system

Abstract

We provide the system. [Solution] A means of collecting individual health data of users, A means of analyzing collected health data in real time, A means of generating individual care plans based on analysis results, Means of providing the generated care plan to the user, A means of regularly monitoring and re-evaluating the progress of users, A system that includes means of providing feedback to parents or instructors based on the user's health status and progress.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional education system, there was a problem that learning support according to the individual needs of each student was not sufficiently provided. Also, it was difficult to grasp the learning progress of students in real time and conduct appropriate educational guidance based on it. As a result, the learning effect of students was not maximized, and it was difficult for teachers and guardians to accurately confirm the understanding level of students and provide appropriate feedback.

Means for Solving the Problems

[0005] This invention provides means for collecting and analyzing student learning data in real time, and means for generating individually optimized learning plans based on the analysis results. Furthermore, it constructs a system that dynamically adjusts learning content by providing the generated learning plan to students and periodically monitoring their progress. In addition, it enables effective adjustment of instructional policies by providing feedback to parents or teachers based on students' learning progress. This aims to provide customized educational support for each student and maximize learning effectiveness.

[0006] A "student" is an individual who receives individualized educational support and is provided with a plan based on their learning progress.

[0007] "Data" refers to information about students' learning content, grades, progress, etc., and is the subject of analysis based on this information.

[0008] "Analysis" is the process of evaluating students' learning progress and understanding based on collected data, and generating individually optimized learning plans.

[0009] A "learning plan" refers to an educational plan that includes learning materials, assignments, and test preparation materials, all tailored to each student's individual needs and progress.

[0010] "Feedback" refers to information provided to parents or instructors regarding evaluations of students' learning status and progress, which is used to adjust teaching methods. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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), or Bluetooth (registered trademark).

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the 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.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0025] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] The system for implementing this invention collects individual student learning data and generates and provides an optimal learning plan based on that data. This system is broadly composed of five functions: data collection, data analysis, learning plan generation, progress monitoring, and feedback provision. These functions work closely together to provide a seamless learning environment.

[0033] First, the user (student) uses a device to input daily learning data. This is often done on an online platform, and the input data includes homework progress, test scores, and time spent studying. The device sends this data to the server in real time.

[0034] Next, the server analyzes each student's current learning progress based on the collected data. An AI model processes this information to identify each student's strengths and areas of weakness. Based on this analysis, the server generates a customized learning plan. This plan includes the necessary materials and assignments, what to focus on next, and appropriate test preparation strategies.

[0035] The generated learning plan is provided to the user (student) via their device. This allows students to learn at their own pace. At the same time, the server periodically monitors the results of applying the learning plan and the students' progress, enabling immediate adjustments to the learning plan.

[0036] Furthermore, after a certain period of time, the server summarizes the progress of learning outcomes and generates a detailed feedback report. This feedback is provided to users (teachers and parents), allowing them to improve teaching methods as needed. This feedback clearly explains students' strengths and areas for improvement, making it extremely useful in actual educational activities.

[0037] As a concrete example, let's consider a high school student who struggles with mechanics in physics. The server analyzes the student's previous test results and past learning history to generate a learning plan that focuses on the fundamental concepts of mechanics. This plan includes interactive video materials and practice problems, allowing the student to deepen their understanding efficiently. The server also tracks progress as the student makes progress and updates the plan when sufficient results are seen.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] Users (students) use a learning app on their device to input data about their daily learning activities and progress. This data includes homework completion status, test results, content of lessons attended, and time spent studying.

[0041] Step 2:

[0042] The device receives the learning data entered by the user and prepares to send it to the server. The data is configured to be sent to the server in real time.

[0043] Step 3:

[0044] The server receives the training data sent from the terminal. The received data is stored in the database and simultaneously checked for any defects in data integrity or format.

[0045] Step 4:

[0046] The server uses AI models to analyze students' learning progress based on data stored in the database. This analysis identifies students' learning progress, level of understanding, and areas where they struggle.

[0047] Step 5:

[0048] Based on the analysis results, the server generates a learning plan optimized for the student's needs. The plan includes recommended materials, homework assignments, and next tasks to tackle.

[0049] Step 6:

[0050] The device presents the user (student) with a learning plan sent from the server. This allows the student to proceed with their learning activities based on the new plan.

[0051] Step 7:

[0052] The server periodically monitors the user's learning progress and re-evaluates the plan based on the analysis results. It updates the learning plan as needed.

[0053] Step 8:

[0054] The server generates a feedback report based on the student's learning progress and provides it to the parent or teacher.

[0055] Step 9:

[0056] Users (parents or teachers) can review feedback reports received from the server and adjust student guidance strategies. They can also provide direct feedback to students, offering encouragement and suggesting areas for improvement.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] In today's educational environment, there is a demand for individualized instruction tailored to each student's learning characteristics. However, it is difficult for teachers and parents to accurately grasp students' progress and understanding, and to provide effective instruction as needed. Furthermore, there is a lack of methods for creating appropriate learning plans and dynamically adjusting them while regularly monitoring their progress. In conventional systems, handling student data and updating plans is often done manually, which is time-consuming and labor-intensive, making effective individualized instruction difficult.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes means for collecting individual student learning information, means for processing the collected information in real time, and means for creating individualized learning plans for students based on the processing results. This enables the automatic generation and monitoring of learning plans tailored to each student. The system also utilizes a generation AI model to flexibly adjust plans according to the student's progress and level of understanding, providing efficient and effective learning support.

[0062] "Individualized student learning information" refers to specific data about each student, such as their educational progress, grades, level of understanding, study time, and progress on assignments.

[0063] "Means of collection" refers to systems or processes for automatically or manually collecting learning information entered by users.

[0064] "Real-time processing methods" refer to technical mechanisms for instantly analyzing collected information and providing rapid feedback or results based on that data.

[0065] "Methods for creating educational plans" refers to the process of designing a learning plan that takes into account the content, materials, assignments, and test preparation that students should learn, based on their individual learning information.

[0066] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to analyze data and automatically create and update the optimal learning strategy and plan for the user.

[0067] "Monitoring" is an oversight activity that involves regularly tracking students' learning progress and understanding, and modifying the educational plan as needed.

[0068] "Means of providing advice" refer to communication methods that effectively convey the generated feedback to parents and educators and help improve educational policies.

[0069] The system for implementing this invention aims to provide individualized education tailored to the learning characteristics of each student. This system functions primarily through interaction between a server, terminals, and users, and is implemented in the following manner.

[0070] Users (students) input daily learning information via a device. This input includes homework progress, test scores, and time spent studying. The entered information is transmitted from the device to the server in real time. The devices used here are compatible with a variety of devices, including PCs, tablets, and smartphones. To ensure data security, secure protocols such as SSL are used for communication.

[0071] The server stores the received data and analyzes it in real time using an AI model. The AI ​​model utilizes supervised learning and generative AI models to identify students' strengths and areas for improvement. Based on this analysis, the server generates a customized learning plan. This plan includes learning materials, assignments, and test preparation strategies. For example, for a student struggling with mechanics in physics, a learning plan focused on fundamental mechanics concepts is created, providing interactive video materials and practice problems.

[0072] The generated educational plans are provided to users via a terminal. The terminal's interface is designed for intuitive operation, enabling users to learn effectively according to the learning plan. The server further uses a generating AI model to monitor students' progress and understanding, and updates the learning plan as needed. In addition, regular monitoring generates feedback reports on educational progress and outcomes, which are provided to parents and educators, thereby improving the quality of individualized instruction.

[0073] A concrete example of a prompt message could be, "Based on this student's physics learning history, generate a customized learning plan focused on mechanics," which could be input into the generating AI model.

[0074] In this way, the system of this invention can provide students with an optimal learning environment and significantly improve the efficiency and effectiveness of educational activities.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] Users (students) input learning information using a terminal. This information includes homework progress, test scores, and study time. This data is entered through the terminal's interface, converted to a data format, and then sent to the server. Input data formats include numerical data, text data, and time data.

[0078] Step 2:

[0079] The terminal transmits the entered learning information to the server in real time. This process utilizes secure protocols such as SSL to ensure data security. During transmission, the data is divided into data packets, which are then reconstructed on the server side. The transmitted data is then ready to be registered in the database.

[0080] Step 3:

[0081] The server stores the received training data in a database. During storage, data integrity checks and formatting are performed. This stored data is then used as foundational information for AI analysis.

[0082] Step 4:

[0083] The server inputs the stored data into an AI model and performs data analysis. The AI ​​model uses supervised learning and performs data mining to recognize students' strengths and weaknesses. This analysis determines their level of understanding in specific subjects and skills. The output provides an evaluation of each student's level of understanding.

[0084] Step 5:

[0085] The server uses a generated AI model based on the analysis results of the AI ​​model to generate an optimized learning plan for the student. The generated plan includes specific teaching materials, assignments, and test preparation strategies. This plan generation process uses prompts to define a plan that suits the student. An example of a prompt might be, "Based on this student's physics learning history, generate a learning plan that focuses on mechanics."

[0086] Step 6:

[0087] The generated educational plans are provided to users via a terminal. The terminal screen displays the plan's contents in detail, helping students effectively progress through their studies according to its content. An interface is provided that allows users to access the information they need.

[0088] Step 7:

[0089] The server regularly monitors users' learning progress and re-analyzes it whenever new data is entered. This allows for timely updates of the educational plan through the generative AI model. Progress feedback reports are generated regularly and sent to parents and educators.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] Managing the health of users, including the elderly, presents a challenge in providing customized care plans tailored to individual needs. Conventional systems rely on a uniform approach, failing to adequately address individual health conditions and lifestyles, making efficient health maintenance difficult. This invention aims to improve individual health maintenance and quality of life by providing personalized care plans tailored to each user.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for collecting individual health data of users, means for analyzing the collected health data in real time, and means for generating individual care plans based on the analysis results. This makes it possible to generate and provide care plans tailored to the user's health condition.

