager.

[0465] "Performance data" is information that records and quantifies the work results and performance of students or factory workers.

[0466] "Personality test results" are the results of a test to evaluate the personality traits and behavioral patterns of students or factory workers.

[0467] "Input and storage means" refers to the set of functions and techniques used to get data into the system and store it in an appropriate database.

[0468] "Means for analyzing and generating learning advice" refers to the functionality for analyzing collected data and providing specific advice based on that data.

[0469] "Means for automatically generating and displaying learning schedules" refers to a function for automatically generating optimal learning or work schedules based on the progress and status of students or factory workers, and displaying them on a terminal.

[0470] "Means for monitoring progress and reporting in real time" refers to the ability to constantly monitor the progress of students and factory workers and report that information to the system in real time.

[0471] "Means for collecting facial and voice data and analyzing emotional states" refers to a function for collecting facial and voice data using a camera or microphone and analyzing it to identify emotional states.

[0472] "Means for adjusting and displaying a work schedule based on emotional information" refers to a function for optimizing a work schedule based on collected emotional information and displaying it on a terminal.

[0473] "Means for sending notifications" refers to the function for sending alerts and notifications to relevant parties (managers and supervisors) based on the status of students and factory workers.

[0474] This invention is a comprehensive system for monitoring the work efficiency and psychological stress of factory workers in real time and providing appropriate work advice and schedule adjustments. The system consists of a server containing many functional modules and devices used by workers (smart glasses, robots, etc.).

[0475] Server Configuration

[0476] The server has the following functional modules:

[0477] 1. Data collection module: Collects worker performance data, personality test results, daily facial expressions, and condition information in real time.

[0478] 2. Data analysis module: Generates work advice based on collected data. In particular, it analyzes emotional information using a generative AI model.

[0479] 3. Work schedule generation module: Automatically generates an optimal work schedule based on the analysis results.

[0480] 4. Progress management system: Monitors the work progress of workers and reports it in real time.

[0481] 5. Notification System: Sends necessary notifications to users, managers and supervisors.

[0482] 6. Emotion engine: Analyzes facial and voice data to identify emotional information.

[0483] Device configuration

[0484] Terminals are devices used by workers, managers and supervisors, and include smart glasses and robots.

[0485] 1. Camera and microphone: Collect facial and voice data of the worker.

[0486] 2. Display: Displays automatically generated work schedules and real-time advice.

[0487] 3. Communication module: Functions for exchanging data with the server.

[0488] System Operation

[0489] The server analyzes facial and voice data collected from the device in real time to identify the worker's emotional state. For example, if stress or fatigue is detected, the work schedule generation module automatically generates a new schedule that includes relaxation time.

[0490] Automatically generated schedules and advice are displayed on the terminal screen, while a notification system alerts managers and supervisors, informing them of the worker's status and recommended actions.

[0491] Specific examples

[0492] For example, consider a situation where a factory worker is working consecutive night shifts. Smart glasses scan the worker's face and detect their fatigue level via a camera and microphone. The system uses a generative AI model to identify high stress levels and automatically insert breaks in the work schedule generation module. A notification is also sent to the manager, stating, "The worker is showing signs of fatigue and should be given a break."

[0493] Example prompt sentence:

[0494] "Monitor the psychological state of factory workers in real time. For example, stress, impatience, etc. If stress levels are high, automatically suggest a break and notify the supervisor."

[0495] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0496] Step 1:

[0497] The terminal uses a camera and microphone to collect facial and voice data of the worker. The collected data is sent to the server in real time. This input data consists of the worker's facial recognition image and voice signal.

[0498] Step 2:

[0499] The server passes the received facial and voice data to the emotion engine, which analyzes this data using a generative AI model to identify the worker's emotional state (e.g., stress, excitement, fatigue, etc.). The analysis results are output.

[0500] Step 3:

[0501] The emotion information output by the emotion engine is received and the server's data analysis module analyzes the worker's situation. This module determines the need for appropriate work advice or schedule adjustments based on the worker's emotional state.

[0502] Step 4:

[0503] The server's work schedule generation module automatically generates an optimal work schedule based on the analysis results. Specifically, if a worker is feeling stressed, a new schedule is created that includes relaxation time and breaks. This new schedule is then output.

[0504] Step 5:

[0505] The generated work schedule and advice are sent to the terminal via the communication module, and the terminal displays the received information on a screen so that the worker can check it.

[0506] Step 6:

[0507] To monitor the worker's progress, data is again collected using cameras and microphones. This data is sent to a server where it is analyzed by the progress management system, and progress is reported.

[0508] Step 7:

[0509] The server's notification system sends alerts to managers and supervisors based on negative emotional states (e.g., high stress or fatigue) identified by the emotion engine. The alerts include the worker's status and recommended actions. A notification is output.

[0510] 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.

[0511] 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.

[0512] 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.

[0513] [Second embodiment]

[0514] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0515] 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.

[0516] 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).

[0517] 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.

[0518] 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.

[0519] 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).

[0520] 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.

[0521] 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.

[0522] 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.

[0523] 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.

[0524] 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.

[0525] 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."

[0526] This invention relates to a learning support system that uses AI to help students improve their grades. The system is used by three parties: a server, a terminal, and users (students, teachers, and parents). Specific embodiments for implementing this invention are described below.

[0527] System configuration

[0528] 1. Server

[0529] The server includes a data collection module, a data analysis module, a study schedule generation module, a progress management system, and a notification system.

[0530] The server centrally manages all performance data, personality test results, daily facial expressions and condition information, and plays a central role in analyzing and notifying users.

[0531] 2. Terminal

[0532] Devices are devices used by students, teachers, and parents, and include computers, tablets, and smartphones.

[0533] The device connects to the server and allows users to enter data, view study advice, report progress, and check notifications.

[0534] 3. Users (students, teachers, parents)

[0535] Each user uses a terminal to perform operations according to his or her role.

[0536] Students review and complete their learning tasks and report their progress.

[0537] Teachers enter performance data and monitor learning progress.

[0538] Parents can monitor and support their students' learning progress and notifications.

[0539] Explanation of program processing

[0540] Data collection and input

[0541] 1. User (Teacher)

[0542] The teacher launches the application on the terminal, enters performance data such as test results, and sends it to the server.

[0543] Personality test results and condition information are updated regularly.

[0544] 2. Terminal

[0545] The entered performance data is received, converted into an appropriate format, and sent to the server.

[0546] To provide a UI that allows users to input information intuitively.

[0547] 3. Server

[0548] The data collection module stores the received performance data and personality test results in a database.

[0549] Accumulates condition information and daily facial expression data.

[0550] Data analysis and generation of learning advice

[0551] 1. Server

[0552] The data analysis module analyzes the information in the database to identify students' learning patterns and problems.

[0553] Based on the results of personality assessment and condition information, optimal learning advice is generated, taking into consideration ways to improve learning performance.

[0554] Generate a study schedule

[0555] 1. Server

[0556] The learning schedule generation module automatically generates learning schedules for each student based on the analysis results.

[0557] The schedule created should have a structure that allows it to be adjusted as needed.

[0558] 2. Terminal

[0559] The learning schedule is sent to the student's device and a list of learning tasks is displayed.

[0560] You can visually check today's tasks, weekly schedule, etc.

[0561] Progress monitoring and notifications

[0562] 1. User (Student)

[0563] Students carry out learning tasks and enter progress information into the device after completion.

[0564] 2. Terminal

[0565] Receives progress information and sends it to the server.

[0566] 3. Server

[0567] The progress information received by the progress management system is updated to the database.

[0568] Monitor learning progress and generate and send real-time reports to teachers and parents.

[0569] 4. Server

[0570] A notification system sends important alerts and announcements to students, teachers and parents.

[0571] Specific use cases

[0572] For example, one day, a teacher enters the results of a mathematics test on a device and sends them to the server. The server stores the received score data in a database using a data collection module, and the data analysis module generates study advice based on that data. This advice is specific, such as "It's a good idea to review basic mathematics problems for three hours a week." Next, the study schedule generation module automatically creates a study schedule based on this advice and sends it to the student's device. The student checks this schedule and performs the tasks for the day. Once the tasks are completed, the student enters progress information into the device and sends it to the server. The server updates this information in a progress management system and reports it to teachers and parents in real time. Important progress and alerts are sent to all relevant parties via a notification system, allowing them to take appropriate action.

[0573] The system described above allows students, teachers, and parents to work together to effectively support students' academic improvement. In this way, the present invention realizes a useful and continuous learning support environment.

[0574] The processing flow will be explained below.

[0575] Step 1:

[0576] The user (teacher) starts the application on the terminal and inputs test result data, including the grade data for each student, the number of correct answers, and comments.

[0577] Step 2:

[0578] The terminal receives the entered test results, converts the data into an appropriate format (e.g., JSON format), and sends it to the server.

[0579] Step 3:

[0580] The server's data collection module receives the test result data sent from the terminal, temporarily stores the received data in a cache, and then stores it in a database.

[0581] Step 4:

[0582] The server's data analysis module retrieves new test result data from the database, while also matching the student's previous performance data and personality assessment results.

[0583] Step 5:

[0584] The server's data analysis module uses AI models to analyze academic performance data and personality assessment results, identifying students' weak points in certain subjects and topics, as well as their learning patterns.

[0585] Step 6:

[0586] The server's data analysis module generates personalized study advice based on the analysis results, such as suggesting specific study content such as "review basic math problems for three hours a week."

[0587] Step 7:

[0588] The server's learning schedule generation module receives the generated learning advice and automatically generates a learning schedule for the student based on it, which includes dates, time slots, subjects, specific learning tasks, etc.

[0589] Step 8:

[0590] The server sends the generated learning schedule to the student's terminal, which receives the schedule and displays it on the user interface.

[0591] Step 9:

[0592] The user (student) checks the learning schedule on the terminal and performs the learning tasks according to the schedule. After completing the learning, the student enters the progress information on the terminal.

[0593] Step 10:

[0594] The terminal receives progress information entered by the student and sends it to the server.

[0595] Step 11:

[0596] The progress management system on the server receives the progress data sent from the devices and updates the database, monitors the updated progress, and adjusts the student's schedule as necessary.

[0597] Step 12:

[0598] The server-based progress management system automatically generates and sends updated progress reports to teachers and parents in real time, including progress levels, remaining tasks, and recommended actions.

[0599] Step 13:

[0600] The server's notification system sends important alerts and announcements to students, teachers, and parents, including conference reminders and changes to learning schedules.

[0601] Step 14:

[0602] Users (teachers, parents) can view reports and notifications on their devices and take necessary actions, such as sending praise or planning additional learning support.

[0603] Through the above processing steps, the AI-based learning support system comprehensively supports students in improving their academic ability.

[0604] Example 1

[0605] 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."

[0606] In today's educational environment, there is a lack of systems that effectively support the learning progress of individual students. In particular, there is a need for systems that can comprehensively handle information on students' grades, personalities, and daily conditions, provide appropriate learning advice, and monitor and report individual learning progress in real time. There is also a need for systems that allow teachers and parents to easily check students' progress. To solve these issues, there is a need for the development of an integrated learning support system that allows students, teachers, and parents to work together effectively.

[0607] 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.

[0608] In this invention, the server includes a means for inputting and storing a student's academic performance data and personality assessment results, a means for analyzing the student's academic performance data and personality assessment results and generating study advice, and a means for automatically generating and displaying the student's study schedule. This makes it possible to identify a student's study patterns and problems and provide optimal study advice to each individual. The server also includes a means for monitoring a student's study progress and reporting it in real time, a means for sending notifications to students, teachers, and parents, a terminal means for converting input data into an appropriate format and sending it to the server, an analysis means for identifying study patterns and problems and generating optimal study advice, and a notification means for appropriately sending important alerts and notifications. This enables students, teachers, and parents to work together to effectively support their study progress.

[0609] "Academic performance data" refers to information including the scores and evaluation results obtained by a student on tests and assessments.

[0610] "Personality assessment results" are diagnostic results that are used to evaluate students' personalities and optimize learning methods and approaches.

[0611] "Condition information" includes information about a student's daily health, mood, fatigue level, etc.

[0612] "Study advice" is advice that shows optimal study methods and areas for improvement based on students' academic performance data and personality assessment results.

[0613] A "study schedule" is a schedule that systematically arranges the learning tasks and activities that students should complete within a certain period of time.

[0614] "Learning progress" is information that indicates the completion status and progress of the learning tasks performed by the student.

[0615] "Monitoring" is the act of regularly observing students' learning progress and recording it as data.

[0616] "Notifications" are messages intended to communicate important information or alerts to students, teachers, and parents.

[0617] A "terminal" is a device used by students, teachers, and parents that works in conjunction with a server to input and display data.

[0618] The "data collection module" is a program that has the function of receiving performance data, condition information, etc. and storing it in a database.

[0619] The "data analysis module" is a program that has the function of analyzing received data and identifying students' learning patterns and problems.

[0620] The "study schedule generation module" is a program with the function of automatically generating individual student study schedules.

[0621] A "progress management system" is a system for updating student progress information in a database and monitoring learning progress.

[0622] A "notification system" is a system for sending important information and alerts to students, teachers, and parents.

[0623] This invention relates to a learning support system that uses AI to help students improve their grades. This system is used by three parties: a server, a terminal, and users (students, teachers, and parents). Specific modes for carrying out the invention are described below.

[0624] System configuration

[0625] 1. Server

[0626] The server includes a data collection module, a data analysis module, a study schedule generation module, a progress management system, and a notification system.

[0627] The server centrally manages performance data, personality test results, daily facial expressions and condition information, and plays a central role in analyzing and notifying users.

[0628] 2. Terminal

[0629] Devices are devices used by students, teachers, and parents, and include computers, tablets, and smartphones.

[0630] The device connects to the server and allows users to enter data, view study advice, report progress, and check notifications.

[0631] 3. Users (students, teachers, parents)

[0632] Each user uses a terminal to perform operations according to his or her role.

[0633] Students review and complete their learning tasks and report their progress.

[0634] Teachers enter performance data and monitor learning progress.

[0635] Parents can monitor and support their students' learning progress and notifications.

[0636] Explanation of program processing

[0637] Data collection and input

[0638] User (Teacher)

[0639] The teacher starts a dedicated application on the device and inputs test results, evaluations, personality test results, and condition information. The device receives this data, converts it into an appropriate format, and sends it to the server.

[0640] For example, a teacher enters math test results into an application and clicks a "submit" button, sending the data to a server.

[0641] Receiving and storing data

[0642] server

[0643] The server receives the data sent from the device using a data collection module and stores it in a database, allowing for centralized management of performance data, personality assessment results, condition information, and other data.

[0644] For example, the server may store the received math test results in a database for further analysis.

[0645] Data analysis and generation of learning advice

[0646] server

[0647] The data analysis module analyzes academic performance data and personality assessment results to identify students' learning patterns and problems. Based on the analysis results, a generative AI model is used to generate optimal learning advice.

[0648] For example, the generated advice could be specific, such as "It would be good to review basic math problems for three hours a week."

[0649] Generate and submit a study schedule

[0650] server

[0651] The learning schedule generation module automatically generates individual learning schedules for each student based on the analysis results. The generated schedules are sent to the student's device, where they can review them and proceed with their studies.

[0652] For example, the generated schedule may include a specific task list such as "Monday: 2 hours of math, Tuesday: 1 hour of English."

[0653] Complete learning tasks and submit progress information

[0654] User (student)

[0655] Students check their study schedule on their devices and proceed with their studies according to the plan. When they complete a task, they enter their progress information into the device and send it to the server.

[0656] For example, on the "Today's Tasks" screen, check the completed tasks and click the "Submit" button.

[0657] Progress updates and reporting

[0658] server

[0659] The progress management system updates the received progress information into a database, monitors learning progress, and creates and sends real-time reports to teachers and parents.

[0660] For example, student progress updates are sent to teachers and parents via email and push notifications.

[0661] Sending notifications

[0662] server

[0663] The notification system sends important alerts and announcements to students, teachers and parents, allowing them to take timely action.

[0664] For example, notifications are sent when there is significant improvement in performance or when a particular learning task is not completed.

[0665] Specific use cases

[0666] For example, one day, a teacher enters the results of a math test into a device and sends them to the server. The server's data collection module stores the received grade data in a database, and the data analysis module analyzes the data and generates study advice. This advice is specific, such as "It's a good idea to review basic math problems for three hours a week." The study schedule generation module then automatically creates a study schedule based on this advice and sends it to the student's device. The student checks this schedule and performs the tasks for the day. Once the tasks are completed, the student enters progress information into the device and sends it to the server. The server updates this information in the progress management system and reports it to teachers and parents in real time. Important progress and alerts are sent to all relevant parties via the notification system, allowing them to take appropriate action.

[0667] Example prompts for generative AI models

[0668] Q: What is the specific process from when a teacher enters test results to when study advice is generated?

[0669] Answer: The teacher launches the application on the device, enters the test results, and sends them to the server. The device receives the performance data, converts it into the appropriate format, and sends it to the server. The server's data collection module receives the data and stores it in a database. The data analysis module then analyzes the performance data and personality assessment results and generates study advice using a generative AI model. This advice is used to create an individualized study schedule.

[0670] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0671] Step 1: Enter and submit grade data

[0672] User (Teacher)

[0673] The teacher launches a dedicated application on the device.

[0674] Enter test results, evaluations, personality test results, and condition information.

[0675] This performs the operation to send the data entered by the teacher to the server.

[0676] Input: Test results, evaluation, personality test results, condition information

[0677] Output: The formatted data sent to the server

[0678] Specific behavior:

[0679] Launch the application on your device.

[0680] Enter the score in the Test Result field.

[0681] Enter your personality test results and condition information in the corresponding fields.

[0682] Click the "Send" button.

[0683] Step 2: Receiving and storing data

[0684] Terminal

[0685] The terminal receives the input data and converts it into the appropriate format.

[0686] The converted data is sent to the server.

[0687] server

[0688] The server stores the data received by the data collection module in a database.

[0689] Input: Formatted data (grade data, personality test results, condition information)

[0690] Output: Data stored in the database

[0691] Specific behavior:

[0692] The terminal converts the data into JSON format.

[0693] Sends data to the server via an HTTP POST request.

[0694] On the server side, the data collection module receives the request and stores it in a database.

[0695] Step 3: Analyze the data and generate learning advice

[0696] server

[0697] The data analysis module analyzes the academic performance data and personality test results in the database.

[0698] Identify learning patterns and identify pain points.

[0699] Generative AI models are used to generate optimal learning advice.

[0700] Input: Academic data in the database, personality test results

[0701] Output: The generated study advice

[0702] Specific behavior:

[0703] The data analysis module extracts the required data from the database using SQL queries.

[0704] Use generative AI models (e.g., GPT-3) to generate personalized advice from analyzed data.

[0705] Step 4: Generate and submit your study schedule

[0706] server

[0707] The learning schedule generation module automatically generates individual learning schedules for each student based on the analysis results.

[0708] The learning schedule is sent to the device, and a visual learning task list is displayed on the student's device.

[0709] Input: Analysis results, generated learning advice

[0710] Output: Study schedule sent to student devices

[0711] Specific behavior:

[0712] The learning schedule generation module creates the schedule in JSON format.

[0713] Send the schedule to the device using an HTTP POST request.

[0714] The schedule is displayed on the device in calendar or list format.

[0715] Step 5: Complete the learning task and submit progress information

[0716] User (student)

[0717] Students check their study schedule on their devices and proceed with their studies according to the plan.

[0718] Once the learning task is completed, progress information is entered into the terminal and sent to the server.

[0719] Input: Progress information for completed learning tasks

[0720] Output: Progress information sent to the server

[0721] Specific behavior:

[0722] Open the "Today's Tasks" screen on your device.

[0723] Complete each task and click the "Done" button when complete.

[0724] After entering the progress information, click the "Submit" button.

[0725] Step 6: Update and report progress

[0726] Terminal

[0727] The device receives the progress information entered by the student and sends it to the server.

[0728] server

[0729] The progress management system updates the received progress information in the database.

[0730] The system monitors progress and generates and sends real-time reports to teachers and parents.

[0731] Input: Progress information sent from the device to the server

[0732] Output: Progress information updated in database, reports to teachers and parents

[0733] Specific behavior:

[0734] The device converts the progress data into JSON format.

[0735] Sends progress data to the server via HTTP POST request.

[0736] The progress management system updates the database on the server side and sends notification emails to teachers and parents.

[0737] Step 7: Sending notifications

[0738] server

[0739] A notification system sends important alerts and announcements to students, teachers and parents.

[0740] Input: Critical alerts and notifications

[0741] Output: Message to notify all involved parties

[0742] Specific behavior:

[0743] The notification system periodically checks for alert conditions.

[0744] When an alert occurs, push notifications and emails are sent to students, teachers, and parents.

[0745] (Application example 1)

[0746] 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."

[0747] To improve the safety and efficiency of autonomous vehicle operations, a system is needed that collects and analyzes vehicle driving data and environmental data in real time and provides optimal driving advice and schedules. However, current systems do not adequately collect and analyze driving data, and there is insufficient cooperation between drivers, supervisors, and maintenance personnel, making it difficult to properly manage operations and respond quickly.

[0748] 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.

[0749] In this invention, the server includes means for inputting and storing vehicle driving data and environmental data, means for analyzing the vehicle driving data and environmental data and generating driving advice, and means for automatically generating and displaying an optimal driving schedule. This enables real-time monitoring of the operation of autonomous vehicles, simultaneously improving safety and efficiency, and enabling quick responses between drivers, supervisors, and maintenance personnel.

[0750] "Vehicle" means a means of movement that is mechanically powered and is used to transport people or goods.

[0751] "Driving data" is a general term for information acquired during driving, such as the vehicle's speed, position, direction, and acceleration.

[0752] "Environmental data" refers to data such as meteorological information, road conditions, and traffic information that is collected to understand the situation around the vehicle.

[0753] "Server" means a central computer system for collecting, analyzing, and storing data and sending necessary notifications and instructions.

[0754] "Driving advice" is specific instructions or suggestions provided to improve vehicle safety or efficiency.

[0755] A "service schedule" is a plan for a vehicle to complete a specific route or task within a specific time frame.

[0756] "Monitoring" is the act of monitoring the current status and progress of a vehicle in real time.

[0757] "Notifications" are messages or alerts that convey important information or warnings to operators, supervisors, and maintenance personnel.

[0758] A "supervisor" is a person whose role is to manage the vehicle's operation status and ensure that it is being driven appropriately.

[0759] A "maintenance technician" is a person who performs maintenance and repairs on vehicles and supports their safe and normal operation.

[0760] "Progress" is a status indicating the degree of accomplishment of the task currently being performed by the vehicle.

[0761] This invention relates to a system that utilizes AI to effectively support the operation of autonomous vehicles. The system is used by three parties: a server, a terminal, and users (drivers, supervisors, and maintenance personnel). Specific embodiments for implementing this invention are described below.

[0762] System configuration

[0763] 1. Server

[0764] The server includes a driving data collection module, a data analysis module, a driving schedule generation module, a progress management system, and a notification system.

[0765] The server plays a central role in centrally managing all operational data, environmental data, daily performance information, etc., and analyzing and reporting them.

[0766] 2. Terminal

[0767] Terminals are devices used by drivers, supervisors, and maintenance personnel, and include PCs, tablets, smartphones, etc.

[0768] The device connects to the server and can input data, display operational advice, report progress, and check notifications.

[0769] 3. Users (operators, supervisors, maintenance personnel)

[0770] Each user uses a terminal to perform operations according to his or her role.

[0771] The driver checks and executes operational tasks and reports on progress.

[0772] The supervisor inputs operational data and monitors operational progress.

[0773] The maintenance staff checks and updates the vehicle's maintenance information.

[0774] Explanation of program processing

[0775] 1. Data collection and input

[0776] The server collects driving and environmental data from the vehicle's sensors through a driving data collection module, including speed, location, and obstacle detection information.

[0777] The supervisor starts the application on the terminal, inputs the operation results and sends them to the server, along with daily performance information.

[0778] 2. Data analysis and operational advice generation

[0779] The server's data analysis module uses collected data to identify driving patterns and issues, using machine learning models such as TensorFlow.

[0780] It generates optimal driving advice, providing specific instructions such as "slow down when turning right at the next intersection."

[0781] 3. Generate operation schedule

[0782] The server's operation schedule generation module automatically generates operation schedules for each vehicle based on the analysis results.

[0783] The created schedule is structured so that it can be adjusted as needed and is sent to the driver's terminal.

[0784] 4. Operation progress monitoring and notification

[0785] The progress management system in the server receives the progress information sent from the driver and updates the database.

[0786] The server monitors the operation progress and generates and sends real-time reports to supervisors and maintainers.

[0787] The server's notification system sends important alerts and announcements to operators, supervisors, and maintenance personnel.

[0788] Specific use cases

[0789] For example, one day, a supervisor enters driving results from a terminal and sends the results to a server. The server stores the data received by the driving data collection module in a database, and the data analysis module generates driving advice based on that data. This advice is specific, such as "slow down when turning right at the next intersection." Next, the operation schedule generation module automatically creates an operation schedule based on this advice and sends it to the driver's terminal. The driver checks this schedule and carries out the operation tasks for the day. Once the task is completed, the progress information is entered into the terminal and sent to the server. The server updates this information in the progress management system and reports it to supervisors and maintenance personnel in real time. Important progress and alerts are sent to all relevant parties via the notification system, allowing them to take appropriate action.

[0790] Prompt Sentence Examples

[0791] Monitor the operation status of vehicle ID 'ABC123', generate advice and send necessary notifications to help it reach its destination safely and efficiently.

[0792] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0793] Step 1:

[0794] The server collects driving and environmental data obtained from the vehicle's sensors through the driving data collection module, including speed, position, obstacle detection information, etc. The input is real-time data from the vehicle's sensors, and the output is storing the collected data in a database.

[0795] Step 2:

[0796] The user, a supervisor, launches the application on the terminal, inputs the operation results, and sends them to the server. Daily performance information is also input. The input is the supervisor's operation results and performance data, and the output is the transmission and storage of all input data to the server.

[0797] Step 3:

[0798] The server's data analysis module identifies driving patterns and problems based on the collected data. This uses a generative AI model such as TensorFlow. The input is stored driving data and performance data, and the output is analysis results and driving advice.

[0799] Step 4:

[0800] The server's operation schedule generation module automatically generates operation schedules for each vehicle based on the analysis results. The inputs are the analysis results and driving advice, and the output is the generated operation schedule.

[0801] Step 5:

[0802] The server sends the generated operation schedule to the driver's terminal, and the driver confirms it and starts the actual operation task. The input is the generated operation schedule, and the output is the transmission and display of the schedule to the driver's terminal.

[0803] Step 6:

[0804] The driver, who is the user, performs the operation task and, after completing it, inputs progress information into the terminal and sends it to the server. The input is the progress information entered by the driver, and the output is transmission to the server and storage.

[0805] Step 7:

[0806] The progress management system on the server receives the progress information sent by the driver and updates the database. The input is the progress information, and the output is the update of the database.

[0807] Step 8:

[0808] The server monitors the operation progress and generates and sends real-time reports to supervisors and maintenance personnel based on the analysis results. The input is the updated progress data, and the output is the generated reports and notification messages.

[0809] Step 9:

[0810] The server's notification system sends important alerts and notifications to operators, supervisors, and maintenance personnel. The input is important information or alerts, and the output is the sending of notification messages.

[0811] 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.

[0812] This invention relates to a learning support system that monitors students' learning progress in real time and provides appropriate learning advice. Furthermore, by combining an emotion engine that recognizes the user's emotions, it is possible to provide flexible advice and adjust learning plans based on emotion information. Specific embodiments of this invention are described below.

[0813] System configuration

[0814] 1. Server

[0815] The server includes a data collection module, a data analysis module, a learning schedule generation module, a progress management system, a notification system, and an emotion engine.

[0816] The server centrally manages performance data, personality test results, daily facial expressions, condition information, emotional information, etc., and plays a central role in analyzing and notifying users.

[0817] 2. Terminal

[0818] Devices are devices used by students, teachers, and parents, and include computers, tablets, and smartphones.

[0819] The device connects to the server and allows users to enter data, view study advice, report progress, and check notifications.

[0820] 3. Users (students, teachers, parents)

[0821] Each user uses a terminal to perform operations according to his or her role.

[0822] Students review and complete their learning tasks and report their progress.

[0823] Teachers enter performance data and monitor learning progress.

[0824] Parents can monitor and support their students' learning progress and notifications.

[0825] Applying the Emotion Engine

[0826] Collecting and inputting emotional information

[0827] 1. User (Student)

[0828] As students study on their devices, facial and voice data is collected in real time through the device's camera and microphone.

[0829] 2. Terminal

[0830] The collected facial and voice data is sent to the emotion engine.

[0831] 3. Server

[0832] The emotion engine analyzes the received data and recognizes the student's emotions, for example, identifying emotional states such as happiness, stress, and anxiety.

[0833] Learning advice and schedule adjustment based on emotion information

[0834] 1. Server

[0835] The emotion information identified by the emotion engine is received by a data analysis module.

[0836] Based on emotional information, the system will provide learning advice and optimize the study schedule. For example, if a student is feeling stressed, the system will adjust the schedule to allow more time for relaxation.

[0837] 2. Server

[0838] The learning schedule generation module automatically generates a learning schedule based on the emotional information and transmits it to the terminal.

[0839] 3. Terminal

[0840] The new study schedule will be displayed on the student's device, and advice based on their emotional state will be provided.

[0841] Emotion-based notifications and alerts

[0842] 1. Server

[0843] If the emotion engine identifies certain negative emotions (e.g., high stress or anxiety), a notification system will send alerts to teachers and parents.

[0844] 2. Terminal

[0845] Alerts will appear on teachers' and parents' devices, informing them of the student's emotional state and recommended actions to take.

[0846] Specific use cases

[0847] For example, one day, a student enters their math test results into their device and sends them to the server. The server stores the received score data in a database using a data collection module, and a data analysis module generates study advice based on that data. The emotion engine then analyzes the student's facial and voice data while studying and identifies whether the student is feeling stressed. Based on this, the study schedule generation module automatically generates a new study schedule that includes time for relaxation and sends it to the student's device. Furthermore, if the emotion engine identifies high stress, the server's notification system sends an alert to teachers and parents and provides guidance on how to deal with the situation.

[0848] In this way, by combining the emotion engine, it is possible to flexibly adjust study advice and schedules according to the user's emotional state, maximizing the student's learning efficiency. It also enables a quick response to negative emotional states and helps reduce the student's psychological stress. This invention provides a learning support system that allows students, teachers, and parents to work together to comprehensively support the improvement of students' academic ability and psychological health.

[0849] The processing flow will be explained below.

[0850] Step 1:

[0851] The user (teacher) starts the application on the terminal and inputs test result data, including the grade data for each student, the number of correct answers, and comments.

[0852] Step 2:

[0853] The terminal receives the entered test results, converts the data into an appropriate format (e.g., JSON format), and sends it to the server.

[0854] Step 3:

[0855] The server's data collection module receives the test result data sent from the terminal, temporarily stores the received data in a cache, and then stores it in a database.

[0856] Step 4:

[0857] The server's data analysis module retrieves new test result data from the database, while also matching the student's previous performance data and personality assessment results.

[0858] Step 5:

[0859] The server's data analysis module uses AI models to analyze academic performance data and personality assessment results, identifying student weaknesses and learning patterns.

[0860] Step 6:

[0861] The server's data analysis module generates personalized study advice based on the analysis results, such as suggesting specific study content such as "review basic math problems for three hours a week."

[0862] Step 7:

[0863] As users (students) study on their devices, facial and voice data is collected in real time through the device's camera and microphone.

[0864] Step 8:

[0865] The terminal transmits the collected facial expression and voice data to the emotion engine.

[0866] Step 9:

[0867] The server's emotion engine analyzes the received data and recognizes the student's emotions, for example, identifying emotional states such as happiness, stress, and anxiety.

[0868] Step 10:

[0869] The emotion information identified by the emotion engine is received by a data analysis module of the server.

[0870] Step 11:

[0871] Based on the emotional information, the server's data analysis module will provide learning advice and optimize the learning schedule. For example, if a student is feeling stressed, the server will adjust the schedule to allow more time for relaxation.

[0872] Step 12:

[0873] The server's learning schedule generation module automatically generates a learning schedule based on the emotion information and sends it to the terminal.

[0874] Step 13:

[0875] The device will display the new study schedule on the student's device and provide advice based on their emotional state.

[0876] Step 14:

[0877] The user (student) checks the learning schedule on the terminal and performs the learning tasks according to the schedule. After completing the learning, the student enters the progress information on the terminal.

[0878] Step 15:

[0879] The terminal receives progress information entered by the student and sends it to the server.

[0880] Step 16:

[0881] The progress management system on the server receives the progress data sent from the devices and updates the database, monitors the updated progress, and adjusts the student's schedule as necessary.

[0882] Step 17:

[0883] If the emotion engine identifies certain negative emotions (e.g., high stress or anxiety), a notification system will send alerts to teachers and parents.

[0884] Step 18:

[0885] The server's progress management system automatically generates updated progress reports and sends them to teachers and parents in real time.