[0095] "User" refers to the person receiving the service, and is the subject from whom health data is collected in this system.

[0096] "Health data" refers to information collected about the user's physical condition and lifestyle, which is used to create care plans.

[0097] "Methods for real-time analysis" refer to technologies that process collected health data immediately and quickly grasp the user's current condition.

[0098] An "individualized care plan" refers to a plan that includes customized care guidance based on the user's health condition and lifestyle.

[0099] "Monitoring progress" refers to continuously recording and evaluating the user's response to the care plan and changes in their health condition.

[0100] "Feedback" refers to evaluations and advice provided to users, their guardians, and instructors, and is information that can be used to adjust care plans.

[0101] The system that implements this application example implements a program to individually manage users' health data and provide optimal care plans. The server plays a central role, processing the collected data and performing real-time analysis. Specifically, the hardware consists of terminals used by users (smartphones or care robots), which transmit health data to the server. The server receives this data and analyzes the user's health status using an AI model implemented in Python.

[0102] Based on the analysis results, the server automatically generates individualized care plans and provides them to users via their terminals. These care plans include personalized dietary suggestions and exercise advice tailored to each user's health condition, contributing to improvements in their daily lives. The server also periodically monitors the user's progress and generates feedback. This feedback monitors changes in the user's condition and the effectiveness of the plan in real time, suggesting the next steps to take.

[0103] As a concrete example, to improve the cardiac health of elderly individuals, a suitable walking plan may be created based on daily heart rate data. The server analyzes changes in heart rate and advises on appropriate exercise intensity and frequency, helping users maintain their cardiac health. An example of a prompt in this case would be, "Generate an exercise plan effective for cardiac health based on current heart rate data." In this way, the system provides optimal care for each user, supporting the maintenance and improvement of their health.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The device collects the user's daily activity and health data. This data includes heart rate, steps taken, and meal records. The device periodically sends this data to the server. Input is the data recorded by the user, and output is the data sent to the server.

[0107] Step 2:

[0108] The server analyzes data received from the terminal in real time. Here, an AI model implemented in Python processes the data and extracts parameters for evaluating the user's health status. The input is raw data received from the terminal, and the output is analyzed health assessment information. Specifically, it calculates the average and variability of the obtained heart rate and generates health status indicators.

[0109] Step 3:

[0110] The server uses a generative AI model to create an individualized care plan based on health assessment information. The input is the assessment information obtained in step 2, and the output is a care plan that includes actions recommended for the user. Specifically, it selects and plans exercise and dietary content appropriate for the user. Prompt messages are also generated in this step, with instructions such as "Create the optimal exercise plan from the current data" being generated by the generative AI model.

[0111] Step 4:

[0112] The terminal presents the care plan received from the server to the user. The input is the care plan sent from the server, and the output is the display to the user. The user can adjust their daily life based on this. Specifically, the terminal displays the plan contents using alerts and notifications to make it easy for the user to understand.

[0113] Step 5:

[0114] The server monitors data updates from the terminal and continuously monitors the user's progress. The input is newly acquired health data, and the output is feedback based on the progress. Specifically, it evaluates whether progress is proceeding according to plan and prepares to adjust the plan as needed. This feedback is ultimately returned to the user.

[0115] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0116] This invention is a system that not only collects and analyzes individual student learning data, but also incorporates an emotion engine that recognizes students' emotions in real time, thereby further optimizing learning plans. The system consists of three main functions: data collection, emotion analysis, learning plan generation, progress monitoring, and feedback provision.

[0117] First, the user (student) uses a device to input their learning progress and activities. In this process, the device may be equipped with sensors to recognize the student's nonverbal responses (e.g., changes in facial expressions or voice), thereby collecting emotional data.

[0118] The device sends collected learning data and sentiment data to the server. This data is designed to be stored on the server in real time.

[0119] The server uses an AI model to analyze the student's learning data and emotional data that it receives. By combining the learning data and emotional data, it is possible to understand not only the student's current level of comprehension but also their emotional state, such as learning stress or decreased motivation.

[0120] Based on these analysis results, the server generates an individually optimized learning plan. This plan may include assignments tailored to the student's learning progress, learning materials to enhance optimal learning, and possibly encouraging and relaxing content that matches the student's emotional state.

[0121] The generated learning plan is provided to the user via the device. Students proceed with their studies based on this plan, while the device continuously monitors changes in the student's emotions and dynamically adjusts the plan as needed.

[0122] Furthermore, the server generates a feedback report that combines learning and emotional progress, and provides this to parents or teachers. This feedback includes not only the student's learning progress but also guidance that takes into account emotional support.

[0123] As a concrete example, when an elementary school student is working on a math problem and encounters a difficult one, the emotion engine recognizes the student's frustration. Based on this data, the server adjusts the difficulty of the problem and generates a new assignment with appropriate explanations. In this way, the system maximizes the effectiveness of student learning while maintaining their motivation to learn.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The user (student) launches the learning app and logs into their device to begin their daily learning activities. At this point, they enter basic information about their learning content and goals.

[0127] Step 2:

[0128] The device monitors students' facial expressions, tone of voice, and input speed in real time, and uses an emotion engine to analyze students' emotional states (joy, excitement, frustration, etc.).

[0129] Step 3:

[0130] The device sends the collected learning data and emotion data to the server. The data includes emotional states along with learning progress information.

[0131] Step 4:

[0132] The server analyzes learning data and emotional data using an AI model to understand the students' current situation. This analysis evaluates not only the students' strengths and weaknesses in subjects, but also their current motivation and stress levels.

[0133] Step 5:

[0134] Based on the analysis results, the server generates a personalized learning plan. This plan is adjusted according to the user's learning progress and understanding, incorporating stress-reducing exercises and positive reinforcement elements as needed.

[0135] Step 6:

[0136] The device presents the user (student) with a learning plan received from the server. The student can proceed with their studies according to the presented plan, and the device continuously monitors their emotional state throughout this time.

[0137] Step 7:

[0138] The server periodically reviews progress in both learning and emotional aspects and adjusts the learning plan as needed. For example, if a student's concentration is waning, it provides instructions such as recommending a short break.

[0139] Step 8:

[0140] The server generates a feedback report based on learning and emotional progress and sends it to parents or instructors. This report includes specific advice to improve the student's learning efficiency and information on emotional support.

[0141] Step 9:

[0142] Users (parents or teachers) can review the feedback reports they receive and adjust their teaching methods and support approaches for students. They can also use this information to improve communication with students, thereby enhancing educational effectiveness.

[0143] (Example 2)

[0144] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0145] Conventional learning support systems provide feedback based on learners' understanding and progress, but they lack optimization that takes into account changes in learners' emotions. As a result, they are unable to respond appropriately when learners experience stress or a decline in motivation, making it difficult to maximize learning effectiveness and maintain motivation.

[0146] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0147] In this invention, the server includes means for collecting individual learning information of learners, means for recognizing nonverbal responses as emotions in real time and extracting emotional information, and means for analyzing the collected learning information and emotional information. This makes it possible to provide an optimized learning plan that takes into account both the learner's learning progress and emotional state.

[0148] A "learner" is an individual who seeks to acquire knowledge and skills through specific educational activities.

[0149] "Individual learning information" refers to data that represents the progress and level of understanding a learner has achieved in a specific subject or topic.

[0150] "Nonverbal responses" refer to reactions that do not involve language, such as facial expressions, tone of voice, and body movements, which are used to understand emotions.

[0151] "Emotional information" refers to data that indicates the learner's emotional state, and is extracted based on real-time emotion recognition.

[0152] "Analyzing" refers to the process of evaluating collected data using statistical methods and machine learning algorithms to understand learners' learning tendencies and emotional states.

[0153] A "learning plan" refers to the content of learning activities and materials provided that are designed based on each learner's individual progress, level of understanding, and emotional state.

[0154] "Feedback" refers to guidance guidelines presented to parents and instructors based on an analysis of information regarding learners' progress and understanding.

[0155] The system of this invention collects and analyzes individual learning information and emotional information of learners, and provides an optimized learning plan based on this information. Specific embodiments of this system will be described below.

[0156] First, the user (learner) uses a device to input their learning progress and activities. The device has built-in sensors such as a camera and microphone, which capture nonverbal responses in real time. During this process, emotional information is collected by capturing changes in the learner's facial expressions and voice.

[0157] Next, the device sends the collected learning and sentiment information to the server. Data transmission is performed using the HTTPS protocol to maintain data confidentiality and integrity.

[0158] The server analyzes the received information using a generative AI model. This AI model combines statistical analysis algorithms and machine learning algorithms to evaluate the learner's learning tendencies and emotional state. This reveals the learner's current level of understanding and emotional state, and generates an individually optimized learning plan.

[0159] The device then provides the user with a generated learning plan. This plan includes personalized content that takes into account the learner's current progress and emotional state, thereby enhancing learning effectiveness.

[0160] For example, if a learner is unable to concentrate on a task, a device sensor can detect this and notify the server. The server can then provide a new plan suggesting an appropriate break.

[0161] An example of a prompt message would be: "Analyze the learner's learning and emotional data and generate a learning plan for the next week. If the learner lacks focus, include measures to address this." This allows the system to continuously and dynamically provide support tailored to the learner's state.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] Users (learners) input their learning progress and activities through the device. In addition, the device's sensors capture the learner's facial expressions and voice, collecting nonverbal responses as data. This input data includes information related to the learner's level of understanding and emotional state.

[0165] Step 2:

[0166] The device transmits collected learning and emotional information to the server. Secure communication protocols such as HTTPS are used to ensure data confidentiality during transmission. Input data includes specific learning progress and emotional changes of students, and this data is stored on the server in real time as output.

[0167] Step 3:

[0168] The server analyzes the received data using a generation AI model. The input training data and emotion data are processed by the AI ​​model to obtain evaluation results of the learner's learning tendencies and emotional state. This process allows for accurate real-time understanding of the learner's comprehension and emotional changes.

[0169] Step 4:

[0170] The server generates individually optimized learning plans based on the analysis results. The input is evaluation data output by the AI ​​model, and based on this, a learning plan is generated that includes tasks, materials, and emotionally sensitive content tailored to the learner's progress. For example, for a learner experiencing a specific emotional state, relaxation content to reduce stress will be selected.

[0171] Step 5:

[0172] The device delivers the generated learning plan to the user (learner). The learner can proceed with their learning according to the presented learning plan, while the device continuously monitors changes in their emotions using sensors. The input is the learning plan, and the output is the specific learning activities in which the learner puts that plan into practice.

[0173] Step 6:

[0174] The server generates and provides feedback reports to instructors and parents, combining learning and emotional progress. Inputs include the learner's learning history and emotional changes, and the server analyzes this data to output progress and guidance guidelines. Specifically, it visualizes areas requiring attention regarding understanding and emotional aspects, and suggests the next instructional steps.

[0175] (Application Example 2)

[0176] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0177] In student learning, it is crucial to provide individually optimized content and flexibly adjust plans to take into account students' emotional states. However, conventional technologies have not adequately provided dynamic adjustments based on individual learning progress and emotions, making it difficult to provide efficient learning while maintaining students' motivation. This invention aims to solve these problems, thereby maximizing learning effectiveness and providing a flexible learning environment that meets the needs of individual learners.

[0178] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0179] In this invention, the server includes means for collecting individual student learning data, means for analyzing the collected data and nonverbal responses in real time, means for generating individual student learning plans based on the analysis results, and means for providing the generated learning plans to users and continuously monitoring and dynamically adjusting the students' emotional changes. This makes it possible to maximize the learning effectiveness of students while providing appropriate feedback and content that responds to their emotions.

[0180] "Individual student learning data" refers to data based on each student's learning progress, level of understanding, and the content of the assignments they are working on.

[0181] "Nonverbal responses" refer to reactions that are conveyed through means other than language, such as a student's facial expressions, tone of voice, and posture.

[0182] "Methods for real-time analysis" refer to technical means that enable immediate analysis of data the moment it is collected, allowing for the immediate acquisition of results.

[0183] "Methods for generating learning plans" refer to methods for creating optimized learning content and schedules based on each student's individual learning data and emotional state.

[0184] "Means for continuously monitoring and dynamically adjusting emotional changes" refers to technical means for constantly monitoring students' emotional states and adjusting learning plans and materials on the spot.

[0185] "Maximizing learning effectiveness" means enabling students to learn efficiently and maximize their understanding and knowledge.

[0186] "Appropriate feedback" refers to providing information and advice that is deemed beneficial in order to enhance students' learning effectiveness.

[0187] "Providing content" means supplying students with materials that facilitate their learning, such as information and teaching materials necessary for studying.

[0188] In this invention, the user first begins learning using a device. The device is equipped with a camera and microphone, which collect the student's nonverbal responses in real time. The device sends this data to a server. The server uses an emotion recognition engine and an AI model to analyze the received learning data and emotion data. Specifically, the emotion recognition engine analyzes the student's facial expressions and voice to determine their emotional state.

[0189] The server uses an AI model to generate personalized learning plans based on collected data. These plans include optimal learning materials and methods, but are also instantly adjusted based on the student's emotional state. If a change in emotion is detected, new learning tasks or refreshing content are presented.

[0190] For example, if a user is working on a math problem and the camera detects signs of stress, the server will present a new practice problem and deliver an AI-generated message of encouragement to the student via their device. This allows the student to continue learning with peace of mind.

[0191] Furthermore, the server generates feedback reports that combine progress data and emotional data, and provides them to parents and educators. This report enables instruction based on students' learning progress and emotional fluctuations.

[0192] An example of a prompt message might be, "What kind of encouraging message would be effective if a student is having trouble concentrating on their studies?" This allows the system to provide encouragement that is optimized for the student.

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The user begins learning using a device. The device is equipped with a camera and microphone, and has the function of collecting the student's facial expressions and voice. At this stage, the input consists of the student's engagement with the learning content and audio and video data. The output is non-verbal response data.

[0196] Step 2:

[0197] The terminal sends the collected nonverbal response data from students to the server. This transmission takes place over the network, and the terminal sends facial expression data and voice data in real time as multiple data packets. The input requires the students' nonverbal response data, and the output is the completion of the data transfer to the server.

[0198] Step 3:

[0199] The server analyzes the received nonverbal response data in real time. Using an emotion recognition engine, it determines the student's emotional state from the input facial expressions and voice. This step yields the emotional state (e.g., joy, stress) as output.

[0200] Step 4:

[0201] The server uses emotion recognition results and training data as input to generate an individually optimized learning plan using an AI model. The AI ​​model performs data calculations to determine the optimal learning materials and pace based on the student's current level of understanding and emotional state. The output is an individualized learning plan.

[0202] Step 5:

[0203] The generated learning plan is provided to the device and presented to the user. The device displays this plan on its screen and provides audio notifications as needed. The input is the learning plan, and the output is the presentation of the plan.

[0204] Step 6:

[0205] As the user continues learning, the device continuously monitors changes in emotions and resends data to the server as needed. Dynamic adjustments are also made on the server based on these changes in emotions. The input for this step is continuously collected emotion data, and the output is an updated learning plan.

[0206] Step 7:

[0207] The server generates feedback reports based on learning and emotional progress data and provides them to parents or educators. Inputs include accumulated progress and emotional data, and the output is a comprehensive feedback report. This feedback is sent via email or other means.

[0208] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0209] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0210] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0214] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0215] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0216] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0217] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0218] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0219] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0220] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0221] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0222] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0223] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0224] The system for implementing this invention collects individual student learning data and generates and provides an optimal learning plan based on that data. This system is broadly composed of five functions: data collection, data analysis, learning plan generation, progress monitoring, and feedback provision. These functions work closely together to provide a seamless learning environment.

[0225] First, the user (student) uses a device to input daily learning data. This is often done on an online platform, and the input data includes homework progress, test scores, and time spent studying. The device sends this data to the server in real time.

[0226] Next, the server analyzes each student's current learning progress based on the collected data. An AI model processes this information to identify each student's strengths and areas of weakness. Based on this analysis, the server generates a customized learning plan. This plan includes the necessary materials and assignments, what to focus on next, and appropriate test preparation strategies.

[0227] The generated learning plan is provided to the user (student) via their device. This allows students to learn at their own pace. At the same time, the server periodically monitors the results of applying the learning plan and the students' progress, enabling immediate adjustments to the learning plan.

[0228] Furthermore, after a certain period of time, the server summarizes the progress of learning outcomes and generates a detailed feedback report. This feedback is provided to users (teachers and parents), allowing them to improve teaching methods as needed. This feedback clearly explains students' strengths and areas for improvement, making it extremely useful in actual educational activities.

[0229] As a concrete example, let's consider a high school student who struggles with mechanics in physics. The server analyzes the student's previous test results and past learning history to generate a learning plan that focuses on the fundamental concepts of mechanics. This plan includes interactive video materials and practice problems, allowing the student to deepen their understanding efficiently. The server also tracks progress as the student makes progress and updates the plan when sufficient results are seen.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] Users (students) use a learning app on their device to input data about their daily learning activities and progress. This data includes homework completion status, test results, content of lessons attended, and time spent studying.

[0233] Step 2:

[0234] The device receives the learning data entered by the user and prepares to send it to the server. The data is configured to be sent to the server in real time.

[0235] Step 3:

[0236] The server receives the training data sent from the terminal. The received data is stored in the database and simultaneously checked for any defects in data integrity or format.

[0237] Step 4:

[0238] The server uses AI models to analyze students' learning progress based on data stored in the database. This analysis identifies students' learning progress, level of understanding, and areas where they struggle.

[0239] Step 5:

[0240] Based on the analysis results, the server generates a learning plan optimized for the student's needs. The plan includes recommended materials, homework assignments, and next tasks to tackle.

[0241] Step 6:

[0242] The device presents the user (student) with a learning plan sent from the server. This allows the student to proceed with their learning activities based on the new plan.

[0243] Step 7:

[0244] The server periodically monitors the user's learning progress and re-evaluates the plan based on the analysis results. It updates the learning plan as needed.

[0245] Step 8:

[0246] The server generates a feedback report based on the student's learning progress and provides it to the parent or teacher.

[0247] Step 9:

[0248] Users (parents or teachers) can review feedback reports received from the server and adjust student guidance strategies. They can also provide direct feedback to students, offering encouragement and suggesting areas for improvement.

[0249] (Example 1)

[0250] Next, we will describe Example 1. 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."

[0251] In today's educational environment, there is a demand for individualized instruction tailored to each student's learning characteristics. However, it is difficult for teachers and parents to accurately grasp students' progress and understanding, and to provide effective instruction as needed. Furthermore, there is a lack of methods for creating appropriate learning plans and dynamically adjusting them while regularly monitoring their progress. In conventional systems, handling student data and updating plans is often done manually, which is time-consuming and labor-intensive, making effective individualized instruction difficult.

[0252] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0253] In this invention, the server includes means for collecting individual student learning information, means for processing the collected information in real time, and means for creating individualized learning plans for students based on the processing results. This enables the automatic generation and monitoring of learning plans tailored to each student. The system also utilizes a generation AI model to flexibly adjust plans according to the student's progress and level of understanding, providing efficient and effective learning support.

[0254] "Individualized student learning information" refers to specific data about each student, such as their educational progress, grades, level of understanding, study time, and progress on assignments.

[0255] "Means of collection" refers to systems or processes for automatically or manually collecting learning information entered by users.

[0256] "Real-time processing methods" refer to technical mechanisms for instantly analyzing collected information and providing rapid feedback or results based on that data.

[0257] "Methods for creating educational plans" refers to the process of designing a learning plan that takes into account the content, materials, assignments, and test preparation that students should learn, based on their individual learning information.