[0886] Step 19:

[0887] Alerts will be displayed on teachers' and parents' devices, informing them of the student's emotional state and recommended actions to take.

[0888] Step 20:

[0889] Users (teachers, parents) can view reports and notifications on their devices and take necessary actions, such as sending praise or planning additional learning support.

[0890] Through the above processing steps, a learning support system that combines an emotion engine can comprehensively support students' academic improvement and psychological health.

[0891] Example 2

[0892] 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."

[0893] While conventional learning support systems provide learning advice based on students' academic performance data and personality assessment results, they lack the ability to flexibly adjust learning schedules that take into account students' real-time emotional states, or the ability to quickly grasp and respond to students' psychological states. As a result, students may continue studying while feeling stressed, which can lead to reduced learning efficiency. Another issue is that it is difficult for teachers and parents to grasp students' emotional states, resulting in delayed provision of appropriate support.

[0894] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means including an emotion engine that receives and analyzes facial expression and voice data collected from the student's device, a means for adjusting study advice and study schedules based on the analyzed emotion information, and a means for identifying specific emotional states such as severe stress and sending alerts to teachers and parents. This makes it possible to optimize study schedules taking into account the student's real-time emotional state and set aside time for stress reduction. It also enables teachers and parents to quickly grasp the student's emotional state and take appropriate measures.

[0895] "Performance Data" means data including the scores and grades a student receives on exams and tests.

[0896] "Personality assessment results" are data including results obtained from a diagnostic tool for assessing a student's personality and behavioral characteristics.

[0897] "Study advice" is a specific suggestion on study methods and areas for improvement that is generated by analyzing students' academic performance data and personality assessment results.

[0898] A "study schedule" is a plan of a student's learning activities organized along a timeline, including study time and break time.

[0899] "Progress" is data that indicates a student's progress toward a learning goal, including the status of completed and incomplete tasks.

[0900] "Device" refers to a device used by a student, teacher, or parent, including a PC, tablet, or smartphone.

[0901] "Facial expression data" is digital data about students' facial expressions collected through the device's camera.

[0902] "Audio data" refers to digital data relating to students' voices and speech collected through the device's microphone.

[0903] The "emotion engine" is software that analyzes collected facial and voice data to identify a student's emotional state.

[0904] The "data analysis module" is software that analyzes collected data and generates study advice and schedules based on the results.

[0905] The "notification system" is a system for sending notifications about students' emotional state and learning progress to teachers' and parents' devices.

[0906] An "alert" is a warning message sent when the emotion engine identifies a specific negative emotion, intended to alert teachers and parents.

[0907] This invention is a learning support system that monitors students' learning progress in real time and provides appropriate learning advice. By combining it with an emotion engine that recognizes emotions, it realizes flexible advice and adjustment of learning plans based on emotional information.

[0908] Hardware / Software used

[0909] 1. Server

[0910] Data collection module, data analysis module, learning schedule generation module, progress management system, notification system, emotion engine

[0911] 2. Terminal

[0912] Devices including computers, tablets, and smartphones

[0913] 3. Emotion Engine

[0914] Software for analyzing student emotions

[0915] Data processing / data calculation details

[0916] 1. Data Collection

[0917] Students use a device to study. The device is equipped with a camera and microphone, which collects facial expression and voice data in real time. The collected data is sent to a server.

[0918] 2. Emotion analysis

[0919] The server's emotion engine analyzes the received facial and voice data to identify the student's emotional state. Using machine learning models, emotions such as happiness, stress, and anxiety can be identified. The analysis results are stored in a database.

[0920] 3. Study advice and schedule adjustment

[0921] The server's data analysis module analyzes the collected academic performance data and personality assessment results, as well as the emotional data identified by the emotion engine. Based on the analysis results, it generates optimal study advice for students. Furthermore, the study schedule generation module automatically generates a study schedule that reflects the emotional information and sends it to the student's device.

[0922] 4. Notification System

[0923] The server's notification system sends alerts to teachers and parents when the emotion engine identifies specific negative emotions (e.g., high stress or anxiety). These notifications are displayed on the device, providing information about the student's emotional state and recommended actions.

[0924] Specific examples

[0925] For example, a student enters the results of a math test on their device and sends them to the server. At this time, the device's camera captures their facial expressions and the microphone records their voice. The server analyzes this data using an emotion engine to identify whether the student is feeling stressed. Based on the low test score and the emotional data of stress, the data analysis module generates study advice such as "Review the basics for the next test." The study schedule generation module creates a new study schedule that includes breaks and sends it to the student's device. Furthermore, since high stress has been detected, the server sends an alert to the teacher and parents to urge them to take action.

[0926] Example prompts for generative AI models

[0927] "Please tell me how to generate optimal study advice and schedules based on student performance data and emotional information."

[0928] "Give an example of an algorithm that analyzes a student's facial and voice data to recognize stress and adjust their learning plan."

[0929] This will enable the realization of a system that provides learning advice and schedules that correspond to the user's emotional state, maximizing students' learning efficiency and reducing psychological stress.

[0930] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0931] Step 1:

[0932] Data collection

[0933] Users (students) use devices to study. As input, they enter their operation data during study, test results, and study time into the device. The device's camera captures the student's facial expression, and the microphone collects audio. As output, this data is sent to the server. Specifically, it includes real-time facial expression images and audio clips captured by the device, as well as test scores and study logs entered by the student.

[0934] Step 2:

[0935] Emotional Data Analysis

[0936] The server inputs the facial and voice data received from the device into the emotion engine, which analyzes this data and identifies the student's emotional state. Specifically, the machine learning model classifies emotions such as happiness, stress, and anxiety from facial expressions and identifies similar emotional states from voice tone and speed. The output is stored in the server's database as a dataset of emotion identification results.

[0937] Step 3:

[0938] Study advice generation

[0939] The server's data analysis module receives collected grade data, personality assessment results, and emotion data identified by the emotion engine as input. Based on the input data, it analyzes the student's learning tendencies and psychological state and generates personalized study advice. Specifically, it combines low test scores with stress detection to generate advice such as "Review the basics for the next test." The output is the generated study advice.

[0940] Step 4:

[0941] Schedule adjustment

[0942] The server's learning schedule generation module receives the generated learning advice and emotion data as input and adjusts the student's learning schedule. Specifically, if stress levels are high, it creates a schedule that includes more breaks and sets short-term goals to encourage successful experiences. The output is sent to the student's device as a new learning schedule.

[0943] Step 5:

[0944] Notifications and Alerts

[0945] The server's notification system sends alerts to teachers and parents when the emotion engine identifies specific negative emotions (e.g., strong stress or anxiety). The input is the emotion engine's identification result data. Specific operations include generating an alert message and sending a notification directly to the teacher's or parent's device. The output is an alert message that is displayed on the teacher's or parent's device.

[0946] This will allow for optimizing learning schedules based on students' real-time emotional state, allocating time to reduce stress, and enabling teachers and parents to quickly grasp students' emotional state and take appropriate measures.

[0947] (Application example 2)

[0948] 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."

[0949] In production sites, there is a need for a comprehensive support system that can maximize the work efficiency of factory workers while reducing their psychological stress. In particular, there is a lack of a system that can grasp the real-time emotional state of workers and respond immediately based on that, making it difficult to balance productivity and worker safety.

[0950] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting and storing student grade data and personality assessment results, means for analyzing student grade data and personality assessment results and generating study advice, means for automatically generating and displaying student study schedules, means for monitoring user progress and reporting in real time, means for collecting user facial expression and voice data and analyzing the user's emotional state, means for adjusting and displaying a work schedule based on the emotional information, and means for sending notifications to users, managers, and supervisors. This makes it possible to grasp the worker's progress and emotional state in real time and provide appropriate work advice, suggest breaks, or promptly notify the manager.

[0951] "Performance data" is information that records and quantifies the work results and performance of students or factory workers.

[0952] "Personality test results" are the results of a test to evaluate the personality traits and behavioral patterns of students or factory workers.

[0953] "Input and storage means" refers to the set of functions and techniques used to get data into the system and store it in an appropriate database.

[0954] "Means for analyzing and generating learning advice" refers to the functionality for analyzing collected data and providing specific advice based on that data.

[0955] "Means for automatically generating and displaying learning schedules" refers to a function for automatically generating optimal learning or work schedules based on the progress and status of students or factory workers, and displaying them on a terminal.

[0956] "Means for monitoring progress and reporting in real time" refers to the ability to constantly monitor the progress of students and factory workers and report that information to the system in real time.

[0957] "Means for collecting facial and voice data and analyzing emotional states" refers to a function for collecting facial and voice data using a camera or microphone and analyzing it to identify emotional states.

[0958] "Means for adjusting and displaying a work schedule based on emotional information" refers to a function for optimizing a work schedule based on collected emotional information and displaying it on a terminal.

[0959] "Means for sending notifications" refers to the function for sending alerts and notifications to relevant parties (managers and supervisors) based on the status of students and factory workers.

[0960] This invention is a comprehensive system for monitoring the work efficiency and psychological stress of factory workers in real time and providing appropriate work advice and schedule adjustments. The system consists of a server containing many functional modules and devices used by workers (smart glasses, robots, etc.).

[0961] Server Configuration

[0962] The server has the following functional modules:

[0963] 1. Data collection module: Collects worker performance data, personality test results, daily facial expressions, and condition information in real time.

[0964] 2. Data analysis module: Generates work advice based on collected data. In particular, it analyzes emotional information using a generative AI model.

[0965] 3. Work schedule generation module: Automatically generates an optimal work schedule based on the analysis results.

[0966] 4. Progress management system: Monitors the work progress of workers and reports it in real time.

[0967] 5. Notification System: Sends necessary notifications to users, managers and supervisors.

[0968] 6. Emotion engine: Analyzes facial and voice data to identify emotional information.

[0969] Device configuration

[0970] Terminals are devices used by workers, managers and supervisors, and include smart glasses and robots.

[0971] 1. Camera and microphone: Collect facial and voice data of the worker.

[0972] 2. Display: Displays automatically generated work schedules and real-time advice.

[0973] 3. Communication module: Functions for exchanging data with the server.

[0974] System Operation

[0975] The server analyzes facial and voice data collected from the device in real time to identify the worker's emotional state. For example, if stress or fatigue is detected, the work schedule generation module automatically generates a new schedule that includes relaxation time.

[0976] Automatically generated schedules and advice are displayed on the terminal screen, while a notification system alerts managers and supervisors, informing them of the worker's status and recommended actions.

[0977] Specific examples

[0978] For example, consider a situation where a factory worker is working consecutive night shifts. Smart glasses scan the worker's face and detect their fatigue level via a camera and microphone. The system uses a generative AI model to identify high stress levels and automatically insert breaks in the work schedule generation module. A notification is also sent to the manager, stating, "The worker is showing signs of fatigue and should be given a break."

[0979] Example prompt sentence:

[0980] "Monitor the psychological state of factory workers in real time. For example, stress, impatience, etc. If stress levels are high, automatically suggest a break and notify the supervisor."

[0981] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0982] Step 1:

[0983] The terminal uses a camera and microphone to collect facial and voice data of the worker. The collected data is sent to the server in real time. This input data consists of the worker's facial recognition image and voice signal.

[0984] Step 2:

[0985] The server passes the received facial and voice data to the emotion engine, which analyzes this data using a generative AI model to identify the worker's emotional state (e.g., stress, excitement, fatigue, etc.). The analysis results are output.

[0986] Step 3:

[0987] The emotion information output by the emotion engine is received and the server's data analysis module analyzes the worker's situation. This module determines the need for appropriate work advice or schedule adjustments based on the worker's emotional state.

[0988] Step 4:

[0989] The server's work schedule generation module automatically generates an optimal work schedule based on the analysis results. Specifically, if a worker is feeling stressed, a new schedule is created that includes relaxation time and breaks. This new schedule is then output.

[0990] Step 5:

[0991] The generated work schedule and advice are sent to the terminal via the communication module, and the terminal displays the received information on a screen so that the worker can check it.

[0992] Step 6:

[0993] To monitor the worker's progress, data is again collected using cameras and microphones. This data is sent to a server where it is analyzed by the progress management system, and progress is reported.

[0994] Step 7:

[0995] The server's notification system sends alerts to managers and supervisors based on negative emotional states (e.g., high stress or fatigue) identified by the emotion engine. The alerts include the worker's status and recommended actions. A notification is output.

[0996] 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.

[0997] 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.

[0998] 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.

[0999] [Third embodiment]

[1000] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1001] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1002] 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).

[1003] 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.

[1004] 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.

[1005] 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).

[1006] 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.

[1007] 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.

[1008] 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.

[1009] 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.

[1010] 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.

[1011] 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."

[1012] This invention relates to a learning support system that uses AI to help students improve their grades. The system is used by three parties: a server, a terminal, and users (students, teachers, and parents). Specific embodiments for implementing this invention are described below.

[1013] System configuration

[1014] 1. Server

[1015] The server includes a data collection module, a data analysis module, a study schedule generation module, a progress management system, and a notification system.

[1016] The server centrally manages all performance data, personality test results, daily facial expressions and condition information, and plays a central role in analyzing and notifying users.

[1017] 2. Terminal

[1018] Devices are devices used by students, teachers, and parents, and include computers, tablets, and smartphones.

[1019] The device connects to the server and allows users to enter data, view study advice, report progress, and check notifications.

[1020] 3. Users (students, teachers, parents)

[1021] Each user uses a terminal to perform operations according to his or her role.

[1022] Students review and complete their learning tasks and report their progress.

[1023] Teachers enter performance data and monitor learning progress.

[1024] Parents can monitor and support their students' learning progress and notifications.

[1025] Explanation of program processing

[1026] Data collection and input

[1027] 1. User (Teacher)

[1028] The teacher launches the application on the terminal, enters performance data such as test results, and sends it to the server.

[1029] Personality test results and condition information are updated regularly.

[1030] 2. Terminal

[1031] The entered performance data is received, converted into an appropriate format, and sent to the server.

[1032] To provide a UI that allows users to input information intuitively.

[1033] 3. Server

[1034] The data collection module stores the received performance data and personality test results in a database.

[1035] Accumulates condition information and daily facial expression data.

[1036] Data analysis and generation of learning advice

[1037] 1. Server

[1038] The data analysis module analyzes the information in the database to identify students' learning patterns and problems.

[1039] Based on the results of personality assessment and condition information, optimal learning advice is generated, taking into consideration ways to improve learning performance.

[1040] Generate a study schedule

[1041] 1. Server

[1042] The learning schedule generation module automatically generates learning schedules for each student based on the analysis results.

[1043] The schedule created should have a structure that allows it to be adjusted as needed.

[1044] 2. Terminal

[1045] The learning schedule is sent to the student's device and a list of learning tasks is displayed.

[1046] You can visually check today's tasks, weekly schedule, etc.

[1047] Progress monitoring and notifications

[1048] 1. User (Student)

[1049] Students carry out learning tasks and enter progress information into the device after completion.

[1050] 2. Terminal

[1051] Receives progress information and sends it to the server.

[1052] 3. Server

[1053] The progress information received by the progress management system is updated to the database.

[1054] Monitor learning progress and generate and send real-time reports to teachers and parents.

[1055] 4. Server

[1056] A notification system sends important alerts and announcements to students, teachers and parents.

[1057] Specific use cases

[1058] For example, one day, a teacher enters the results of a mathematics test on a device and sends them to the server. The server stores the received score data in a database using a data collection module, and the data analysis module generates study advice based on that data. This advice is specific, such as "It's a good idea to review basic mathematics problems for three hours a week." Next, the study schedule generation module automatically creates a study schedule based on this advice and sends it to the student's device. The student checks this schedule and performs the tasks for the day. Once the tasks are completed, the student enters progress information into the device and sends it to the server. The server updates this information in a progress management system and reports it to teachers and parents in real time. Important progress and alerts are sent to all relevant parties via a notification system, allowing them to take appropriate action.

[1059] The system described above allows students, teachers, and parents to work together to effectively support students' academic improvement. In this way, the present invention realizes a useful and continuous learning support environment.

[1060] The processing flow will be explained below.

[1061] Step 1:

[1062] The user (teacher) starts the application on the terminal and inputs test result data, including the grade data for each student, the number of correct answers, and comments.

[1063] Step 2:

[1064] The terminal receives the entered test results, converts the data into an appropriate format (e.g., JSON format), and sends it to the server.

[1065] Step 3:

[1066] The server's data collection module receives the test result data sent from the terminal, temporarily stores the received data in a cache, and then stores it in a database.

[1067] Step 4:

[1068] The server's data analysis module retrieves new test result data from the database, while also matching the student's previous performance data and personality assessment results.

[1069] Step 5:

[1070] The server's data analysis module uses AI models to analyze academic performance data and personality assessment results, identifying students' weak points in certain subjects and topics, as well as their learning patterns.

[1071] Step 6:

[1072] The server's data analysis module generates personalized study advice based on the analysis results, such as suggesting specific study content such as "review basic math problems for three hours a week."

[1073] Step 7:

[1074] The server's learning schedule generation module receives the generated learning advice and automatically generates a learning schedule for the student based on it, which includes dates, time slots, subjects, specific learning tasks, etc.

[1075] Step 8:

[1076] The server sends the generated learning schedule to the student's terminal, which receives the schedule and displays it on the user interface.

[1077] Step 9:

[1078] The user (student) checks the learning schedule on the terminal and performs the learning tasks according to the schedule. After completing the learning, the student enters the progress information on the terminal.

[1079] Step 10:

[1080] The terminal receives progress information entered by the student and sends it to the server.

[1081] Step 11:

[1082] The progress management system on the server receives the progress data sent from the devices and updates the database, monitors the updated progress, and adjusts the student's schedule as necessary.

[1083] Step 12:

[1084] The server-based progress management system automatically generates and sends updated progress reports to teachers and parents in real time, including progress levels, remaining tasks, and recommended actions.

[1085] Step 13:

[1086] The server's notification system sends important alerts and announcements to students, teachers, and parents, including conference reminders and changes to learning schedules.

[1087] Step 14:

[1088] Users (teachers, parents) can view reports and notifications on their devices and take necessary actions, such as sending praise or planning additional learning support.

[1089] Through the above processing steps, the AI-based learning support system comprehensively supports students in improving their academic ability.

[1090] Example 1

[1091] 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."

[1092] In today's educational environment, there is a lack of systems that effectively support the learning progress of individual students. In particular, there is a need for systems that can comprehensively handle information on students' grades, personalities, and daily conditions, provide appropriate learning advice, and monitor and report individual learning progress in real time. There is also a need for systems that allow teachers and parents to easily check students' progress. To solve these issues, there is a need for the development of an integrated learning support system that allows students, teachers, and parents to work together effectively.

[1093] 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.

[1094] In this invention, the server includes a means for inputting and storing a student's academic performance data and personality assessment results, a means for analyzing the student's academic performance data and personality assessment results and generating study advice, and a means for automatically generating and displaying the student's study schedule. This makes it possible to identify a student's study patterns and problems and provide optimal study advice to each individual. The server also includes a means for monitoring a student's study progress and reporting it in real time, a means for sending notifications to students, teachers, and parents, a terminal means for converting input data into an appropriate format and sending it to the server, an analysis means for identifying study patterns and problems and generating optimal study advice, and a notification means for appropriately sending important alerts and notifications. This enables students, teachers, and parents to work together to effectively support their study progress.

[1095] "Academic performance data" refers to information including the scores and evaluation results obtained by a student on tests and assessments.

[1096] "Personality assessment results" are diagnostic results that are used to evaluate students' personalities and optimize learning methods and approaches.

[1097] "Condition information" includes information about a student's daily health, mood, fatigue level, etc.

[1098] "Study advice" is advice that shows optimal study methods and areas for improvement based on students' academic performance data and personality assessment results.

[1099] A "study schedule" is a schedule that systematically arranges the learning tasks and activities that students should complete within a certain period of time.

[1100] "Learning progress" is information that indicates the completion status and progress of the learning tasks performed by the student.

[1101] "Monitoring" is the act of regularly observing students' learning progress and recording it as data.

[1102] "Notifications" are messages intended to communicate important information or alerts to students, teachers, and parents.

[1103] A "terminal" is a device used by students, teachers, and parents that works in conjunction with a server to input and display data.

[1104] The "data collection module" is a program that has the function of receiving performance data, condition information, etc. and storing it in a database.

[1105] The "data analysis module" is a program that has the function of analyzing received data and identifying students' learning patterns and problems.

[1106] The "study schedule generation module" is a program with the function of automatically generating individual student study schedules.

[1107] A "progress management system" is a system for updating student progress information in a database and monitoring learning progress.

[1108] A "notification system" is a system for sending important information and alerts to students, teachers, and parents.

[1109] This invention relates to a learning support system that uses AI to help students improve their grades. This system is used by three parties: a server, a terminal, and users (students, teachers, and parents). Specific modes for carrying out the invention are described below.

[1110] System configuration

[1111] 1. Server

[1112] The server includes a data collection module, a data analysis module, a study schedule generation module, a progress management system, and a notification system.

[1113] The server centrally manages performance data, personality test results, daily facial expressions and condition information, and plays a central role in analyzing and notifying users.

[1114] 2. Terminal

[1115] Devices are devices used by students, teachers, and parents, and include computers, tablets, and smartphones.

[1116] The device connects to the server and allows users to enter data, view study advice, report progress, and check notifications.

[1117] 3. Users (students, teachers, parents)

[1118] Each user uses a terminal to perform operations according to his or her role.

[1119] Students review and complete their learning tasks and report their progress.

[1120] Teachers enter performance data and monitor learning progress.

[1121] Parents can monitor and support their students' learning progress and notifications.

[1122] Explanation of program processing

[1123] Data collection and input

[1124] User (Teacher)

[1125] The teacher starts a dedicated application on the device and inputs test results, evaluations, personality test results, and condition information. The device receives this data, converts it into an appropriate format, and sends it to the server.

[1126] For example, a teacher enters math test results into an application and clicks a "submit" button, sending the data to a server.

[1127] Receiving and storing data

[1128] server

[1129] The server receives the data sent from the device using a data collection module and stores it in a database, allowing for centralized management of performance data, personality assessment results, condition information, and other data.

[1130] For example, the server may store the received math test results in a database for further analysis.

[1131] Data analysis and generation of learning advice

[1132] server

[1133] The data analysis module analyzes academic performance data and personality assessment results to identify students' learning patterns and problems. Based on the analysis results, a generative AI model is used to generate optimal learning advice.

[1134] For example, the generated advice could be specific, such as "It would be good to review basic math problems for three hours a week."

[1135] Generate and submit a study schedule

[1136] server

[1137] The learning schedule generation module automatically generates individual learning schedules for each student based on the analysis results. The generated schedules are sent to the student's device, where they can review them and proceed with their studies.

[1138] For example, the generated schedule may include a specific task list such as "Monday: 2 hours of math, Tuesday: 1 hour of English."

[1139] Complete learning tasks and submit progress information

[1140] User (student)

[1141] Students check their study schedule on their devices and proceed with their studies according to the plan. When they complete a task, they enter their progress information into the device and send it to the server.

[1142] For example, on the "Today's Tasks" screen, check the completed tasks and click the "Submit" button.

[1143] Progress updates and reporting

[1144] server

[1145] The progress management system updates the received progress information into a database, monitors learning progress, and creates and sends real-time reports to teachers and parents.

[1146] For example, student progress updates are sent to teachers and parents via email and push notifications.

[1147] Sending notifications

[1148] server

[1149] The notification system sends important alerts and announcements to students, teachers and parents, allowing them to take timely action.

[1150] For example, notifications are sent when there is significant improvement in performance or when a particular learning task is not completed.

[1151] Specific use cases

[1152] For example, one day, a teacher enters the results of a math test into a device and sends them to the server. The server's data collection module stores the received grade data in a database, and the data analysis module analyzes the data and generates study advice. This advice is specific, such as "It's a good idea to review basic math problems for three hours a week." The study schedule generation module then automatically creates a study schedule based on this advice and sends it to the student's device. The student checks this schedule and performs the tasks for the day. Once the tasks are completed, the student enters progress information into the device and sends it to the server. The server updates this information in the progress management system and reports it to teachers and parents in real time. Important progress and alerts are sent to all relevant parties via the notification system, allowing them to take appropriate action.

[1153] Example prompts for generative AI models

[1154] Q: What is the specific process from when a teacher enters test results to when study advice is generated?

[1155] Answer: The teacher launches the application on the device, enters the test results, and sends them to the server. The device receives the performance data, converts it into the appropriate format, and sends it to the server. The server's data collection module receives the data and stores it in a database. The data analysis module then analyzes the performance data and personality assessment results and generates study advice using a generative AI model. This advice is used to create an individualized study schedule.

[1156] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1157] Step 1: Enter and submit grade data

[1158] User (Teacher)

[1159] The teacher launches a dedicated application on the device.

[1160] Enter test results, evaluations, personality test results, and condition information.

[1161] This performs the operation to send the data entered by the teacher to the server.

[1162] Input: Test results, evaluation, personality test results, condition information

[1163] Output: The formatted data sent to the server

[1164] Specific behavior:

[1165] Launch the application on your device.

[1166] Enter the score in the Test Result field.

[1167] Enter your personality test results and condition information in the corresponding fields.

[1168] Click the "Send" button.

[1169] Step 2: Receiving and storing data

[1170] Terminal

[1171] The terminal receives the input data and converts it into the appropriate format.

[1172] The converted data is sent to the server.

[1173] server

[1174] The server stores the data received by the data collection module in a database.

[1175] Input: Formatted data (grade data, personality test results, condition information)

[1176] Output: Data stored in the database

[1177] Specific behavior:

[1178] The terminal converts the data into JSON format.

[1179] Sends data to the server via an HTTP POST request.

[1180] On the server side, the data collection module receives the request and stores it in a database.

[1181] Step 3: Analyze the data and generate learning advice

[1182] server

[1183] The data analysis module analyzes the academic performance data and personality test results in the database.

[1184] Identify learning patterns and identify pain points.

[1185] Generative AI models are used to generate optimal learning advice.

[1186] Input: Academic data in the database, personality test results

[1187] Output: The generated study advice

[1188] Specific behavior:

[1189] The data analysis module extracts the required data from the database using SQL queries.

[1190] Use generative AI models (e.g., GPT-3) to generate personalized advice from analyzed data.

[1191] Step 4: Generate and submit your study schedule

[1192] server

[1193] The learning schedule generation module automatically generates individual learning schedules for each student based on the analysis results.

[1194] The learning schedule is sent to the device, and a visual learning task list is displayed on the student's device.

[1195] Input: Analysis results, generated learning advice

[1196] Output: Study schedule sent to student devices

[1197] Specific behavior:

[1198] The learning schedule generation module creates the schedule in JSON format.

[1199] Send the schedule to the device using an HTTP POST request.

[1200] The schedule is displayed on the device in calendar or list format.

[1201] Step 5: Complete the learning task and submit progress information

[1202] User (student)

[1203] Students check their study schedule on their devices and proceed with their studies according to the plan.

[1204] Once the learning task is completed, progress information is entered into the terminal and sent to the server.

[1205] Input: Progress information for completed learning tasks

[1206] Output: Progress information sent to the server

[1207] Specific behavior:

[1208] Open the "Today's Tasks" screen on your device.

[1209] Complete each task and click the "Done" button when complete.

[1210] After entering the progress information, click the "Submit" button.

[1211] Step 6: Update and report progress

[1212] Terminal

[1213] The device receives the progress information entered by the student and sends it to the server.

[1214] server

[1215] The progress management system updates the received progress information in the database.

[1216] The system monitors progress and generates and sends real-time reports to teachers and parents.

[1217] Input: Progress information sent from the device to the server

[1218] Output: Progress information updated in database, reports to teachers and parents

[1219] Specific behavior:

[1220] The device converts the progress data into JSON format.

[1221] Sends progress data to the server via HTTP POST request.

[1222] The progress management system updates the database on the server side and sends notification emails to teachers and parents.

[1223] Step 7: Sending notifications

[1224] server

[1225] A notification system sends important alerts and announcements to students, teachers and parents.

[1226] Input: Critical alerts and notifications

[1227] Output: Message to notify all involved parties

[1228] Specific behavior:

[1229] The notification system periodically checks for alert conditions.

[1230] When an alert occurs, push notifications and emails are sent to students, teachers, and parents.

[1231] (Application example 1)

[1232] 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."

[1233] To improve the safety and efficiency of autonomous vehicle operations, a system is needed that collects and analyzes vehicle driving data and environmental data in real time and provides optimal driving advice and schedules. However, current systems do not adequately collect and analyze driving data, and there is insufficient cooperation between drivers, supervisors, and maintenance personnel, making it difficult to properly manage operations and respond quickly.

[1234] 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.

[1235] In this invention, the server includes means for inputting and storing vehicle driving data and environmental data, means for analyzing the vehicle driving data and environmental data and generating driving advice, and means for automatically generating and displaying an optimal driving schedule. This enables real-time monitoring of the operation of autonomous vehicles, simultaneously improving safety and efficiency, and enabling quick responses between drivers, supervisors, and maintenance personnel.

[1236] "Vehicle" means a means of movement that is mechanically powered and is used to transport people or goods.

[1237] "Driving data" is a general term for information acquired during driving, such as the vehicle's speed, position, direction, and acceleration.

[1238] "Environmental data" refers to data such as meteorological information, road conditions, and traffic information that is collected to understand the situation around the vehicle.

[1239] "Server" means a central computer system for collecting, analyzing, and storing data and sending necessary notifications and instructions.

[1240] "Driving advice" is specific instructions or suggestions provided to improve vehicle safety or efficiency.

[1241] A "service schedule" is a plan for a vehicle to complete a specific route or task within a specific time frame.

[1242] "Monitoring" is the act of monitoring the current status and progress of a vehicle in real time.

[1243] "Notifications" are messages or alerts that convey important information or warnings to operators, supervisors, and maintenance personnel.

[1244] A "supervisor" is a person whose role is to manage the vehicle's operation status and ensure that it is being driven appropriately.

[1245] A "maintenance technician" is a person who performs maintenance and repairs on vehicles and supports their safe and normal operation.

[1246] "Progress" is a status indicating the degree of accomplishment of the task currently being performed by the vehicle.

[1247] This invention relates to a system that utilizes AI to effectively support the operation of autonomous vehicles. The system is used by three parties: a server, a terminal, and users (drivers, supervisors, and maintenance personnel). Specific embodiments for implementing this invention are described below.

[1248] System configuration

[1249] 1. Server

[1250] The server includes a driving data collection module, a data analysis module, a driving schedule generation module, a progress management system, and a notification system.

[1251] The server plays a central role in centrally managing all operational data, environmental data, daily performance information, etc., and analyzing and reporting them.

[1252] 2. Terminal

[1253] Terminals are devices used by drivers, supervisors, and maintenance personnel, and include PCs, tablets, smartphones, etc.

[1254] The device connects to the server and can input data, display operational advice, report progress, and check notifications.

[1255] 3. Users (operators, supervisors, maintenance personnel)

[1256] Each user uses a terminal to perform operations according to his or her role.

[1257] The driver checks and executes operational tasks and reports on progress.

[1258] The supervisor inputs operational data and monitors operational progress.

[1259] The maintenance staff checks and updates the vehicle's maintenance information.

[1260] Explanation of program processing

[1261] 1. Data collection and input

[1262] The server collects driving and environmental data from the vehicle's sensors through a driving data collection module, including speed, location, and obstacle detection information.

[1263] The supervisor starts the application on the terminal, inputs the operation results and sends them to the server, along with daily performance information.

[1264] 2. Data analysis and operational advice generation

[1265] The server's data analysis module uses collected data to identify driving patterns and issues, using machine learning models such as TensorFlow.

[1266] It generates optimal driving advice, providing specific instructions such as "slow down when turning right at the next intersection."

[1267] 3. Generate operation schedule

[1268] The server's operation schedule generation module automatically generates operation schedules for each vehicle based on the analysis results.

[1269] The created schedule is structured so that it can be adjusted as needed and is sent to the driver's terminal.

[1270] 4. Operation progress monitoring and notification

[1271] The progress management system in the server receives the progress information sent from the driver and updates the database.

[1272] The server monitors the operation progress and generates and sends real-time reports to supervisors and maintainers.

[1273] The server's notification system sends important alerts and announcements to operators, supervisors, and maintenance personnel.

[1274] Specific use cases

[1275] For example, one day, a supervisor enters driving results from a terminal and sends the results to a server. The server stores the data received by the driving data collection module in a database, and the data analysis module generates driving advice based on that data. This advice is specific, such as "slow down when turning right at the next intersection." Next, the operation schedule generation module automatically creates an operation schedule based on this advice and sends it to the driver's terminal. The driver checks this schedule and carries out the operation tasks for the day. Once the task is completed, the progress information is entered into the terminal and sent to the server. The server updates this information in the progress management system and reports it to supervisors and maintenance personnel in real time. Important progress and alerts are sent to all relevant parties via the notification system, allowing them to take appropriate action.

[1276] Prompt Sentence Examples

[1277] Monitor the operation status of vehicle ID 'ABC123', generate advice and send necessary notifications to help it reach its destination safely and efficiently.

[1278] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1279] Step 1:

[1280] The server collects driving and environmental data obtained from the vehicle's sensors through the driving data collection module, including speed, position, obstacle detection information, etc. The input is real-time data from the vehicle's sensors, and the output is storing the collected data in a database.