[0258] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to analyze data and automatically create and update the optimal learning strategy and plan for the user.

[0259] "Monitoring" is an oversight activity that involves regularly tracking students' learning progress and understanding, and modifying the educational plan as needed.

[0260] "Means of providing advice" refer to communication methods that effectively convey the generated feedback to parents and educators and help improve educational policies.

[0261] The system for implementing this invention aims to provide individualized education tailored to the learning characteristics of each student. This system functions primarily through interaction between a server, terminals, and users, and is implemented in the following manner.

[0262] Users (students) input daily learning information via a device. This input includes homework progress, test scores, and time spent studying. The entered information is transmitted from the device to the server in real time. The devices used here are compatible with a variety of devices, including PCs, tablets, and smartphones. To ensure data security, secure protocols such as SSL are used for communication.

[0263] The server stores the received data and analyzes it in real time using an AI model. The AI ​​model utilizes supervised learning and generative AI models to identify students' strengths and areas for improvement. Based on this analysis, the server generates a customized learning plan. This plan includes learning materials, assignments, and test preparation strategies. For example, for a student struggling with mechanics in physics, a learning plan focused on fundamental mechanics concepts is created, providing interactive video materials and practice problems.

[0264] The generated educational plans are provided to users via a terminal. The terminal's interface is designed for intuitive operation, enabling users to learn effectively according to the learning plan. The server further uses a generating AI model to monitor students' progress and understanding, and updates the learning plan as needed. In addition, regular monitoring generates feedback reports on educational progress and outcomes, which are provided to parents and educators, thereby improving the quality of individualized instruction.

[0265] A concrete example of a prompt message could be, "Based on this student's physics learning history, generate a customized learning plan focused on mechanics," which could be input into the generating AI model.

[0266] In this way, the system of this invention can provide students with an optimal learning environment and significantly improve the efficiency and effectiveness of educational activities.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] Users (students) input learning information using a terminal. This information includes homework progress, test scores, and study time. This data is entered through the terminal's interface, converted to a data format, and then sent to the server. Input data formats include numerical data, text data, and time data.

[0270] Step 2:

[0271] The terminal transmits the entered learning information to the server in real time. This process utilizes secure protocols such as SSL to ensure data security. During transmission, the data is divided into data packets, which are then reconstructed on the server side. The transmitted data is then ready to be registered in the database.

[0272] Step 3:

[0273] The server stores the received training data in a database. During storage, data integrity checks and formatting are performed. This stored data is then used as foundational information for AI analysis.

[0274] Step 4:

[0275] The server inputs the stored data into an AI model and performs data analysis. The AI ​​model uses supervised learning and performs data mining to recognize students' strengths and weaknesses. This analysis determines their level of understanding in specific subjects and skills. The output provides an evaluation of each student's level of understanding.

[0276] Step 5:

[0277] The server uses a generated AI model based on the analysis results of the AI ​​model to generate an optimized learning plan for the student. The generated plan includes specific teaching materials, assignments, and test preparation strategies. This plan generation process uses prompts to define a plan that suits the student. An example of a prompt might be, "Based on this student's physics learning history, generate a learning plan that focuses on mechanics."

[0278] Step 6:

[0279] The generated educational plans are provided to users via a terminal. The terminal screen displays the plan's contents in detail, helping students effectively progress through their studies according to its content. An interface is provided that allows users to access the information they need.

[0280] Step 7:

[0281] The server regularly monitors users' learning progress and re-analyzes it whenever new data is entered. This allows for timely updates of the educational plan through the generative AI model. Progress feedback reports are generated regularly and sent to parents and educators.

[0282] (Application Example 1)

[0283] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0284] Health management for users including the elderly has the problem that it is difficult to provide a customized care plan according to individual conditions. In the conventional system, due to a uniform approach, it is impossible to appropriately respond to individual health conditions and lifestyles, and there is a problem that efficient health maintenance is difficult. The purpose of the present invention is to improve individual health maintenance and the quality of life by providing an individual care plan tailored to each user.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0286] In this invention, the server includes means for collecting individual health data of users, means for analyzing the collected health data in real time, and means for generating an individual care plan based on the analysis results. As a result, it becomes possible to generate and provide a care plan specialized for the health state of the user.

[0287] A "user" is a person who receives the service and refers to the target for which health data is collected in this system.

[0288] "Health data" is a collection of information regarding the physical state and lifestyle of the user, and this is used for creating a care plan.

[0289] "Means for analyzing in real time" refers to a technology for immediately processing the collected health data and quickly grasping the current state of the user.

[0290] "Individual care plan" refers to a plan including customized care guidance based on the health state and lifestyle of the user.

[0291] "Monitoring the progress" refers to continuously recording and evaluating the reaction of the user to the care plan and changes in the health state.

[0292] "Feedback" refers to evaluations and advice provided to users, their guardians, and instructors, and is information that can be used to adjust care plans.

[0293] The system that implements this application example implements a program to individually manage users' health data and provide optimal care plans. The server plays a central role, processing the collected data and performing real-time analysis. Specifically, the hardware consists of terminals used by users (smartphones or care robots), which transmit health data to the server. The server receives this data and analyzes the user's health status using an AI model implemented in Python.

[0294] Based on the analysis results, the server automatically generates individualized care plans and provides them to users via their terminals. These care plans include personalized dietary suggestions and exercise advice tailored to each user's health condition, contributing to improvements in their daily lives. The server also periodically monitors the user's progress and generates feedback. This feedback monitors changes in the user's condition and the effectiveness of the plan in real time, suggesting the next steps to take.

[0295] As a concrete example, to improve the cardiac health of elderly individuals, a suitable walking plan may be created based on daily heart rate data. The server analyzes changes in heart rate and advises on appropriate exercise intensity and frequency, helping users maintain their cardiac health. An example of a prompt in this case would be, "Generate an exercise plan effective for cardiac health based on current heart rate data." In this way, the system provides optimal care for each user, supporting the maintenance and improvement of their health.

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The device collects the user's daily activity and health data. This data includes heart rate, steps taken, and meal records. The device periodically sends this data to the server. Input is the data recorded by the user, and output is the data sent to the server.

[0299] Step 2:

[0300] The server analyzes data received from the terminal in real time. Here, an AI model implemented in Python processes the data and extracts parameters for evaluating the user's health status. The input is raw data received from the terminal, and the output is analyzed health assessment information. Specifically, it calculates the average and variability of the obtained heart rate and generates health status indicators.

[0301] Step 3:

[0302] The server uses a generative AI model to create an individualized care plan based on health assessment information. The input is the assessment information obtained in step 2, and the output is a care plan that includes actions recommended for the user. Specifically, it selects and plans exercise and dietary content appropriate for the user. Prompt messages are also generated in this step, with instructions such as "Create the optimal exercise plan from the current data" being generated by the generative AI model.

[0303] Step 4:

[0304] The terminal presents the care plan received from the server to the user. The input is the care plan sent from the server, and the output is the display to the user. The user can adjust their daily life based on this. Specifically, the terminal displays the plan contents using alerts and notifications to make it easy for the user to understand.

[0305] Step 5:

[0306] The server monitors data updates from the terminal and continuously monitors the progress of the user. The input is newly acquired health data, and the output is feedback based on the progress status. As a specific operation, it evaluates whether the progress is proceeding as planned and prepares to adjust the plan if necessary. This feedback is ultimately returned to the user.

[0307] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0308] This invention is a system that not only collects and analyzes individual learning data of students but also incorporates an emotion engine that recognizes the emotions of students in real time to further optimize the learning plan. The system consists of main functions such as data collection, emotion analysis, learning plan generation, progress monitoring, and feedback provision.

[0309] First, the user (student) uses the terminal to input their learning progress and activity content. In this process, the terminal may be equipped with sensors for recognizing the student's non-verbal reactions (e.g., changes in expression and voice), and thereby emotion data is collected.

[0310] The terminal transmits the collected learning data and emotion data to the server. This data is designed to be accumulated on the server in real time.

[0311] The server analyzes the received learning data and emotion data of the student using an AI model. By combining the learning data and emotion data, it is possible to grasp not only the current understanding level of the student but also the emotional state such as the decline in learning stress and motivation.

[0312] Based on these analysis results, the server generates an individually optimized learning plan. This plan may include assignments tailored to the student's learning progress, learning materials to enhance optimal learning, and possibly encouraging and relaxing content that matches the student's emotional state.

[0313] The generated learning plan is provided to the user via the device. Students proceed with their studies based on this plan, while the device continuously monitors changes in the student's emotions and dynamically adjusts the plan as needed.

[0314] Furthermore, the server generates a feedback report that combines learning and emotional progress, and provides this to parents or teachers. This feedback includes not only the student's learning progress but also guidance that takes into account emotional support.

[0315] As a concrete example, when an elementary school student is working on a math problem and encounters a difficult one, the emotion engine recognizes the student's frustration. Based on this data, the server adjusts the difficulty of the problem and generates a new assignment with appropriate explanations. In this way, the system maximizes the effectiveness of student learning while maintaining their motivation to learn.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The user (student) launches the learning app and logs into their device to begin their daily learning activities. At this point, they enter basic information about their learning content and goals.

[0319] Step 2:

[0320] The device monitors students' facial expressions, tone of voice, and input speed in real time, and uses an emotion engine to analyze students' emotional states (joy, excitement, frustration, etc.).

[0321] Step 3:

[0322] The device sends the collected learning data and emotion data to the server. The data includes emotional states along with learning progress information.

[0323] Step 4:

[0324] The server analyzes learning data and emotional data using an AI model to understand the students' current situation. This analysis evaluates not only the students' strengths and weaknesses in subjects, but also their current motivation and stress levels.

[0325] Step 5:

[0326] Based on the analysis results, the server generates a personalized learning plan. This plan is adjusted according to the user's learning progress and understanding, incorporating stress-reducing exercises and positive reinforcement elements as needed.

[0327] Step 6:

[0328] The device presents the user (student) with a learning plan received from the server. The student can proceed with their studies according to the presented plan, and the device continuously monitors their emotional state throughout this time.

[0329] Step 7:

[0330] The server periodically reviews progress in both learning and emotional aspects and adjusts the learning plan as needed. For example, if a student's concentration is waning, it provides instructions such as recommending a short break.

[0331] Step 8:

[0332] The server generates a feedback report based on learning and emotional progress and sends it to parents or instructors. This report includes specific advice to improve the student's learning efficiency and information on emotional support.

[0333] Step 9:

[0334] Users (parents or teachers) can review the feedback reports they receive and adjust their teaching methods and support approaches for students. They can also use this information to improve communication with students, thereby enhancing educational effectiveness.

[0335] (Example 2)

[0336] Next, we will describe Example 2. 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".

[0337] Conventional learning support systems provide feedback based on learners' understanding and progress, but they lack optimization that takes into account changes in learners' emotions. As a result, they are unable to respond appropriately when learners experience stress or a decline in motivation, making it difficult to maximize learning effectiveness and maintain motivation.

[0338] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0339] In this invention, the server includes means for collecting individual learning information of learners, means for recognizing nonverbal responses as emotions in real time and extracting emotional information, and means for analyzing the collected learning information and emotional information. This makes it possible to provide an optimized learning plan that takes into account both the learner's learning progress and emotional state.

[0340] A "learner" is an individual who seeks to acquire knowledge and skills through specific educational activities.

[0341] "Individual learning information" refers to data that represents the progress and level of understanding a learner has achieved in a specific subject or topic.

[0342] "Nonverbal responses" refer to reactions that do not involve language, such as facial expressions, tone of voice, and body movements, which are used to understand emotions.

[0343] "Emotional information" refers to data that indicates the learner's emotional state, and is extracted based on real-time emotion recognition.

[0344] "Analyzing" refers to the process of evaluating collected data using statistical methods and machine learning algorithms to understand learners' learning tendencies and emotional states.

[0345] A "learning plan" refers to the content of learning activities and materials provided that are designed based on each learner's individual progress, level of understanding, and emotional state.

[0346] "Feedback" refers to guidance guidelines presented to parents and instructors based on an analysis of information regarding learners' progress and understanding.

[0347] The system of this invention collects and analyzes individual learning information and emotional information of learners, and provides an optimized learning plan based on this information. Specific embodiments of this system will be described below.

[0348] First, the user (learner) uses a device to input their learning progress and activities. The device has built-in sensors such as a camera and microphone, which capture nonverbal responses in real time. During this process, emotional information is collected by capturing changes in the learner's facial expressions and voice.

[0349] Next, the device sends the collected learning and sentiment information to the server. Data transmission is performed using the HTTPS protocol to maintain data confidentiality and integrity.

[0350] The server analyzes the received information using a generative AI model. This AI model combines statistical analysis algorithms and machine learning algorithms to evaluate the learner's learning tendencies and emotional state. This reveals the learner's current level of understanding and emotional state, and generates an individually optimized learning plan.

[0351] The device then provides the user with a generated learning plan. This plan includes personalized content that takes into account the learner's current progress and emotional state, thereby enhancing learning effectiveness.

[0352] For example, if a learner is unable to concentrate on a task, a device sensor can detect this and notify the server. The server can then provide a new plan suggesting an appropriate break.

[0353] An example of a prompt message would be: "Analyze the learner's learning and emotional data and generate a learning plan for the next week. If the learner lacks focus, include measures to address this." This allows the system to continuously and dynamically provide support tailored to the learner's state.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] Users (learners) input their learning progress and activities through the device. In addition, the device's sensors capture the learner's facial expressions and voice, collecting nonverbal responses as data. This input data includes information related to the learner's level of understanding and emotional state.

[0357] Step 2:

[0358] The device transmits collected learning and emotional information to the server. Secure communication protocols such as HTTPS are used to ensure data confidentiality during transmission. Input data includes specific learning progress and emotional changes of students, and this data is stored on the server in real time as output.

[0359] Step 3:

[0360] The server analyzes the received data using a generation AI model. The input training data and emotion data are processed by the AI ​​model to obtain evaluation results of the learner's learning tendencies and emotional state. This process allows for accurate real-time understanding of the learner's comprehension and emotional changes.

[0361] Step 4:

[0362] The server generates individually optimized learning plans based on the analysis results. The input is evaluation data output by the AI ​​model, and based on this, a learning plan is generated that includes tasks, materials, and emotionally sensitive content tailored to the learner's progress. For example, for a learner experiencing a specific emotional state, relaxation content to reduce stress will be selected.

[0363] Step 5:

[0364] The device delivers the generated learning plan to the user (learner). The learner can proceed with their learning according to the presented learning plan, while the device continuously monitors changes in their emotions using sensors. The input is the learning plan, and the output is the specific learning activities in which the learner puts that plan into practice.

[0365] Step 6:

[0366] The server generates and provides feedback reports to instructors and parents, combining learning and emotional progress. Inputs include the learner's learning history and emotional changes, and the server analyzes this data to output progress and guidance guidelines. Specifically, it visualizes areas requiring attention regarding understanding and emotional aspects, and suggests the next instructional steps.

[0367] (Application Example 2)

[0368] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0369] In student learning, it is crucial to provide individually optimized content and flexibly adjust plans to take into account students' emotional states. However, conventional technologies have not adequately provided dynamic adjustments based on individual learning progress and emotions, making it difficult to provide efficient learning while maintaining students' motivation. This invention aims to solve these problems, thereby maximizing learning effectiveness and providing a flexible learning environment that meets the needs of individual learners.

[0370] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0371] In this invention, the server includes means for collecting individual student learning data, means for analyzing the collected data and nonverbal responses in real time, means for generating individual student learning plans based on the analysis results, and means for providing the generated learning plans to users and continuously monitoring and dynamically adjusting the students' emotional changes. This makes it possible to maximize the learning effectiveness of students while providing appropriate feedback and content that responds to their emotions.

[0372] "Individual student learning data" refers to data based on each student's learning progress, level of understanding, and the content of the assignments they are working on.

[0373] "Nonverbal responses" refer to reactions that are conveyed through means other than language, such as a student's facial expressions, tone of voice, and posture.

[0374] "Methods for real-time analysis" refer to technical means that enable immediate analysis of data the moment it is collected, allowing for the immediate acquisition of results.

[0375] "Methods for generating learning plans" refer to methods for creating optimized learning content and schedules based on each student's individual learning data and emotional state.

[0376] "Means for continuously monitoring and dynamically adjusting emotional changes" refers to technical means for constantly monitoring students' emotional states and adjusting learning plans and materials on the spot.

[0377] "Maximizing learning effectiveness" means enabling students to learn efficiently and maximize their understanding and knowledge.

[0378] "Appropriate feedback" refers to providing information and advice that is deemed beneficial in order to enhance students' learning effectiveness.

[0379] "Providing content" means supplying students with materials that facilitate their learning, such as information and teaching materials necessary for studying.

[0380] In this invention, the user first begins learning using a device. The device is equipped with a camera and microphone, which collect the student's nonverbal responses in real time. The device sends this data to a server. The server uses an emotion recognition engine and an AI model to analyze the received learning data and emotion data. Specifically, the emotion recognition engine analyzes the student's facial expressions and voice to determine their emotional state.

[0381] The server uses an AI model to generate personalized learning plans based on collected data. These plans include optimal learning materials and methods, but are also instantly adjusted based on the student's emotional state. If a change in emotion is detected, new learning tasks or refreshing content are presented.

[0382] For example, if a user is working on a math problem and the camera detects signs of stress, the server will present a new practice problem and deliver an AI-generated message of encouragement to the student via their device. This allows the student to continue learning with peace of mind.

[0383] Furthermore, the server generates feedback reports that combine progress data and emotional data, and provides them to parents and educators. This report enables instruction based on students' learning progress and emotional fluctuations.

[0384] An example of a prompt message might be, "What kind of encouraging message would be effective if a student is having trouble concentrating on their studies?" This allows the system to provide encouragement that is optimized for the student.

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The user begins learning using a device. The device is equipped with a camera and microphone, and has the function of collecting the student's facial expressions and voice. At this stage, the input consists of the student's engagement with the learning content and audio and video data. The output is non-verbal response data.

[0388] Step 2:

[0389] The terminal sends the collected nonverbal response data from students to the server. This transmission takes place over the network, and the terminal sends facial expression data and voice data in real time as multiple data packets. The input requires the students' nonverbal response data, and the output is the completion of the data transfer to the server.

[0390] Step 3:

[0391] The server analyzes the received nonverbal response data in real time. Using an emotion recognition engine, it determines the student's emotional state from the input facial expressions and voice. This step yields the emotional state (e.g., joy, stress) as output.

[0392] Step 4:

[0393] The server uses emotion recognition results and training data as input to generate an individually optimized learning plan using an AI model. The AI ​​model performs data calculations to determine the optimal learning materials and pace based on the student's current level of understanding and emotional state. The output is an individualized learning plan.

[0394] Step 5:

[0395] The generated learning plan is provided to the device and presented to the user. The device displays this plan on its screen and provides audio notifications as needed. The input is the learning plan, and the output is the presentation of the plan.

[0396] Step 6:

[0397] As the user continues learning, the device continuously monitors changes in emotions and resends data to the server as needed. Dynamic adjustments are also made on the server based on these changes in emotions. The input for this step is continuously collected emotion data, and the output is an updated learning plan.

[0398] Step 7:

[0399] The server generates feedback reports based on learning and emotional progress data and provides them to parents or educators. Inputs include accumulated progress and emotional data, and the output is a comprehensive feedback report. This feedback is sent via email or other means.