[1281] Step 2:

[1282] The user, a supervisor, launches the application on the terminal, inputs the operation results, and sends them to the server. Daily performance information is also input. The input is the supervisor's operation results and performance data, and the output is the transmission and storage of all input data to the server.

[1283] Step 3:

[1284] The server's data analysis module identifies driving patterns and problems based on the collected data. This uses a generative AI model such as TensorFlow. The input is stored driving data and performance data, and the output is analysis results and driving advice.

[1285] Step 4:

[1286] The server's operation schedule generation module automatically generates operation schedules for each vehicle based on the analysis results. The inputs are the analysis results and driving advice, and the output is the generated operation schedule.

[1287] Step 5:

[1288] The server sends the generated operation schedule to the driver's terminal, and the driver confirms it and starts the actual operation task. The input is the generated operation schedule, and the output is the transmission and display of the schedule to the driver's terminal.

[1289] Step 6:

[1290] The driver, who is the user, performs the operation task and, after completing it, inputs progress information into the terminal and sends it to the server. The input is the progress information entered by the driver, and the output is transmission to the server and storage.

[1291] Step 7:

[1292] The progress management system on the server receives the progress information sent by the driver and updates the database. The input is the progress information, and the output is the update of the database.

[1293] Step 8:

[1294] The server monitors the operation progress and generates and sends real-time reports to supervisors and maintenance personnel based on the analysis results. The input is the updated progress data, and the output is the generated reports and notification messages.

[1295] Step 9:

[1296] The server's notification system sends important alerts and notifications to operators, supervisors, and maintenance personnel. The input is important information or alerts, and the output is the sending of notification messages.

[1297] 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.

[1298] This invention relates to a learning support system that monitors students' learning progress in real time and provides appropriate learning advice. Furthermore, by combining an emotion engine that recognizes the user's emotions, it is possible to provide flexible advice and adjust learning plans based on emotion information. Specific embodiments of this invention are described below.

[1299] System configuration

[1300] 1. Server

[1301] The server includes a data collection module, a data analysis module, a learning schedule generation module, a progress management system, a notification system, and an emotion engine.

[1302] The server centrally manages performance data, personality test results, daily facial expressions, condition information, emotional information, etc., and plays a central role in analyzing and notifying users.

[1303] 2. Terminal

[1304] Devices are devices used by students, teachers, and parents, and include computers, tablets, and smartphones.

[1305] The device connects to the server and allows users to enter data, view study advice, report progress, and check notifications.

[1306] 3. Users (students, teachers, parents)

[1307] Each user uses a terminal to perform operations according to his or her role.

[1308] Students review and complete their learning tasks and report their progress.

[1309] Teachers enter performance data and monitor learning progress.

[1310] Parents can monitor and support their students' learning progress and notifications.

[1311] Applying the Emotion Engine

[1312] Collecting and inputting emotional information

[1313] 1. User (Student)

[1314] As students study on their devices, facial and voice data is collected in real time through the device's camera and microphone.

[1315] 2. Terminal

[1316] The collected facial and voice data is sent to the emotion engine.

[1317] 3. Server

[1318] The emotion engine analyzes the received data and recognizes the student's emotions, for example, identifying emotional states such as happiness, stress, and anxiety.

[1319] Learning advice and schedule adjustment based on emotion information

[1320] 1. Server

[1321] The emotion information identified by the emotion engine is received by a data analysis module.

[1322] Based on emotional information, the system will provide learning advice and optimize the study schedule. For example, if a student is feeling stressed, the system will adjust the schedule to allow more time for relaxation.

[1323] 2. Server

[1324] The learning schedule generation module automatically generates a learning schedule based on the emotional information and transmits it to the terminal.

[1325] 3. Terminal

[1326] The new study schedule will be displayed on the student's device, and advice based on their emotional state will be provided.

[1327] Emotion-based notifications and alerts

[1328] 1. Server

[1329] If the emotion engine identifies certain negative emotions (e.g., high stress or anxiety), a notification system will send alerts to teachers and parents.

[1330] 2. Terminal

[1331] Alerts will appear on teachers' and parents' devices, informing them of the student's emotional state and recommended actions to take.

[1332] Specific use cases

[1333] For example, one day, a student enters their math test results into their device and sends them to the server. The server stores the received score data in a database using a data collection module, and a data analysis module generates study advice based on that data. The emotion engine then analyzes the student's facial and voice data while studying and identifies whether the student is feeling stressed. Based on this, the study schedule generation module automatically generates a new study schedule that includes time for relaxation and sends it to the student's device. Furthermore, if the emotion engine identifies high stress, the server's notification system sends an alert to teachers and parents and provides guidance on how to deal with the situation.

[1334] In this way, by combining the emotion engine, it is possible to flexibly adjust study advice and schedules according to the user's emotional state, maximizing the student's learning efficiency. It also enables a quick response to negative emotional states and helps reduce the student's psychological stress. This invention provides a learning support system that allows students, teachers, and parents to work together to comprehensively support the improvement of students' academic ability and psychological health.

[1335] The processing flow will be explained below.

[1336] Step 1:

[1337] The user (teacher) starts the application on the terminal and inputs test result data, including the grade data for each student, the number of correct answers, and comments.

[1338] Step 2:

[1339] The terminal receives the entered test results, converts the data into an appropriate format (e.g., JSON format), and sends it to the server.

[1340] Step 3:

[1341] The server's data collection module receives the test result data sent from the terminal, temporarily stores the received data in a cache, and then stores it in a database.

[1342] Step 4:

[1343] The server's data analysis module retrieves new test result data from the database, while also matching the student's previous performance data and personality assessment results.

[1344] Step 5:

[1345] The server's data analysis module uses AI models to analyze academic performance data and personality assessment results, identifying student weaknesses and learning patterns.

[1346] Step 6:

[1347] The server's data analysis module generates personalized study advice based on the analysis results, such as suggesting specific study content such as "review basic math problems for three hours a week."

[1348] Step 7:

[1349] As users (students) study on their devices, facial and voice data is collected in real time through the device's camera and microphone.

[1350] Step 8:

[1351] The terminal transmits the collected facial expression and voice data to the emotion engine.

[1352] Step 9:

[1353] The server's emotion engine analyzes the received data and recognizes the student's emotions, for example, identifying emotional states such as happiness, stress, and anxiety.

[1354] Step 10:

[1355] The emotion information identified by the emotion engine is received by a data analysis module of the server.

[1356] Step 11:

[1357] Based on the emotional information, the server's data analysis module will provide learning advice and optimize the learning schedule. For example, if a student is feeling stressed, the server will adjust the schedule to allow more time for relaxation.

[1358] Step 12:

[1359] The server's learning schedule generation module automatically generates a learning schedule based on the emotion information and sends it to the terminal.

[1360] Step 13:

[1361] The device will display the new study schedule on the student's device and provide advice based on their emotional state.

[1362] Step 14:

[1363] The user (student) checks the learning schedule on the terminal and performs the learning tasks according to the schedule. After completing the learning, the student enters the progress information on the terminal.

[1364] Step 15:

[1365] The terminal receives progress information entered by the student and sends it to the server.

[1366] Step 16:

[1367] The progress management system on the server receives the progress data sent from the devices and updates the database, monitors the updated progress, and adjusts the student's schedule as necessary.

[1368] Step 17:

[1369] If the emotion engine identifies certain negative emotions (e.g., high stress or anxiety), a notification system will send alerts to teachers and parents.

[1370] Step 18:

[1371] The server's progress management system automatically generates updated progress reports and sends them to teachers and parents in real time.

[1372] Step 19:

[1373] Alerts will be displayed on teachers' and parents' devices, informing them of the student's emotional state and recommended actions to take.

[1374] Step 20:

[1375] Users (teachers, parents) can view reports and notifications on their devices and take necessary actions, such as sending praise or planning additional learning support.

[1376] Through the above processing steps, a learning support system that combines an emotion engine can comprehensively support students' academic improvement and psychological health.

[1377] Example 2

[1378] 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."

[1379] While conventional learning support systems provide learning advice based on students' academic performance data and personality assessment results, they lack the ability to flexibly adjust learning schedules that take into account students' real-time emotional states, or the ability to quickly grasp and respond to students' psychological states. As a result, students may continue studying while feeling stressed, which can lead to reduced learning efficiency. Another issue is that it is difficult for teachers and parents to grasp students' emotional states, resulting in delayed provision of appropriate support.

[1380] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means including an emotion engine that receives and analyzes facial expression and voice data collected from the student's device, a means for adjusting study advice and study schedules based on the analyzed emotion information, and a means for identifying specific emotional states such as severe stress and sending alerts to teachers and parents. This makes it possible to optimize study schedules taking into account the student's real-time emotional state and set aside time for stress reduction. It also enables teachers and parents to quickly grasp the student's emotional state and take appropriate measures.

[1381] "Performance Data" means data including the scores and grades a student receives on exams and tests.

[1382] "Personality assessment results" are data including results obtained from a diagnostic tool for assessing a student's personality and behavioral characteristics.

[1383] "Study advice" is a specific suggestion on study methods and areas for improvement that is generated by analyzing students' academic performance data and personality assessment results.

[1384] A "study schedule" is a plan of a student's learning activities organized along a timeline, including study time and break time.

[1385] "Progress" is data that indicates a student's progress toward a learning goal, including the status of completed and incomplete tasks.

[1386] "Device" refers to a device used by a student, teacher, or parent, including a PC, tablet, or smartphone.

[1387] "Facial expression data" is digital data about students' facial expressions collected through the device's camera.

[1388] "Audio data" refers to digital data relating to students' voices and speech collected through the device's microphone.

[1389] The "emotion engine" is software that analyzes collected facial and voice data to identify a student's emotional state.

[1390] The "data analysis module" is software that analyzes collected data and generates study advice and schedules based on the results.

[1391] The "notification system" is a system for sending notifications about students' emotional state and learning progress to teachers' and parents' devices.

[1392] An "alert" is a warning message sent when the emotion engine identifies a specific negative emotion, intended to alert teachers and parents.

[1393] This invention is a learning support system that monitors students' learning progress in real time and provides appropriate learning advice. By combining it with an emotion engine that recognizes emotions, it realizes flexible advice and adjustment of learning plans based on emotional information.

[1394] Hardware / Software used

[1395] 1. Server

[1396] Data collection module, data analysis module, learning schedule generation module, progress management system, notification system, emotion engine

[1397] 2. Terminal

[1398] Devices including computers, tablets, and smartphones

[1399] 3. Emotion Engine

[1400] Software for analyzing student emotions

[1401] Data processing / data calculation details

[1402] 1. Data Collection

[1403] Students use a device to study. The device is equipped with a camera and microphone, which collects facial expression and voice data in real time. The collected data is sent to a server.

[1404] 2. Emotion analysis

[1405] The server's emotion engine analyzes the received facial and voice data to identify the student's emotional state. Using machine learning models, emotions such as happiness, stress, and anxiety can be identified. The analysis results are stored in a database.

[1406] 3. Study advice and schedule adjustment

[1407] The server's data analysis module analyzes the collected academic performance data and personality assessment results, as well as the emotional data identified by the emotion engine. Based on the analysis results, it generates optimal study advice for students. Furthermore, the study schedule generation module automatically generates a study schedule that reflects the emotional information and sends it to the student's device.

[1408] 4. Notification System

[1409] The server's notification system sends alerts to teachers and parents when the emotion engine identifies specific negative emotions (e.g., high stress or anxiety). These notifications are displayed on the device, providing information about the student's emotional state and recommended actions.

[1410] Specific examples

[1411] For example, a student enters the results of a math test on their device and sends them to the server. At this time, the device's camera captures their facial expressions and the microphone records their voice. The server analyzes this data using an emotion engine to identify whether the student is feeling stressed. Based on the low test score and the emotional data of stress, the data analysis module generates study advice such as "Review the basics for the next test." The study schedule generation module creates a new study schedule that includes breaks and sends it to the student's device. Furthermore, since high stress has been detected, the server sends an alert to the teacher and parents to urge them to take action.

[1412] Example prompts for generative AI models

[1413] "Please tell me how to generate optimal study advice and schedules based on student performance data and emotional information."

[1414] "Give an example of an algorithm that analyzes a student's facial and voice data to recognize stress and adjust their learning plan."

[1415] This will enable the realization of a system that provides learning advice and schedules that correspond to the user's emotional state, maximizing students' learning efficiency and reducing psychological stress.

[1416] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1417] Step 1:

[1418] Data collection

[1419] Users (students) use devices to study. As input, they enter their operation data during study, test results, and study time into the device. The device's camera captures the student's facial expression, and the microphone collects audio. As output, this data is sent to the server. Specifically, it includes real-time facial expression images and audio clips captured by the device, as well as test scores and study logs entered by the student.

[1420] Step 2:

[1421] Emotional Data Analysis

[1422] The server inputs the facial and voice data received from the device into the emotion engine, which analyzes this data and identifies the student's emotional state. Specifically, the machine learning model classifies emotions such as happiness, stress, and anxiety from facial expressions and identifies similar emotional states from voice tone and speed. The output is stored in the server's database as a dataset of emotion identification results.

[1423] Step 3:

[1424] Study advice generation

[1425] The server's data analysis module receives collected grade data, personality assessment results, and emotion data identified by the emotion engine as input. Based on the input data, it analyzes the student's learning tendencies and psychological state and generates personalized study advice. Specifically, it combines low test scores with stress detection to generate advice such as "Review the basics for the next test." The output is the generated study advice.

[1426] Step 4:

[1427] Schedule adjustment

[1428] The server's learning schedule generation module receives the generated learning advice and emotion data as input and adjusts the student's learning schedule. Specifically, if stress levels are high, it creates a schedule that includes more breaks and sets short-term goals to encourage successful experiences. The output is sent to the student's device as a new learning schedule.

[1429] Step 5:

[1430] Notifications and Alerts

[1431] The server's notification system sends alerts to teachers and parents when the emotion engine identifies specific negative emotions (e.g., strong stress or anxiety). The input is the emotion engine's identification result data. Specific operations include generating an alert message and sending a notification directly to the teacher's or parent's device. The output is an alert message that is displayed on the teacher's or parent's device.

[1432] This will allow for optimizing learning schedules based on students' real-time emotional state, allocating time to reduce stress, and enabling teachers and parents to quickly grasp students' emotional state and take appropriate measures.

[1433] (Application example 2)

[1434] 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."

[1435] In production sites, there is a need for a comprehensive support system that can maximize the work efficiency of factory workers while reducing their psychological stress. In particular, there is a lack of a system that can grasp the real-time emotional state of workers and respond immediately based on that, making it difficult to balance productivity and worker safety.

[1436] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting and storing student grade data and personality assessment results, means for analyzing student grade data and personality assessment results and generating study advice, means for automatically generating and displaying student study schedules, means for monitoring user progress and reporting in real time, means for collecting user facial expression and voice data and analyzing the user's emotional state, means for adjusting and displaying a work schedule based on the emotional information, and means for sending notifications to users, managers, and supervisors. This makes it possible to grasp the worker's progress and emotional state in real time and provide appropriate work advice, suggest breaks, or promptly notify the manager.

[1437] "Performance data" is information that records and quantifies the work results and performance of students or factory workers.

[1438] "Personality test results" are the results of a test to evaluate the personality traits and behavioral patterns of students or factory workers.

[1439] "Input and storage means" refers to the set of functions and techniques used to get data into the system and store it in an appropriate database.

[1440] "Means for analyzing and generating learning advice" refers to the functionality for analyzing collected data and providing specific advice based on that data.

[1441] "Means for automatically generating and displaying learning schedules" refers to a function for automatically generating optimal learning or work schedules based on the progress and status of students or factory workers, and displaying them on a terminal.

[1442] "Means for monitoring progress and reporting in real time" refers to the ability to constantly monitor the progress of students and factory workers and report that information to the system in real time.

[1443] "Means for collecting facial and voice data and analyzing emotional states" refers to a function for collecting facial and voice data using a camera or microphone and analyzing it to identify emotional states.

[1444] "Means for adjusting and displaying a work schedule based on emotional information" refers to a function for optimizing a work schedule based on collected emotional information and displaying it on a terminal.

[1445] "Means for sending notifications" refers to the function for sending alerts and notifications to relevant parties (managers and supervisors) based on the status of students and factory workers.

[1446] This invention is a comprehensive system for monitoring the work efficiency and psychological stress of factory workers in real time and providing appropriate work advice and schedule adjustments. The system consists of a server containing many functional modules and devices used by workers (smart glasses, robots, etc.).

[1447] Server Configuration

[1448] The server has the following functional modules:

[1449] 1. Data collection module: Collects worker performance data, personality test results, daily facial expressions, and condition information in real time.