[0400] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0401] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0402] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0403] [Third Embodiment]

[0404] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0405] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0406] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0407] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0408] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0409] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0410] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0411] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0412] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0413] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0414] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0415] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0416] The system for implementing this invention collects individual student learning data and generates and provides an optimal learning plan based on that data. This system is broadly composed of five functions: data collection, data analysis, learning plan generation, progress monitoring, and feedback provision. These functions work closely together to provide a seamless learning environment.

[0417] First, the user (student) uses a device to input daily learning data. This is often done on an online platform, and the input data includes homework progress, test scores, and time spent studying. The device sends this data to the server in real time.

[0418] Next, the server analyzes each student's current learning progress based on the collected data. An AI model processes this information to identify each student's strengths and areas of weakness. Based on this analysis, the server generates a customized learning plan. This plan includes the necessary materials and assignments, what to focus on next, and appropriate test preparation strategies.

[0419] The generated learning plan is provided to the user (student) via their device. This allows students to learn at their own pace. At the same time, the server periodically monitors the results of applying the learning plan and the students' progress, enabling immediate adjustments to the learning plan.

[0420] Furthermore, after a certain period of time, the server summarizes the progress of learning outcomes and generates a detailed feedback report. This feedback is provided to users (teachers and parents), allowing them to improve teaching methods as needed. This feedback clearly explains students' strengths and areas for improvement, making it extremely useful in actual educational activities.

[0421] As a concrete example, let's consider a high school student who struggles with mechanics in physics. The server analyzes the student's previous test results and past learning history to generate a learning plan that focuses on the fundamental concepts of mechanics. This plan includes interactive video materials and practice problems, allowing the student to deepen their understanding efficiently. The server also tracks progress as the student makes progress and updates the plan when sufficient results are seen.

[0422] The following describes the processing flow.

[0423] Step 1:

[0424] Users (students) use a learning app on their device to input data about their daily learning activities and progress. This data includes homework completion status, test results, content of lessons attended, and time spent studying.

[0425] Step 2:

[0426] The device receives the learning data entered by the user and prepares to send it to the server. The data is configured to be sent to the server in real time.

[0427] Step 3:

[0428] The server receives the training data sent from the terminal. The received data is stored in the database and simultaneously checked for any defects in data integrity or format.

[0429] Step 4:

[0430] The server uses AI models to analyze students' learning progress based on data stored in the database. This analysis identifies students' learning progress, level of understanding, and areas where they struggle.

[0431] Step 5:

[0432] Based on the analysis results, the server generates a learning plan optimized for the student's needs. The plan includes recommended materials, homework assignments, and next tasks to tackle.

[0433] Step 6:

[0434] The device presents the user (student) with a learning plan sent from the server. This allows the student to proceed with their learning activities based on the new plan.

[0435] Step 7:

[0436] The server periodically monitors the user's learning progress and re-evaluates the plan based on the analysis results. It updates the learning plan as needed.

[0437] Step 8:

[0438] The server generates a feedback report based on the student's learning progress and provides it to the parent or teacher.

[0439] Step 9:

[0440] Users (parents or teachers) can review feedback reports received from the server and adjust student guidance strategies. They can also provide direct feedback to students, offering encouragement and suggesting areas for improvement.

[0441] (Example 1)

[0442] Next, we will describe Example 1. 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."

[0443] In today's educational environment, there is a demand for individualized instruction tailored to each student's learning characteristics. However, it is difficult for teachers and parents to accurately grasp students' progress and understanding, and to provide effective instruction as needed. Furthermore, there is a lack of methods for creating appropriate learning plans and dynamically adjusting them while regularly monitoring their progress. In conventional systems, handling student data and updating plans is often done manually, which is time-consuming and labor-intensive, making effective individualized instruction difficult.

[0444] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0445] In this invention, the server includes means for collecting individual student learning information, means for processing the collected information in real time, and means for creating individualized learning plans for students based on the processing results. This enables the automatic generation and monitoring of learning plans tailored to each student. The system also utilizes a generation AI model to flexibly adjust plans according to the student's progress and level of understanding, providing efficient and effective learning support.

[0446] "Individualized student learning information" refers to specific data about each student, such as their educational progress, grades, level of understanding, study time, and progress on assignments.

[0447] "Means of collection" refers to systems or processes for automatically or manually collecting learning information entered by users.

[0448] "Real-time processing methods" refer to technical mechanisms for instantly analyzing collected information and providing rapid feedback or results based on that data.

[0449] "Methods for creating educational plans" refers to the process of designing a learning plan that takes into account the content, materials, assignments, and test preparation that students should learn, based on their individual learning information.

[0450] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to analyze data and automatically create and update the optimal learning strategy and plan for the user.

[0451] "Monitoring" is an oversight activity that involves regularly tracking students' learning progress and understanding, and modifying the educational plan as needed.

[0452] "Means of providing advice" refer to communication methods that effectively convey the generated feedback to parents and educators and help improve educational policies.

[0453] The system for implementing this invention aims to provide individualized education tailored to the learning characteristics of each student. This system functions primarily through interaction between a server, terminals, and users, and is implemented in the following manner.

[0454] Users (students) input daily learning information via a device. This input includes homework progress, test scores, and time spent studying. The entered information is transmitted from the device to the server in real time. The devices used here are compatible with a variety of devices, including PCs, tablets, and smartphones. To ensure data security, secure protocols such as SSL are used for communication.

[0455] The server stores the received data and analyzes it in real time using an AI model. The AI ​​model utilizes supervised learning and generative AI models to identify students' strengths and areas for improvement. Based on this analysis, the server generates a customized learning plan. This plan includes learning materials, assignments, and test preparation strategies. For example, for a student struggling with mechanics in physics, a learning plan focused on fundamental mechanics concepts is created, providing interactive video materials and practice problems.

[0456] The generated educational plans are provided to users via a terminal. The terminal's interface is designed for intuitive operation, enabling users to learn effectively according to the learning plan. The server further uses a generating AI model to monitor students' progress and understanding, and updates the learning plan as needed. In addition, regular monitoring generates feedback reports on educational progress and outcomes, which are provided to parents and educators, thereby improving the quality of individualized instruction.

[0457] A concrete example of a prompt message could be, "Based on this student's physics learning history, generate a customized learning plan focused on mechanics," which could be input into the generating AI model.

[0458] In this way, the system of this invention can provide students with an optimal learning environment and significantly improve the efficiency and effectiveness of educational activities.

[0459] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0460] Step 1:

[0461] Users (students) input learning information using a terminal. This information includes homework progress, test scores, and study time. This data is entered through the terminal's interface, converted to a data format, and then sent to the server. Input data formats include numerical data, text data, and time data.

[0462] Step 2:

[0463] The terminal transmits the entered learning information to the server in real time. This process utilizes secure protocols such as SSL to ensure data security. During transmission, the data is divided into data packets, which are then reconstructed on the server side. The transmitted data is then ready to be registered in the database.

[0464] Step 3:

[0465] The server stores the received training data in a database. During storage, data integrity checks and formatting are performed. This stored data is then used as foundational information for AI analysis.

[0466] Step 4:

[0467] The server inputs the stored data into an AI model and performs data analysis. The AI ​​model uses supervised learning and performs data mining to recognize students' strengths and weaknesses. This analysis determines their level of understanding in specific subjects and skills. The output provides an evaluation of each student's level of understanding.

[0468] Step 5:

[0469] The server uses a generated AI model based on the analysis results of the AI ​​model to generate an optimized learning plan for the student. The generated plan includes specific teaching materials, assignments, and test preparation strategies. This plan generation process uses prompts to define a plan that suits the student. An example of a prompt might be, "Based on this student's physics learning history, generate a learning plan that focuses on mechanics."

[0470] Step 6:

[0471] The generated educational plans are provided to users via a terminal. The terminal screen displays the plan's contents in detail, helping students effectively progress through their studies according to its content. An interface is provided that allows users to access the information they need.

[0472] Step 7:

[0473] The server regularly monitors users' learning progress and re-analyzes it whenever new data is entered. This allows for timely updates of the educational plan through the generative AI model. Progress feedback reports are generated regularly and sent to parents and educators.

[0474] (Application Example 1)

[0475] Next, we will explain Application Example 1. In the following explanation, 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."

[0476] Managing the health of users, including the elderly, presents a challenge in providing customized care plans tailored to individual needs. Conventional systems rely on a uniform approach, failing to adequately address individual health conditions and lifestyles, making efficient health maintenance difficult. This invention aims to improve individual health maintenance and quality of life by providing personalized care plans tailored to each user.

[0477] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0478] In this invention, the server includes means for collecting individual health data of users, means for analyzing the collected health data in real time, and means for generating individual care plans based on the analysis results. This makes it possible to generate and provide care plans tailored to the user's health condition.

[0479] "User" refers to the person receiving the service, and is the subject from whom health data is collected in this system.

[0480] "Health data" refers to information collected about the user's physical condition and lifestyle, which is used to create care plans.

[0481] "Methods for real-time analysis" refer to technologies that process collected health data immediately and quickly grasp the user's current condition.

[0482] An "individualized care plan" refers to a plan that includes customized care guidance based on the user's health condition and lifestyle.

[0483] "Monitoring progress" refers to continuously recording and evaluating the user's response to the care plan and changes in their health condition.

[0484] "Feedback" refers to evaluations and advice provided to users, their guardians, and instructors, and is information that can be used to adjust care plans.

[0485] The system that implements this application example implements a program to individually manage users' health data and provide optimal care plans. The server plays a central role, processing the collected data and performing real-time analysis. Specifically, the hardware consists of terminals used by users (smartphones or care robots), which transmit health data to the server. The server receives this data and analyzes the user's health status using an AI model implemented in Python.

[0486] Based on the analysis results, the server automatically generates individualized care plans and provides them to users via their terminals. These care plans include personalized dietary suggestions and exercise advice tailored to each user's health condition, contributing to improvements in their daily lives. The server also periodically monitors the user's progress and generates feedback. This feedback monitors changes in the user's condition and the effectiveness of the plan in real time, suggesting the next steps to take.