[1450] 2. Data analysis module: Generates work advice based on collected data. In particular, it analyzes emotional information using a generative AI model.

[1451] 3. Work schedule generation module: Automatically generates an optimal work schedule based on the analysis results.

[1452] 4. Progress management system: Monitors the work progress of workers and reports it in real time.

[1453] 5. Notification System: Sends necessary notifications to users, managers and supervisors.

[1454] 6. Emotion engine: Analyzes facial and voice data to identify emotional information.

[1455] Device configuration

[1456] Terminals are devices used by workers, managers and supervisors, and include smart glasses and robots.

[1457] 1. Camera and microphone: Collect facial and voice data of the worker.

[1458] 2. Display: Displays automatically generated work schedules and real-time advice.

[1459] 3. Communication module: Functions for exchanging data with the server.

[1460] System Operation

[1461] The server analyzes facial and voice data collected from the device in real time to identify the worker's emotional state. For example, if stress or fatigue is detected, the work schedule generation module automatically generates a new schedule that includes relaxation time.

[1462] Automatically generated schedules and advice are displayed on the terminal screen, while a notification system alerts managers and supervisors, informing them of the worker's status and recommended actions.

[1463] Specific examples

[1464] For example, consider a situation where a factory worker is working consecutive night shifts. Smart glasses scan the worker's face and detect their fatigue level via a camera and microphone. The system uses a generative AI model to identify high stress levels and automatically insert breaks in the work schedule generation module. A notification is also sent to the manager, stating, "The worker is showing signs of fatigue and should be given a break."

[1465] Example prompt sentence:

[1466] "Monitor the psychological state of factory workers in real time. For example, stress, impatience, etc. If stress levels are high, automatically suggest a break and notify the supervisor."

[1467] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1468] Step 1:

[1469] The terminal uses a camera and microphone to collect facial and voice data of the worker. The collected data is sent to the server in real time. This input data consists of the worker's facial recognition image and voice signal.

[1470] Step 2:

[1471] The server passes the received facial and voice data to the emotion engine, which analyzes this data using a generative AI model to identify the worker's emotional state (e.g., stress, excitement, fatigue, etc.). The analysis results are output.

[1472] Step 3:

[1473] The emotion information output by the emotion engine is received and the server's data analysis module analyzes the worker's situation. This module determines the need for appropriate work advice or schedule adjustments based on the worker's emotional state.

[1474] Step 4:

[1475] The server's work schedule generation module automatically generates an optimal work schedule based on the analysis results. Specifically, if a worker is feeling stressed, a new schedule is created that includes relaxation time and breaks. This new schedule is then output.

[1476] Step 5:

[1477] The generated work schedule and advice are sent to the terminal via the communication module, and the terminal displays the received information on a screen so that the worker can check it.

[1478] Step 6:

[1479] To monitor the worker's progress, data is again collected using cameras and microphones. This data is sent to a server where it is analyzed by the progress management system, and progress is reported.

[1480] Step 7:

[1481] The server's notification system sends alerts to managers and supervisors based on negative emotional states (e.g., high stress or fatigue) identified by the emotion engine. The alerts include the worker's status and recommended actions. A notification is output.

[1482] 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.

[1483] 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.

[1484] 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.

[1485] [Fourth embodiment]

[1486] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1487] 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.

[1488] 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).

[1489] 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.

[1490] 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.

[1491] 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).

[1492] 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.

[1493] 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.

[1494] 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.

[1495] 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.

[1496] 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.

[1497] 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.

[1498] 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."

[1499] This invention relates to a learning support system that uses AI to help students improve their grades. The system is used by three parties: a server, a terminal, and users (students, teachers, and parents). Specific embodiments for implementing this invention are described below.

[1500] System configuration

[1501] 1. Server

[1502] The server includes a data collection module, a data analysis module, a study schedule generation module, a progress management system, and a notification system.

[1503] The server centrally manages all performance data, personality test results, daily facial expressions and condition information, and plays a central role in analyzing and notifying users.

[1504] 2. Terminal

[1505] Devices are devices used by students, teachers, and parents, and include computers, tablets, and smartphones.

[1506] The device connects to the server and allows users to enter data, view study advice, report progress, and check notifications.

[1507] 3. Users (students, teachers, parents)

[1508] Each user uses a terminal to perform operations according to his or her role.

[1509] Students review and complete their learning tasks and report their progress.

[1510] Teachers enter performance data and monitor learning progress.

[1511] Parents can monitor and support their students' learning progress and notifications.

[1512] Explanation of program processing

[1513] Data collection and input

[1514] 1. User (Teacher)

[1515] The teacher launches the application on the terminal, enters performance data such as test results, and sends it to the server.

[1516] Personality test results and condition information are updated regularly.

[1517] 2. Terminal

[1518] The entered performance data is received, converted into an appropriate format, and sent to the server.

[1519] To provide a UI that allows users to input information intuitively.

[1520] 3. Server

[1521] The data collection module stores the received performance data and personality test results in a database.

[1522] Accumulates condition information and daily facial expression data.

[1523] Data analysis and generation of learning advice

[1524] 1. Server

[1525] The data analysis module analyzes the information in the database to identify students' learning patterns and problems.

[1526] Based on the results of personality assessment and condition information, optimal learning advice is generated, taking into consideration ways to improve learning performance.

[1527] Generate a study schedule

[1528] 1. Server

[1529] The learning schedule generation module automatically generates learning schedules for each student based on the analysis results.

[1530] The schedule created should have a structure that allows it to be adjusted as needed.

[1531] 2. Terminal

[1532] The learning schedule is sent to the student's device and a list of learning tasks is displayed.

[1533] You can visually check today's tasks, weekly schedule, etc.

[1534] Progress monitoring and notifications

[1535] 1. User (Student)

[1536] Students carry out learning tasks and enter progress information into the device after completion.

[1537] 2. Terminal

[1538] Receives progress information and sends it to the server.

[1539] 3. Server

[1540] The progress information received by the progress management system is updated to the database.

[1541] Monitor learning progress and generate and send real-time reports to teachers and parents.

[1542] 4. Server

[1543] A notification system sends important alerts and announcements to students, teachers and parents.

[1544] Specific use cases

[1545] For example, one day, a teacher enters the results of a mathematics test on a device and sends them to the server. The server stores the received score data in a database using a data collection module, and the data analysis module generates study advice based on that data. This advice is specific, such as "It's a good idea to review basic mathematics problems for three hours a week." Next, the study schedule generation module automatically creates a study schedule based on this advice and sends it to the student's device. The student checks this schedule and performs the tasks for the day. Once the tasks are completed, the student enters progress information into the device and sends it to the server. The server updates this information in a progress management system and reports it to teachers and parents in real time. Important progress and alerts are sent to all relevant parties via a notification system, allowing them to take appropriate action.

[1546] The system described above allows students, teachers, and parents to work together to effectively support students' academic improvement. In this way, the present invention realizes a useful and continuous learning support environment.

[1547] The processing flow will be explained below.

[1548] Step 1:

[1549] The user (teacher) starts the application on the terminal and inputs test result data, including the grade data for each student, the number of correct answers, and comments.

[1550] Step 2:

[1551] The terminal receives the entered test results, converts the data into an appropriate format (e.g., JSON format), and sends it to the server.

[1552] Step 3:

[1553] The server's data collection module receives the test result data sent from the terminal, temporarily stores the received data in a cache, and then stores it in a database.

[1554] Step 4:

[1555] The server's data analysis module retrieves new test result data from the database, while also matching the student's previous performance data and personality assessment results.

[1556] Step 5:

[1557] The server's data analysis module uses AI models to analyze academic performance data and personality assessment results, identifying students' weak points in certain subjects and topics, as well as their learning patterns.

[1558] Step 6:

[1559] The server's data analysis module generates personalized study advice based on the analysis results, such as suggesting specific study content such as "review basic math problems for three hours a week."

[1560] Step 7:

[1561] The server's learning schedule generation module receives the generated learning advice and automatically generates a learning schedule for the student based on it, which includes dates, time slots, subjects, specific learning tasks, etc.

[1562] Step 8:

[1563] The server sends the generated learning schedule to the student's terminal, which receives the schedule and displays it on the user interface.

[1564] Step 9:

[1565] The user (student) checks the learning schedule on the terminal and performs the learning tasks according to the schedule. After completing the learning, the student enters the progress information on the terminal.

[1566] Step 10:

[1567] The terminal receives progress information entered by the student and sends it to the server.

[1568] Step 11:

[1569] The progress management system on the server receives the progress data sent from the devices and updates the database, monitors the updated progress, and adjusts the student's schedule as necessary.

[1570] Step 12:

[1571] The server-based progress management system automatically generates and sends updated progress reports to teachers and parents in real time, including progress levels, remaining tasks, and recommended actions.

[1572] Step 13:

[1573] The server's notification system sends important alerts and announcements to students, teachers, and parents, including conference reminders and changes to learning schedules.

[1574] Step 14:

[1575] Users (teachers, parents) can view reports and notifications on their devices and take necessary actions, such as sending praise or planning additional learning support.

[1576] Through the above processing steps, the AI-based learning support system comprehensively supports students in improving their academic ability.

[1577] Example 1

[1578] 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."

[1579] In today's educational environment, there is a lack of systems that effectively support the learning progress of individual students. In particular, there is a need for systems that can comprehensively handle information on students' grades, personalities, and daily conditions, provide appropriate learning advice, and monitor and report individual learning progress in real time. There is also a need for systems that allow teachers and parents to easily check students' progress. To solve these issues, there is a need for the development of an integrated learning support system that allows students, teachers, and parents to work together effectively.

[1580] 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.

[1581] In this invention, the server includes a means for inputting and storing a student's academic performance data and personality assessment results, a means for analyzing the student's academic performance data and personality assessment results and generating study advice, and a means for automatically generating and displaying the student's study schedule. This makes it possible to identify a student's study patterns and problems and provide optimal study advice to each individual. The server also includes a means for monitoring a student's study progress and reporting it in real time, a means for sending notifications to students, teachers, and parents, a terminal means for converting input data into an appropriate format and sending it to the server, an analysis means for identifying study patterns and problems and generating optimal study advice, and a notification means for appropriately sending important alerts and notifications. This enables students, teachers, and parents to work together to effectively support their study progress.

[1582] "Academic performance data" refers to information including the scores and evaluation results obtained by a student on tests and assessments.

[1583] "Personality assessment results" are diagnostic results that are used to evaluate students' personalities and optimize learning methods and approaches.

[1584] "Condition information" includes information about a student's daily health, mood, fatigue level, etc.

[1585] "Study advice" is advice that shows optimal study methods and areas for improvement based on students' academic performance data and personality assessment results.

[1586] A "study schedule" is a schedule that systematically arranges the learning tasks and activities that students should complete within a certain period of time.

[1587] "Learning progress" is information that indicates the completion status and progress of the learning tasks performed by the student.

[1588] "Monitoring" is the act of regularly observing students' learning progress and recording it as data.

[1589] "Notifications" are messages intended to communicate important information or alerts to students, teachers, and parents.

[1590] A "terminal" is a device used by students, teachers, and parents that works in conjunction with a server to input and display data.

[1591] The "data collection module" is a program that has the function of receiving performance data, condition information, etc. and storing it in a database.

[1592] The "data analysis module" is a program that has the function of analyzing received data and identifying students' learning patterns and problems.

[1593] The "study schedule generation module" is a program with the function of automatically generating individual student study schedules.

[1594] A "progress management system" is a system for updating student progress information in a database and monitoring learning progress.

[1595] A "notification system" is a system for sending important information and alerts to students, teachers, and parents.

[1596] This invention relates to a learning support system that uses AI to help students improve their grades. This system is used by three parties: a server, a terminal, and users (students, teachers, and parents). Specific modes for carrying out the invention are described below.

[1597] System configuration

[1598] 1. Server

[1599] The server includes a data collection module, a data analysis module, a study schedule generation module, a progress management system, and a notification system.

[1600] The server centrally manages performance data, personality test results, daily facial expressions and condition information, and plays a central role in analyzing and notifying users.

[1601] 2. Terminal

[1602] Devices are devices used by students, teachers, and parents, and include computers, tablets, and smartphones.

[1603] The device connects to the server and allows users to enter data, view study advice, report progress, and check notifications.

[1604] 3. Users (students, teachers, parents)

[1605] Each user uses a terminal to perform operations according to his or her role.

[1606] Students review and complete their learning tasks and report their progress.

[1607] Teachers enter performance data and monitor learning progress.

[1608] Parents can monitor and support their students' learning progress and notifications.

[1609] Explanation of program processing

[1610] Data collection and input

[1611] User (Teacher)

[1612] The teacher starts a dedicated application on the device and inputs test results, evaluations, personality test results, and condition information. The device receives this data, converts it into an appropriate format, and sends it to the server.

[1613] For example, a teacher enters math test results into an application and clicks a "submit" button, sending the data to a server.

[1614] Receiving and storing data

[1615] server

[1616] The server receives the data sent from the device using a data collection module and stores it in a database, allowing for centralized management of performance data, personality assessment results, condition information, and other data.

[1617] For example, the server may store the received math test results in a database for further analysis.

[1618] Data analysis and generation of learning advice

[1619] server

[1620] The data analysis module analyzes academic performance data and personality assessment results to identify students' learning patterns and problems. Based on the analysis results, a generative AI model is used to generate optimal learning advice.

[1621] For example, the generated advice could be specific, such as "It would be good to review basic math problems for three hours a week."

[1622] Generate and submit a study schedule

[1623] server

[1624] The learning schedule generation module automatically generates individual learning schedules for each student based on the analysis results. The generated schedules are sent to the student's device, where they can review them and proceed with their studies.

[1625] For example, the generated schedule may include a specific task list such as "Monday: 2 hours of math, Tuesday: 1 hour of English."

[1626] Complete learning tasks and submit progress information

[1627] User (student)

[1628] Students check their study schedule on their devices and proceed with their studies according to the plan. When they complete a task, they enter their progress information into the device and send it to the server.

[1629] For example, on the "Today's Tasks" screen, check the completed tasks and click the "Submit" button.

[1630] Progress updates and reporting

[1631] server

[1632] The progress management system updates the received progress information into a database, monitors learning progress, and creates and sends real-time reports to teachers and parents.

[1633] For example, student progress updates are sent to teachers and parents via email and push notifications.

[1634] Sending notifications

[1635] server

[1636] The notification system sends important alerts and announcements to students, teachers and parents, allowing them to take timely action.

[1637] For example, notifications are sent when there is significant improvement in performance or when a particular learning task is not completed.

[1638] Specific use cases

[1639] For example, one day, a teacher enters the results of a math test into a device and sends them to the server. The server's data collection module stores the received grade data in a database, and the data analysis module analyzes the data and generates study advice. This advice is specific, such as "It's a good idea to review basic math problems for three hours a week." The study schedule generation module then automatically creates a study schedule based on this advice and sends it to the student's device. The student checks this schedule and performs the tasks for the day. Once the tasks are completed, the student enters progress information into the device and sends it to the server. The server updates this information in the progress management system and reports it to teachers and parents in real time. Important progress and alerts are sent to all relevant parties via the notification system, allowing them to take appropriate action.

[1640] Example prompts for generative AI models

[1641] Q: What is the specific process from when a teacher enters test results to when study advice is generated?

[1642] Answer: The teacher launches the application on the device, enters the test results, and sends them to the server. The device receives the performance data, converts it into the appropriate format, and sends it to the server. The server's data collection module receives the data and stores it in a database. The data analysis module then analyzes the performance data and personality assessment results and generates study advice using a generative AI model. This advice is used to create an individualized study schedule.

[1643] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1644] Step 1: Enter and submit grade data

[1645] User (Teacher)

[1646] The teacher launches a dedicated application on the device.

[1647] Enter test results, evaluations, personality test results, and condition information.

[1648] This performs the operation to send the data entered by the teacher to the server.

[1649] Input: Test results, evaluation, personality test results, condition information

[1650] Output: The formatted data sent to the server

[1651] Specific behavior:

[1652] Launch the application on your device.

[1653] Enter the score in the Test Result field.

[1654] Enter your personality test results and condition information in the corresponding fields.

[1655] Click the "Send" button.

[1656] Step 2: Receiving and storing data

[1657] Terminal

[1658] The terminal receives the input data and converts it into the appropriate format.

[1659] The converted data is sent to the server.

[1660] server

[1661] The server stores the data received by the data collection module in a database.

[1662] Input: Formatted data (grade data, personality test results, condition information)

[1663] Output: Data stored in the database

[1664] Specific behavior:

[1665] The terminal converts the data into JSON format.

[1666] Sends data to the server via an HTTP POST request.

[1667] On the server side, the data collection module receives the request and stores it in a database.

[1668] Step 3: Analyze the data and generate learning advice

[1669] server

[1670] The data analysis module analyzes the academic performance data and personality test results in the database.

[1671] Identify learning patterns and identify pain points.

[1672] Generative AI models are used to generate optimal learning advice.

[1673] Input: Academic data in the database, personality test results

[1674] Output: The generated study advice

[1675] Specific behavior:

[1676] The data analysis module extracts the required data from the database using SQL queries.

[1677] Use generative AI models (e.g., GPT-3) to generate personalized advice from analyzed data.

[1678] Step 4: Generate and submit your study schedule

[1679] server

[1680] The learning schedule generation module automatically generates individual learning schedules for each student based on the analysis results.

[1681] The learning schedule is sent to the device, and a visual learning task list is displayed on the student's device.

[1682] Input: Analysis results, generated learning advice

[1683] Output: Study schedule sent to student devices

[1684] Specific behavior:

[1685] The learning schedule generation module creates the schedule in JSON format.

[1686] Send the schedule to the device using an HTTP POST request.

[1687] The schedule is displayed on the device in calendar or list format.

[1688] Step 5: Complete the learning task and submit progress information

[1689] User (student)

[1690] Students check their study schedule on their devices and proceed with their studies according to the plan.

[1691] Once the learning task is completed, progress information is entered into the terminal and sent to the server.

[1692] Input: Progress information for completed learning tasks

[1693] Output: Progress information sent to the server

[1694] Specific behavior:

[1695] Open the "Today's Tasks" screen on your device.

[1696] Complete each task and click the "Done" button when complete.

[1697] After entering the progress information, click the "Submit" button.

[1698] Step 6: Update and report progress

[1699] Terminal

[1700] The device receives the progress information entered by the student and sends it to the server.

[1701] server

[1702] The progress management system updates the received progress information in the database.

[1703] The system monitors progress and generates and sends real-time reports to teachers and parents.

[1704] Input: Progress information sent from the device to the server

[1705] Output: Progress information updated in database, reports to teachers and parents

[1706] Specific behavior:

[1707] The device converts the progress data into JSON format.

[1708] Sends progress data to the server via HTTP POST request.

[1709] The progress management system updates the database on the server side and sends notification emails to teachers and parents.

[1710] Step 7: Sending notifications

[1711] server

[1712] A notification system sends important alerts and announcements to students, teachers and parents.

[1713] Input: Critical alerts and notifications

[1714] Output: Message to notify all involved parties

[1715] Specific behavior:

[1716] The notification system periodically checks for alert conditions.

[1717] When an alert occurs, push notifications and emails are sent to students, teachers, and parents.

[1718] (Application example 1)

[1719] 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."

[1720] To improve the safety and efficiency of autonomous vehicle operations, a system is needed that collects and analyzes vehicle driving data and environmental data in real time and provides optimal driving advice and schedules. However, current systems do not adequately collect and analyze driving data, and there is insufficient cooperation between drivers, supervisors, and maintenance personnel, making it difficult to properly manage operations and respond quickly.

[1721] 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.

[1722] In this invention, the server includes means for inputting and storing vehicle driving data and environmental data, means for analyzing the vehicle driving data and environmental data and generating driving advice, and means for automatically generating and displaying an optimal driving schedule. This enables real-time monitoring of the operation of autonomous vehicles, simultaneously improving safety and efficiency, and enabling quick responses between drivers, supervisors, and maintenance personnel.

[1723] "Vehicle" means a means of movement that is mechanically powered and is used to transport people or goods.

[1724] "Driving data" is a general term for information acquired during driving, such as the vehicle's speed, position, direction, and acceleration.

[1725] "Environmental data" refers to data such as meteorological information, road conditions, and traffic information that is collected to understand the situation around the vehicle.

[1726] "Server" means a central computer system for collecting, analyzing, and storing data and sending necessary notifications and instructions.

[1727] "Driving advice" is specific instructions or suggestions provided to improve vehicle safety or efficiency.

[1728] A "service schedule" is a plan for a vehicle to complete a specific route or task within a specific time frame.

[1729] "Monitoring" is the act of monitoring the current status and progress of a vehicle in real time.

[1730] "Notifications" are messages or alerts that convey important information or warnings to operators, supervisors, and maintenance personnel.

[1731] A "supervisor" is a person whose role is to manage the vehicle's operation status and ensure that it is being driven appropriately.

[1732] A "maintenance technician" is a person who performs maintenance and repairs on vehicles and supports their safe and normal operation.

[1733] "Progress" is a status indicating the degree of accomplishment of the task currently being performed by the vehicle.

[1734] This invention relates to a system that utilizes AI to effectively support the operation of autonomous vehicles. The system is used by three parties: a server, a terminal, and users (drivers, supervisors, and maintenance personnel). Specific embodiments for implementing this invention are described below.

[1735] System configuration

[1736] 1. Server

[1737] The server includes a driving data collection module, a data analysis module, a driving schedule generation module, a progress management system, and a notification system.

[1738] The server plays a central role in centrally managing all operational data, environmental data, daily performance information, etc., and analyzing and reporting them.

[1739] 2. Terminal

[1740] Terminals are devices used by drivers, supervisors, and maintenance personnel, and include PCs, tablets, smartphones, etc.

[1741] The device connects to the server and can input data, display operational advice, report progress, and check notifications.

[1742] 3. Users (operators, supervisors, maintenance personnel)

[1743] Each user uses a terminal to perform operations according to his or her role.

[1744] The driver checks and executes operational tasks and reports on progress.

[1745] The supervisor inputs operational data and monitors operational progress.

[1746] The maintenance staff checks and updates the vehicle's maintenance information.

[1747] Explanation of program processing

[1748] 1. Data collection and input

[1749] The server collects driving and environmental data from the vehicle's sensors through a driving data collection module, including speed, location, and obstacle detection information.

[1750] The supervisor starts the application on the terminal, inputs the operation results and sends them to the server, along with daily performance information.

[1751] 2. Data analysis and operational advice generation

[1752] The server's data analysis module uses collected data to identify driving patterns and issues, using machine learning models such as TensorFlow.

[1753] It generates optimal driving advice, providing specific instructions such as "slow down when turning right at the next intersection."

[1754] 3. Generate operation schedule

[1755] The server's operation schedule generation module automatically generates operation schedules for each vehicle based on the analysis results.

[1756] The created schedule is structured so that it can be adjusted as needed and is sent to the driver's terminal.

[1757] 4. Operation progress monitoring and notification

[1758] The progress management system in the server receives the progress information sent from the driver and updates the database.

[1759] The server monitors the operation progress and generates and sends real-time reports to supervisors and maintainers.

[1760] The server's notification system sends important alerts and announcements to operators, supervisors, and maintenance personnel.

[1761] Specific use cases

[1762] For example, one day, a supervisor enters driving results from a terminal and sends the results to a server. The server stores the data received by the driving data collection module in a database, and the data analysis module generates driving advice based on that data. This advice is specific, such as "slow down when turning right at the next intersection." Next, the operation schedule generation module automatically creates an operation schedule based on this advice and sends it to the driver's terminal. The driver checks this schedule and carries out the operation tasks for the day. Once the task is completed, the progress information is entered into the terminal and sent to the server. The server updates this information in the progress management system and reports it to supervisors and maintenance personnel in real time. Important progress and alerts are sent to all relevant parties via the notification system, allowing them to take appropriate action.

[1763] Prompt Sentence Examples

[1764] Monitor the operation status of vehicle ID 'ABC123', generate advice and send necessary notifications to help it reach its destination safely and efficiently.

[1765] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1766] Step 1:

[1767] The server collects driving and environmental data obtained from the vehicle's sensors through the driving data collection module, including speed, position, obstacle detection information, etc. The input is real-time data from the vehicle's sensors, and the output is storing the collected data in a database.

[1768] Step 2:

[1769] The user, a supervisor, launches the application on the terminal, inputs the operation results, and sends them to the server. Daily performance information is also input. The input is the supervisor's operation results and performance data, and the output is the transmission and storage of all input data to the server.

[1770] Step 3:

[1771] The server's data analysis module identifies driving patterns and problems based on the collected data. This uses a generative AI model such as TensorFlow. The input is stored driving data and performance data, and the output is analysis results and driving advice.

[1772] Step 4:

[1773] The server's operation schedule generation module automatically generates operation schedules for each vehicle based on the analysis results. The inputs are the analysis results and driving advice, and the output is the generated operation schedule.

[1774] Step 5:

[1775] The server sends the generated operation schedule to the driver's terminal, and the driver confirms it and starts the actual operation task. The input is the generated operation schedule, and the output is the transmission and display of the schedule to the driver's terminal.

[1776] Step 6:

[1777] The driver, who is the user, performs the operation task and, after completing it, inputs progress information into the terminal and sends it to the server. The input is the progress information entered by the driver, and the output is transmission to the server and storage.

[1778] Step 7:

[1779] The progress management system on the server receives the progress information sent by the driver and updates the database. The input is the progress information, and the output is the update of the database.

[1780] Step 8:

[1781] The server monitors the operation progress and generates and sends real-time reports to supervisors and maintenance personnel based on the analysis results. The input is the updated progress data, and the output is the generated reports and notification messages.

[1782] Step 9:

[1783] The server's notification system sends important alerts and notifications to operators, supervisors, and maintenance personnel. The input is important information or alerts, and the output is the sending of notification messages.

[1784] 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.

[1785] This invention relates to a learning support system that monitors students' learning progress in real time and provides appropriate learning advice. Furthermore, by combining an emotion engine that recognizes the user's emotions, it is possible to provide flexible advice and adjust learning plans based on emotion information. Specific embodiments of this invention are described below.

[1786] System configuration

[1787] 1. Server

[1788] The server includes a data collection module, a data analysis module, a learning schedule generation module, a progress management system, a notification system, and an emotion engine.

[1789] The server centrally manages performance data, personality test results, daily facial expressions, condition information, emotional information, etc., and plays a central role in analyzing and notifying users.

[1790] 2. Terminal

[1791] Devices are devices used by students, teachers, and parents, and include computers, tablets, and smartphones.

[1792] The device connects to the server and allows users to enter data, view study advice, report progress, and check notifications.

[1793] 3. Users (students, teachers, parents)

[1794] Each user uses a terminal to perform operations according to his or her role.

[1795] Students review and complete their learning tasks and report their progress.

[1796] Teachers enter performance data and monitor learning progress.

[1797] Parents can monitor and support their students' learning progress and notifications.

[1798] Applying the Emotion Engine

[1799] Collecting and inputting emotional information

[1800] 1. User (Student)

[1801] As students study on their devices, facial and voice data is collected in real time through the device's camera and microphone.

[1802] 2. Terminal

[1803] The collected facial and voice data is sent to the emotion engine.

[1804] 3. Server

[1805] The emotion engine analyzes the received data and recognizes the student's emotions, for example, identifying emotional states such as happiness, stress, and anxiety.

[1806] Learning advice and schedule adjustment based on emotion information

[1807] 1. Server

[1808] The emotion information identified by the emotion engine is received by a data analysis module.

[1809] Based on emotional information, the system will provide learning advice and optimize the study schedule. For example, if a student is feeling stressed, the system will adjust the schedule to allow more time for relaxation.

[1810] 2. Server

[1811] The learning schedule generation module automatically generates a learning schedule based on the emotional information and transmits it to the terminal.

[1812] 3. Terminal

[1813] The new study schedule will be displayed on the student's device, and advice based on their emotional state will be provided.

[1814] Emotion-based notifications and alerts

[1815] 1. Server

[1816] If the emotion engine identifies certain negative emotions (e.g., high stress or anxiety), a notification system will send alerts to teachers and parents.

[1817] 2. Terminal

[1818] Alerts will appear on teachers' and parents' devices, informing them of the student's emotional state and recommended actions to take.

[1819] Specific use cases

[1820] For example, one day, a student enters their math test results into their device and sends them to the server. The server stores the received score data in a database using a data collection module, and a data analysis module generates study advice based on that data. The emotion engine then analyzes the student's facial and voice data while studying and identifies whether the student is feeling stressed. Based on this, the study schedule generation module automatically generates a new study schedule that includes time for relaxation and sends it to the student's device. Furthermore, if the emotion engine identifies high stress, the server's notification system sends an alert to teachers and parents and provides guidance on how to deal with the situation.

[1821] In this way, by combining the emotion engine, it is possible to flexibly adjust study advice and schedules according to the user's emotional state, maximizing the student's learning efficiency. It also enables a quick response to negative emotional states and helps reduce the student's psychological stress. This invention provides a learning support system that allows students, teachers, and parents to work together to comprehensively support the improvement of students' academic ability and psychological health.

[1822] The processing flow will be explained below.

[1823] Step 1:

[1824] The user (teacher) starts the application on the terminal and inputs test result data, including the grade data for each student, the number of correct answers, and comments.

[1825] Step 2:

[1826] The terminal receives the entered test results, converts the data into an appropriate format (e.g., JSON format), and sends it to the server.

[1827] Step 3:

[1828] The server's data collection module receives the test result data sent from the terminal, temporarily stores the received data in a cache, and then stores it in a database.

[1829] Step 4:

[1830] The server's data analysis module retrieves new test result data from the database, while also matching the student's previous performance data and personality assessment results.

[1831] Step 5:

[1832] The server's data analysis module uses AI models to analyze academic performance data and personality assessment results, identifying student weaknesses and learning patterns.

[1833] Step 6:

[1834] The server's data analysis module generates personalized study advice based on the analysis results, such as suggesting specific study content such as "review basic math problems for three hours a week."

[1835] Step 7:

[1836] As users (students) study on their devices, facial and voice data is collected in real time through the device's camera and microphone.

[1837] Step 8:

[1838] The terminal transmits the collected facial expression and voice data to the emotion engine.

[1839] Step 9:

[1840] The server's emotion engine analyzes the received data and recognizes the student's emotions, for example, identifying emotional states such as happiness, stress, and anxiety.

[1841] Step 10:

[1842] The emotion information identified by the emotion engine is received by a data analysis module of the server.

[1843] Step 11:

[1844] Based on the emotional information, the server's data analysis module will provide learning advice and optimize the learning schedule. For example, if a student is feeling stressed, the server will adjust the schedule to allow more time for relaxation.

[1845] Step 12:

[1846] The server's learning schedule generation module automatically generates a learning schedule based on the emotion information and sends it to the terminal.

[1847] Step 13:

[1848] The device will display the new study schedule on the student's device and provide advice based on their emotional state.

[1849] Step 14:

[1850] The user (student) checks the learning schedule on the terminal and performs the learning tasks according to the schedule. After completing the learning, the student enters the progress information on the terminal.

[1851] Step 15:

[1852] The terminal receives progress information entered by the student and sends it to the server.

[1853] Step 16:

[1854] The progress management system on the server receives the progress data sent from the devices and updates the database, monitors the updated progress, and adjusts the student's schedule as necessary.

[1855] Step 17:

[1856] If the emotion engine identifies certain negative emotions (e.g., high stress or anxiety), a notification system will send alerts to teachers and parents.

[1857] Step 18:

[1858] The server's progress management system automatically generates updated progress reports and sends them to teachers and parents in real time.

[1859] Step 19:

[1860] Alerts will be displayed on teachers' and parents' devices, informing them of the student's emotional state and recommended actions to take.

[1861] Step 20:

[1862] Users (teachers, parents) can view reports and notifications on their devices and take necessary actions, such as sending praise or planning additional learning support.

[1863] Through the above processing steps, a learning support system that combines an emotion engine can comprehensively support students' academic improvement and psychological health.

[1864] Example 2

[1865] 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."

[1866] While conventional learning support systems provide learning advice based on students' academic performance data and personality assessment results, they lack the ability to flexibly adjust learning schedules that take into account students' real-time emotional states, or the ability to quickly grasp and respond to students' psychological states. As a result, students may continue studying while feeling stressed, which can lead to reduced learning efficiency. Another issue is that it is difficult for teachers and parents to grasp students' emotional states, resulting in delayed provision of appropriate support.

[1867] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means including an emotion engine that receives and analyzes facial expression and voice data collected from the student's device, a means for adjusting study advice and study schedules based on the analyzed emotion information, and a means for identifying specific emotional states such as severe stress and sending alerts to teachers and parents. This makes it possible to optimize study schedules taking into account the student's real-time emotional state and set aside time for stress reduction. It also enables teachers and parents to quickly grasp the student's emotional state and take appropriate measures.

[1868] "Performance Data" means data including the scores and grades a student receives on exams and tests.

[1869] "Personality assessment results" are data including results obtained from a diagnostic tool for assessing a student's personality and behavioral characteristics.

[1870] "Study advice" is a specific suggestion on study methods and areas for improvement that is generated by analyzing students' academic performance data and personality assessment results.