[0487] As a concrete example, to improve the cardiac health of elderly individuals, a suitable walking plan may be created based on daily heart rate data. The server analyzes changes in heart rate and advises on appropriate exercise intensity and frequency, helping users maintain their cardiac health. An example of a prompt in this case would be, "Generate an exercise plan effective for cardiac health based on current heart rate data." In this way, the system provides optimal care for each user, supporting the maintenance and improvement of their health.

[0488] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0489] Step 1:

[0490] The device collects the user's daily activity and health data. This data includes heart rate, steps taken, and meal records. The device periodically sends this data to the server. Input is the data recorded by the user, and output is the data sent to the server.

[0491] Step 2:

[0492] The server analyzes data received from the terminal in real time. Here, an AI model implemented in Python processes the data and extracts parameters for evaluating the user's health status. The input is raw data received from the terminal, and the output is analyzed health assessment information. Specifically, it calculates the average and variability of the obtained heart rate and generates health status indicators.

[0493] Step 3:

[0494] The server uses a generative AI model to create an individualized care plan based on health assessment information. The input is the assessment information obtained in step 2, and the output is a care plan that includes actions recommended for the user. Specifically, it selects and plans exercise and dietary content appropriate for the user. Prompt messages are also generated in this step, with instructions such as "Create the optimal exercise plan from the current data" being generated by the generative AI model.

[0495] Step 4:

[0496] The terminal presents the care plan received from the server to the user. The input is the care plan sent from the server, and the output is the display to the user. The user can adjust their daily life based on this. Specifically, the terminal displays the plan contents using alerts and notifications to make it easy for the user to understand.

[0497] Step 5:

[0498] The server monitors data updates from the terminal and continuously monitors the user's progress. The input is newly acquired health data, and the output is feedback based on the progress. Specifically, it evaluates whether progress is proceeding according to plan and prepares to adjust the plan as needed. This feedback is ultimately returned to the user.

[0499] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0500] This invention is a system that not only collects and analyzes individual student learning data, but also incorporates an emotion engine that recognizes students' emotions in real time, thereby further optimizing learning plans. The system consists of three main functions: data collection, emotion analysis, learning plan generation, progress monitoring, and feedback provision.

[0501] First, the user (student) uses a device to input their learning progress and activities. In this process, the device may be equipped with sensors to recognize the student's nonverbal responses (e.g., changes in facial expressions or voice), thereby collecting emotional data.

[0502] The device sends collected learning data and sentiment data to the server. This data is designed to be stored on the server in real time.

[0503] The server uses an AI model to analyze the student's learning data and emotional data that it receives. By combining the learning data and emotional data, it is possible to understand not only the student's current level of comprehension but also their emotional state, such as learning stress or decreased motivation.

[0504] Based on these analysis results, the server generates an individually optimized learning plan. This plan may include assignments tailored to the student's learning progress, learning materials to enhance optimal learning, and possibly encouraging and relaxing content that matches the student's emotional state.

[0505] The generated learning plan is provided to the user via the device. Students proceed with their studies based on this plan, while the device continuously monitors changes in the student's emotions and dynamically adjusts the plan as needed.

[0506] Furthermore, the server generates a feedback report that combines learning and emotional progress, and provides this to parents or teachers. This feedback includes not only the student's learning progress but also guidance that takes into account emotional support.

[0507] As a concrete example, when an elementary school student is working on a math problem and encounters a difficult one, the emotion engine recognizes the student's frustration. Based on this data, the server adjusts the difficulty of the problem and generates a new assignment with appropriate explanations. In this way, the system maximizes the effectiveness of student learning while maintaining their motivation to learn.

[0508] The following describes the processing flow.

[0509] Step 1:

[0510] The user (student) launches the learning app and logs into their device to begin their daily learning activities. At this point, they enter basic information about their learning content and goals.

[0511] Step 2:

[0512] The device monitors students' facial expressions, tone of voice, and input speed in real time, and uses an emotion engine to analyze students' emotional states (joy, excitement, frustration, etc.).

[0513] Step 3:

[0514] The device sends the collected learning data and emotion data to the server. The data includes emotional states along with learning progress information.

[0515] Step 4:

[0516] The server analyzes learning data and emotional data using an AI model to understand the students' current situation. This analysis evaluates not only the students' strengths and weaknesses in subjects, but also their current motivation and stress levels.

[0517] Step 5:

[0518] Based on the analysis results, the server generates a personalized learning plan. This plan is adjusted according to the user's learning progress and understanding, incorporating stress-reducing exercises and positive reinforcement elements as needed.

[0519] Step 6:

[0520] The device presents the user (student) with a learning plan received from the server. The student can proceed with their studies according to the presented plan, and the device continuously monitors their emotional state throughout this time.

[0521] Step 7:

[0522] The server periodically reviews progress in both learning and emotional aspects and adjusts the learning plan as needed. For example, if a student's concentration is waning, it provides instructions such as recommending a short break.

[0523] Step 8:

[0524] The server generates a feedback report based on learning and emotional progress and sends it to parents or instructors. This report includes specific advice to improve the student's learning efficiency and information on emotional support.

[0525] Step 9:

[0526] Users (parents or teachers) can review the feedback reports they receive and adjust their teaching methods and support approaches for students. They can also use this information to improve communication with students, thereby enhancing educational effectiveness.

[0527] (Example 2)

[0528] Next, we will describe Example 2. 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."

[0529] Conventional learning support systems provide feedback based on learners' understanding and progress, but they lack optimization that takes into account changes in learners' emotions. As a result, they are unable to respond appropriately when learners experience stress or a decline in motivation, making it difficult to maximize learning effectiveness and maintain motivation.

[0530] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0531] In this invention, the server includes means for collecting individual learning information of learners, means for recognizing nonverbal responses as emotions in real time and extracting emotional information, and means for analyzing the collected learning information and emotional information. This makes it possible to provide an optimized learning plan that takes into account both the learner's learning progress and emotional state.

[0532] A "learner" is an individual who seeks to acquire knowledge and skills through specific educational activities.

[0533] "Individual learning information" refers to data that represents the progress and level of understanding a learner has achieved in a specific subject or topic.

[0534] "Nonverbal responses" refer to reactions that do not involve language, such as facial expressions, tone of voice, and body movements, which are used to understand emotions.

[0535] "Emotional information" refers to data that indicates the learner's emotional state, and is extracted based on real-time emotion recognition.

[0536] "Analyzing" refers to the process of evaluating collected data using statistical methods and machine learning algorithms to understand learners' learning tendencies and emotional states.

[0537] A "learning plan" refers to the content of learning activities and materials provided that are designed based on each learner's individual progress, level of understanding, and emotional state.

[0538] "Feedback" refers to guidance guidelines presented to parents and instructors based on an analysis of information regarding learners' progress and understanding.

[0539] The system of this invention collects and analyzes individual learning information and emotional information of learners, and provides an optimized learning plan based on this information. Specific embodiments of this system will be described below.

[0540] First, the user (learner) uses a device to input their learning progress and activities. The device has built-in sensors such as a camera and microphone, which capture nonverbal responses in real time. During this process, emotional information is collected by capturing changes in the learner's facial expressions and voice.

[0541] Next, the device sends the collected learning and sentiment information to the server. Data transmission is performed using the HTTPS protocol to maintain data confidentiality and integrity.

[0542] The server analyzes the received information using a generative AI model. This AI model combines statistical analysis algorithms and machine learning algorithms to evaluate the learner's learning tendencies and emotional state. This reveals the learner's current level of understanding and emotional state, and generates an individually optimized learning plan.

[0543] The device then provides the user with a generated learning plan. This plan includes personalized content that takes into account the learner's current progress and emotional state, thereby enhancing learning effectiveness.

[0544] For example, if a learner is unable to concentrate on a task, a device sensor can detect this and notify the server. The server can then provide a new plan suggesting an appropriate break.

[0545] An example of a prompt message would be: "Analyze the learner's learning and emotional data and generate a learning plan for the next week. If the learner lacks focus, include measures to address this." This allows the system to continuously and dynamically provide support tailored to the learner's state.

[0546] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0547] Step 1:

[0548] Users (learners) input their learning progress and activities through the device. In addition, the device's sensors capture the learner's facial expressions and voice, collecting nonverbal responses as data. This input data includes information related to the learner's level of understanding and emotional state.

[0549] Step 2:

[0550] The device transmits collected learning and emotional information to the server. Secure communication protocols such as HTTPS are used to ensure data confidentiality during transmission. Input data includes specific learning progress and emotional changes of students, and this data is stored on the server in real time as output.

[0551] Step 3:

[0552] The server analyzes the received data using a generation AI model. The input training data and emotion data are processed by the AI ​​model to obtain evaluation results of the learner's learning tendencies and emotional state. This process allows for accurate real-time understanding of the learner's comprehension and emotional changes.

[0553] Step 4:

[0554] The server generates individually optimized learning plans based on the analysis results. The input is evaluation data output by the AI ​​model, and based on this, a learning plan is generated that includes tasks, materials, and emotionally sensitive content tailored to the learner's progress. For example, for a learner experiencing a specific emotional state, relaxation content to reduce stress will be selected.

[0555] Step 5:

[0556] The device delivers the generated learning plan to the user (learner). The learner can proceed with their learning according to the presented learning plan, while the device continuously monitors changes in their emotions using sensors. The input is the learning plan, and the output is the specific learning activities in which the learner puts that plan into practice.

[0557] Step 6:

[0558] The server generates and provides feedback reports to instructors and parents, combining learning and emotional progress. Inputs include the learner's learning history and emotional changes, and the server analyzes this data to output progress and guidance guidelines. Specifically, it visualizes areas requiring attention regarding understanding and emotional aspects, and suggests the next instructional steps.

[0559] (Application Example 2)

[0560] Next, we will explain application example 2. In the following explanation, 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."