[1871] A "study schedule" is a plan of a student's learning activities organized along a timeline, including study time and break time.

[1872] "Progress" is data that indicates a student's progress toward a learning goal, including the status of completed and incomplete tasks.

[1873] "Device" refers to a device used by a student, teacher, or parent, including a PC, tablet, or smartphone.

[1874] "Facial expression data" is digital data about students' facial expressions collected through the device's camera.

[1875] "Audio data" refers to digital data relating to students' voices and speech collected through the device's microphone.

[1876] The "emotion engine" is software that analyzes collected facial and voice data to identify a student's emotional state.

[1877] The "data analysis module" is software that analyzes collected data and generates study advice and schedules based on the results.

[1878] The "notification system" is a system for sending notifications about students' emotional state and learning progress to teachers' and parents' devices.

[1879] An "alert" is a warning message sent when the emotion engine identifies a specific negative emotion, intended to alert teachers and parents.

[1880] This invention is a learning support system that monitors students' learning progress in real time and provides appropriate learning advice. By combining it with an emotion engine that recognizes emotions, it realizes flexible advice and adjustment of learning plans based on emotional information.

[1881] Hardware / Software used

[1882] 1. Server

[1883] Data collection module, data analysis module, learning schedule generation module, progress management system, notification system, emotion engine

[1884] 2. Terminal

[1885] Devices including computers, tablets, and smartphones

[1886] 3. Emotion Engine

[1887] Software for analyzing student emotions

[1888] Data processing / data calculation details

[1889] 1. Data Collection

[1890] Students use a device to study. The device is equipped with a camera and microphone, which collects facial expression and voice data in real time. The collected data is sent to a server.

[1891] 2. Emotion analysis

[1892] The server's emotion engine analyzes the received facial and voice data to identify the student's emotional state. Using machine learning models, emotions such as happiness, stress, and anxiety can be identified. The analysis results are stored in a database.

[1893] 3. Study advice and schedule adjustment

[1894] The server's data analysis module analyzes the collected academic performance data and personality assessment results, as well as the emotional data identified by the emotion engine. Based on the analysis results, it generates optimal study advice for students. Furthermore, the study schedule generation module automatically generates a study schedule that reflects the emotional information and sends it to the student's device.

[1895] 4. Notification System

[1896] The server's notification system sends alerts to teachers and parents when the emotion engine identifies specific negative emotions (e.g., high stress or anxiety). These notifications are displayed on the device, providing information about the student's emotional state and recommended actions.

[1897] Specific examples

[1898] For example, a student enters the results of a math test on their device and sends them to the server. At this time, the device's camera captures their facial expressions and the microphone records their voice. The server analyzes this data using an emotion engine to identify whether the student is feeling stressed. Based on the low test score and the emotional data of stress, the data analysis module generates study advice such as "Review the basics for the next test." The study schedule generation module creates a new study schedule that includes breaks and sends it to the student's device. Furthermore, since high stress has been detected, the server sends an alert to the teacher and parents to urge them to take action.

[1899] Example prompts for generative AI models

[1900] "Please tell me how to generate optimal study advice and schedules based on student performance data and emotional information."

[1901] "Give an example of an algorithm that analyzes a student's facial and voice data to recognize stress and adjust their learning plan."

[1902] This will enable the realization of a system that provides learning advice and schedules that correspond to the user's emotional state, maximizing students' learning efficiency and reducing psychological stress.

[1903] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1904] Step 1:

[1905] Data collection

[1906] Users (students) use devices to study. As input, they enter their operation data during study, test results, and study time into the device. The device's camera captures the student's facial expression, and the microphone collects audio. As output, this data is sent to the server. Specifically, it includes real-time facial expression images and audio clips captured by the device, as well as test scores and study logs entered by the student.

[1907] Step 2:

[1908] Emotional Data Analysis

[1909] The server inputs the facial and voice data received from the device into the emotion engine, which analyzes this data and identifies the student's emotional state. Specifically, the machine learning model classifies emotions such as happiness, stress, and anxiety from facial expressions and identifies similar emotional states from voice tone and speed. The output is stored in the server's database as a dataset of emotion identification results.

[1910] Step 3:

[1911] Study advice generation

[1912] The server's data analysis module receives collected grade data, personality assessment results, and emotion data identified by the emotion engine as input. Based on the input data, it analyzes the student's learning tendencies and psychological state and generates personalized study advice. Specifically, it combines low test scores with stress detection to generate advice such as "Review the basics for the next test." The output is the generated study advice.

[1913] Step 4:

[1914] Schedule adjustment

[1915] The server's learning schedule generation module receives the generated learning advice and emotion data as input and adjusts the student's learning schedule. Specifically, if stress levels are high, it creates a schedule that includes more breaks and sets short-term goals to encourage successful experiences. The output is sent to the student's device as a new learning schedule.

[1916] Step 5:

[1917] Notifications and Alerts

[1918] The server's notification system sends alerts to teachers and parents when the emotion engine identifies specific negative emotions (e.g., strong stress or anxiety). The input is the emotion engine's identification result data. Specific operations include generating an alert message and sending a notification directly to the teacher's or parent's device. The output is an alert message that is displayed on the teacher's or parent's device.

[1919] This will allow for optimizing learning schedules based on students' real-time emotional state, allocating time to reduce stress, and enabling teachers and parents to quickly grasp students' emotional state and take appropriate measures.

[1920] (Application example 2)

[1921] 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."

[1922] In production sites, there is a need for a comprehensive support system that can maximize the work efficiency of factory workers while reducing their psychological stress. In particular, there is a lack of a system that can grasp the real-time emotional state of workers and respond immediately based on that, making it difficult to balance productivity and worker safety.

[1923] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting and storing student grade data and personality assessment results, means for analyzing student grade data and personality assessment results and generating study advice, means for automatically generating and displaying student study schedules, means for monitoring user progress and reporting in real time, means for collecting user facial expression and voice data and analyzing the user's emotional state, means for adjusting and displaying a work schedule based on the emotional information, and means for sending notifications to users, managers, and supervisors. This makes it possible to grasp the worker's progress and emotional state in real time and provide appropriate work advice, suggest breaks, or promptly notify the manager.

[1924] "Performance data" is information that records and quantifies the work results and performance of students or factory workers.

[1925] "Personality test results" are the results of a test to evaluate the personality traits and behavioral patterns of students or factory workers.

[1926] "Input and storage means" refers to the set of functions and techniques used to get data into the system and store it in an appropriate database.

[1927] "Means for analyzing and generating learning advice" refers to the functionality for analyzing collected data and providing specific advice based on that data.

[1928] "Means for automatically generating and displaying learning schedules" refers to a function for automatically generating optimal learning or work schedules based on the progress and status of students or factory workers, and displaying them on a terminal.

[1929] "Means for monitoring progress and reporting in real time" refers to the ability to constantly monitor the progress of students and factory workers and report that information to the system in real time.

[1930] "Means for collecting facial and voice data and analyzing emotional states" refers to a function for collecting facial and voice data using a camera or microphone and analyzing it to identify emotional states.

[1931] "Means for adjusting and displaying a work schedule based on emotional information" refers to a function for optimizing a work schedule based on collected emotional information and displaying it on a terminal.

[1932] "Means for sending notifications" refers to the function for sending alerts and notifications to relevant parties (managers and supervisors) based on the status of students and factory workers.

[1933] This invention is a comprehensive system for monitoring the work efficiency and psychological stress of factory workers in real time and providing appropriate work advice and schedule adjustments. The system consists of a server containing many functional modules and devices used by workers (smart glasses, robots, etc.).

[1934] Server Configuration

[1935] The server has the following functional modules:

[1936] 1. Data collection module: Collects worker performance data, personality test results, daily facial expressions, and condition information in real time.

[1937] 2. Data analysis module: Generates work advice based on collected data. In particular, it analyzes emotional information using a generative AI model.

[1938] 3. Work schedule generation module: Automatically generates an optimal work schedule based on the analysis results.

[1939] 4. Progress management system: Monitors the work progress of workers and reports it in real time.

[1940] 5. Notification System: Sends necessary notifications to users, managers and supervisors.

[1941] 6. Emotion engine: Analyzes facial and voice data to identify emotional information.

[1942] Device configuration

[1943] Terminals are devices used by workers, managers and supervisors, and include smart glasses and robots.

[1944] 1. Camera and microphone: Collect facial and voice data of the worker.

[1945] 2. Display: Displays automatically generated work schedules and real-time advice.

[1946] 3. Communication module: Functions for exchanging data with the server.

[1947] System Operation

[1948] The server analyzes facial and voice data collected from the device in real time to identify the worker's emotional state. For example, if stress or fatigue is detected, the work schedule generation module automatically generates a new schedule that includes relaxation time.

[1949] Automatically generated schedules and advice are displayed on the terminal screen, while a notification system alerts managers and supervisors, informing them of the worker's status and recommended actions.

[1950] Specific examples

[1951] For example, consider a situation where a factory worker is working consecutive night shifts. Smart glasses scan the worker's face and detect their fatigue level via a camera and microphone. The system uses a generative AI model to identify high stress levels and automatically insert breaks in the work schedule generation module. A notification is also sent to the manager, stating, "The worker is showing signs of fatigue and should be given a break."

[1952] Example prompt sentence:

[1953] "Monitor the psychological state of factory workers in real time. For example, stress, impatience, etc. If stress levels are high, automatically suggest a break and notify the supervisor."

[1954] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1955] Step 1:

[1956] The terminal uses a camera and microphone to collect facial and voice data of the worker. The collected data is sent to the server in real time. This input data consists of the worker's facial recognition image and voice signal.

[1957] Step 2:

[1958] The server passes the received facial and voice data to the emotion engine, which analyzes this data using a generative AI model to identify the worker's emotional state (e.g., stress, excitement, fatigue, etc.). The analysis results are output.

[1959] Step 3:

[1960] The emotion information output by the emotion engine is received and the server's data analysis module analyzes the worker's situation. This module determines the need for appropriate work advice or schedule adjustments based on the worker's emotional state.

[1961] Step 4:

[1962] The server's work schedule generation module automatically generates an optimal work schedule based on the analysis results. Specifically, if a worker is feeling stressed, a new schedule is created that includes relaxation time and breaks. This new schedule is then output.

[1963] Step 5:

[1964] The generated work schedule and advice are sent to the terminal via the communication module, and the terminal displays the received information on a screen so that the worker can check it.

[1965] Step 6:

[1966] To monitor the worker's progress, data is again collected using cameras and microphones. This data is sent to a server where it is analyzed by the progress management system, and progress is reported.

[1967] Step 7:

[1968] The server's notification system sends alerts to managers and supervisors based on negative emotional states (e.g., high stress or fatigue) identified by the emotion engine. The alerts include the worker's status and recommended actions. A notification is output.

[1969] 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.

[1970] 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.

[1971] 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 robot 414.

[1972] 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.

[1973] 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.

[1974] 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.

[1975] 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).

[1976] 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.

[1977] 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."

[1978] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1979] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1980] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1981] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1982] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1983] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1984] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1985] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1986] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1987] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1988] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1989] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1990] The following is further disclosed regarding the above embodiment.

[1991] (Claim 1)

[1992] A means for inputting and storing student achievement data and personality assessment results;

[1993] A means for analyzing the student's academic performance data and personality assessment results and generating study advice;

[1994] means for automatically generating and displaying a student's learning schedule;

[1995] A means of monitoring and reporting student progress in real time;

[1996] means for sending notifications to students, teachers and parents;

[1997] A learning support system including:

[1998] (Claim 2)

[1999] A means of collecting information on students' daily expressions and conditions;

[2000] A means for adjusting study advice and study schedules based on the collected information;

[2001] The learning support system according to claim 1, further comprising:

[2002] (Claim 3)

[2003] means for receiving and storing teacher-entered test results in a database;

[2004] a means of communicating student progress reports to teachers and parents;

[2005] The learning support system according to claim 1, further comprising:

[2006] "Example 1"

[2007] (Claim 1)

[2008] A means for inputting and storing student achievement data and personality assessment results;

[2009] A means for analyzing the student's academic performance data and personality assessment results and generating study advice;

[2010] means for automatically generating and displaying a student's learning schedule;

[2011] A means of monitoring and reporting student progress in real time;

[2012] means for sending notifications to students, teachers and parents;

[2013] a terminal means for converting input data into an appropriate format and transmitting the data to a server;

[2014] An analytical method to identify learning patterns and problems and generate optimal learning advice;

[2015] Notification methods to send important alerts and announcements in a timely manner;

[2016] A system including:

[2017] (Claim 2)

[2018] A means of collecting information on students' daily expressions and conditions;

[2019] A means for adjusting study advice and study schedules based on the collected information;

[2020] terminal means for receiving student progress information and transmitting it to a server;

[2021] a means for updating progress information by a progress management system in the server;

[2022] The system of claim 1 further comprising:

[2023] (Claim 3)

[2024] means for receiving and storing teacher-entered test results in a database;

[2025] a means of communicating student progress reports to teachers and parents;

[2026] means for transmitting performance data to a server using a dedicated application;

[2027] means for converting the format of the data and transmitting it to a server;

[2028] The system of claim 1 further comprising:

[2029] "Application Example 1"

[2030] (Claim 1)

[2031] means for inputting and storing vehicle operating and environmental data;

[2032] means for analyzing vehicle driving data and environmental data and generating driving advice;

[2033] A means for automatically generating and displaying an optimal operation schedule;

[2034] A means of monitoring and reporting on the vehicle's operational progress in real time;

[2035] means for sending notifications to operators, supervisors, and maintenance personnel;

[2036] A system including:

[2037] (Claim 2)

[2038] a means for collecting daily vehicle performance and condition information;

[2039] a means for adjusting driving advice and operation schedules based on the collected information;

[2040] The system of claim 1 further comprising:

[2041] (Claim 3)

[2042] means for receiving the operation results input by the supervisor and storing them in a database;

[2043] a means for notifying the supervisor and the maintainer of the progress of the vehicle's operation;

[2044] The system of claim 1 further comprising:

[2045] "Example 2: Combining Emotion Engines"

[2046] (Claim 1)

[2047] A means for inputting and storing student achievement data and personality assessment results;

[2048] A means for analyzing the student's academic performance data and personality assessment results and generating study advice;

[2049] means for automatically generating and displaying a student's learning schedule;

[2050] A means of monitoring and reporting student progress in real time;

[2051] A means including an emotion engine for receiving and analyzing facial expression and voice data collected from the student's device;

[2052] means for adjusting study advice and study schedules based on the analyzed emotion information;

[2053] means for identifying specific emotional states, such as high stress, and sending alerts to teachers and parents;

[2054] A system including:

[2055] (Claim 2)

[2056] A means for collecting daily facial expression and voice data of students and transmitting it to a server;

[2057] means for analyzing the collected information on the server and identifying the emotional state of the student;

[2058] 10. The system of claim 1.

[2059] (Claim 3)

[2060] means for receiving and storing teacher-entered test results in a database;

[2061] a means of informing teachers and parents of reports of students' emotional state and academic progress;

[2062] 10. The system of claim 1.

[2063] "Application example 2 when combining emotion engines"

[2064] New Claims

[2065] (Claim 1)

[2066] A means for inputting and storing student achievement data and personality assessment results;

[2067] A means for analyzing the student's academic performance data and personality assessment results and generating study advice;

[2068] means for automatically generating and displaying a student's learning schedule;

[2069] A means of monitoring and reporting user progress in real time;

[2070] means for collecting facial expression and voice data of a user and analyzing the emotional state of the user;

[2071] a means for adjusting and displaying a work schedule based on the emotion information;

[2072] means for sending notifications to users, managers and supervisors;

[2073] A system including:

[2074] (Claim 2)

[2075] A means for collecting daily facial expression and condition information of a user;

[2076] A means for adjusting work advice and work schedules based on the collected information;

[2077] The system of claim 1 further comprising:

[2078] (Claim 3)

[2079] means for receiving the work results entered by the supervisor and storing them in a database;

[2080] a means of communicating the employer's progress to managers and supervisors;

[2081] The system of claim 1 further comprising: [Explanation of symbols]

[2082] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for inputting and storing student achievement data and personality assessment results; A means for analyzing the student's academic performance data and personality assessment results and generating study advice; means for automatically generating and displaying a student's learning schedule; A means of monitoring and reporting student progress in real time; means for sending notifications to students, teachers and parents; A learning support system including:

2. A means of collecting information on students' daily expressions and conditions; A means for adjusting study advice and study schedules based on the collected information; The learning support system according to claim 1 , further comprising:

3. means for receiving and storing teacher-entered test results in a database; a means of communicating student progress reports to teachers and parents; The learning support system according to claim 1 , further comprising:

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