[0561] In student learning, it is crucial to provide individually optimized content and flexibly adjust plans to take into account students' emotional states. However, conventional technologies have not adequately provided dynamic adjustments based on individual learning progress and emotions, making it difficult to provide efficient learning while maintaining students' motivation. This invention aims to solve these problems, thereby maximizing learning effectiveness and providing a flexible learning environment that meets the needs of individual learners.

[0562] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0563] In this invention, the server includes means for collecting individual student learning data, means for analyzing the collected data and nonverbal responses in real time, means for generating individual student learning plans based on the analysis results, and means for providing the generated learning plans to users and continuously monitoring and dynamically adjusting the students' emotional changes. This makes it possible to maximize the learning effectiveness of students while providing appropriate feedback and content that responds to their emotions.

[0564] "Individual student learning data" refers to data based on each student's learning progress, level of understanding, and the content of the assignments they are working on.

[0565] "Nonverbal responses" refer to reactions that are conveyed through means other than language, such as a student's facial expressions, tone of voice, and posture.

[0566] "Methods for real-time analysis" refer to technical means that enable immediate analysis of data the moment it is collected, allowing for the immediate acquisition of results.

[0567] "Methods for generating learning plans" refer to methods for creating optimized learning content and schedules based on each student's individual learning data and emotional state.

[0568] "Means for continuously monitoring and dynamically adjusting emotional changes" refers to technical means for constantly monitoring students' emotional states and adjusting learning plans and materials on the spot.

[0569] "Maximizing learning effectiveness" means enabling students to learn efficiently and maximize their understanding and knowledge.

[0570] "Appropriate feedback" refers to providing information and advice that is deemed beneficial in order to enhance students' learning effectiveness.

[0571] "Providing content" means supplying students with materials that facilitate their learning, such as information and teaching materials necessary for studying.

[0572] In this invention, the user first begins learning using a device. The device is equipped with a camera and microphone, which collect the student's nonverbal responses in real time. The device sends this data to a server. The server uses an emotion recognition engine and an AI model to analyze the received learning data and emotion data. Specifically, the emotion recognition engine analyzes the student's facial expressions and voice to determine their emotional state.

[0573] The server uses an AI model to generate personalized learning plans based on collected data. These plans include optimal learning materials and methods, but are also instantly adjusted based on the student's emotional state. If a change in emotion is detected, new learning tasks or refreshing content are presented.

[0574] For example, if a user is working on a math problem and the camera detects signs of stress, the server will present a new practice problem and deliver an AI-generated message of encouragement to the student via their device. This allows the student to continue learning with peace of mind.

[0575] Furthermore, the server generates feedback reports that combine progress data and emotional data, and provides them to parents and educators. This report enables instruction based on students' learning progress and emotional fluctuations.

[0576] An example of a prompt message might be, "What kind of encouraging message would be effective if a student is having trouble concentrating on their studies?" This allows the system to provide encouragement that is optimized for the student.

[0577] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0578] Step 1:

[0579] The user begins learning using a device. The device is equipped with a camera and microphone, and has the function of collecting the student's facial expressions and voice. At this stage, the input consists of the student's engagement with the learning content and audio and video data. The output is non-verbal response data.

[0580] Step 2:

[0581] The terminal sends the collected nonverbal response data from students to the server. This transmission takes place over the network, and the terminal sends facial expression data and voice data in real time as multiple data packets. The input requires the students' nonverbal response data, and the output is the completion of the data transfer to the server.

[0582] Step 3:

[0583] The server analyzes the received nonverbal response data in real time. Using an emotion recognition engine, it determines the student's emotional state from the input facial expressions and voice. This step yields the emotional state (e.g., joy, stress) as output.

[0584] Step 4:

[0585] The server uses emotion recognition results and training data as input to generate an individually optimized learning plan using an AI model. The AI ​​model performs data calculations to determine the optimal learning materials and pace based on the student's current level of understanding and emotional state. The output is an individualized learning plan.

[0586] Step 5:

[0587] The generated learning plan is provided to the device and presented to the user. The device displays this plan on its screen and provides audio notifications as needed. The input is the learning plan, and the output is the presentation of the plan.

[0588] Step 6:

[0589] As the user continues learning, the device continuously monitors changes in emotions and resends data to the server as needed. Dynamic adjustments are also made on the server based on these changes in emotions. The input for this step is continuously collected emotion data, and the output is an updated learning plan.

[0590] Step 7:

[0591] The server generates feedback reports based on learning and emotional progress data and provides them to parents or educators. Inputs include accumulated progress and emotional data, and the output is a comprehensive feedback report. This feedback is sent via email or other means.

[0592] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0593] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0594] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0595] [Fourth Embodiment]

[0596] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0597] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0598] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0599] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0600] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0601] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0602] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0603] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0604] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0605] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0606] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0607] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0608] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0609] The system for implementing this invention collects individual student learning data and generates and provides an optimal learning plan based on that data. This system is broadly composed of five functions: data collection, data analysis, learning plan generation, progress monitoring, and feedback provision. These functions work closely together to provide a seamless learning environment.

[0610] First, the user (student) uses a device to input daily learning data. This is often done on an online platform, and the input data includes homework progress, test scores, and time spent studying. The device sends this data to the server in real time.

[0611] Next, the server analyzes each student's current learning progress based on the collected data. An AI model processes this information to identify each student's strengths and areas of weakness. Based on this analysis, the server generates a customized learning plan. This plan includes the necessary materials and assignments, what to focus on next, and appropriate test preparation strategies.

[0612] The generated learning plan is provided to the user (student) via their device. This allows students to learn at their own pace. At the same time, the server periodically monitors the results of applying the learning plan and the students' progress, enabling immediate adjustments to the learning plan.

[0613] Furthermore, after a certain period of time, the server summarizes the progress of learning outcomes and generates a detailed feedback report. This feedback is provided to users (teachers and parents), allowing them to improve teaching methods as needed. This feedback clearly explains students' strengths and areas for improvement, making it extremely useful in actual educational activities.

[0614] As a concrete example, let's consider a high school student who struggles with mechanics in physics. The server analyzes the student's previous test results and past learning history to generate a learning plan that focuses on the fundamental concepts of mechanics. This plan includes interactive video materials and practice problems, allowing the student to deepen their understanding efficiently. The server also tracks progress as the student makes progress and updates the plan when sufficient results are seen.

[0615] The following describes the processing flow.

[0616] Step 1:

[0617] Users (students) use a learning app on their device to input data about their daily learning activities and progress. This data includes homework completion status, test results, content of lessons attended, and time spent studying.

[0618] Step 2:

[0619] The device receives the learning data entered by the user and prepares to send it to the server. The data is configured to be sent to the server in real time.

[0620] Step 3:

[0621] The server receives the training data sent from the terminal. The received data is stored in the database and simultaneously checked for any defects in data integrity or format.

[0622] Step 4:

[0623] The server uses AI models to analyze students' learning progress based on data stored in the database. This analysis identifies students' learning progress, level of understanding, and areas where they struggle.

[0624] Step 5:

[0625] Based on the analysis results, the server generates a learning plan optimized for the student's needs. The plan includes recommended materials, homework assignments, and next tasks to tackle.

[0626] Step 6:

[0627] The device presents the user (student) with a learning plan sent from the server. This allows the student to proceed with their learning activities based on the new plan.

[0628] Step 7:

[0629] The server periodically monitors the user's learning progress and re-evaluates the plan based on the analysis results. It updates the learning plan as needed.

[0630] Step 8:

[0631] The server generates a feedback report based on the student's learning progress and provides it to the parent or teacher.

[0632] Step 9:

[0633] Users (parents or teachers) can review feedback reports received from the server and adjust student guidance strategies. They can also provide direct feedback to students, offering encouragement and suggesting areas for improvement.

[0634] (Example 1)

[0635] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0636] In today's educational environment, there is a demand for individualized instruction tailored to each student's learning characteristics. However, it is difficult for teachers and parents to accurately grasp students' progress and understanding, and to provide effective instruction as needed. Furthermore, there is a lack of methods for creating appropriate learning plans and dynamically adjusting them while regularly monitoring their progress. In conventional systems, handling student data and updating plans is often done manually, which is time-consuming and labor-intensive, making effective individualized instruction difficult.

[0637] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0638] In this invention, the server includes means for collecting individual student learning information, means for processing the collected information in real time, and means for creating individualized learning plans for students based on the processing results. This enables the automatic generation and monitoring of learning plans tailored to each student. The system also utilizes a generation AI model to flexibly adjust plans according to the student's progress and level of understanding, providing efficient and effective learning support.

[0639] "Individualized student learning information" refers to specific data about each student, such as their educational progress, grades, level of understanding, study time, and progress on assignments.

[0640] "Means of collection" refers to systems or processes for automatically or manually collecting learning information entered by users.

[0641] "Real-time processing methods" refer to technical mechanisms for instantly analyzing collected information and providing rapid feedback or results based on that data.

[0642] "Methods for creating educational plans" refers to the process of designing a learning plan that takes into account the content, materials, assignments, and test preparation that students should learn, based on their individual learning information.

[0643] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to analyze data and automatically create and update the optimal learning strategy and plan for the user.

[0644] "Monitoring" is an oversight activity that involves regularly tracking students' learning progress and understanding, and modifying the educational plan as needed.

[0645] "Means of providing advice" refer to communication methods that effectively convey the generated feedback to parents and educators and help improve educational policies.

[0646] The system for implementing this invention aims to provide individualized education tailored to the learning characteristics of each student. This system functions primarily through interaction between a server, terminals, and users, and is implemented in the following manner.

[0647] Users (students) input daily learning information via a device. This input includes homework progress, test scores, and time spent studying. The entered information is transmitted from the device to the server in real time. The devices used here are compatible with a variety of devices, including PCs, tablets, and smartphones. To ensure data security, secure protocols such as SSL are used for communication.

[0648] The server stores the received data and analyzes it in real time using an AI model. The AI ​​model utilizes supervised learning and generative AI models to identify students' strengths and areas for improvement. Based on this analysis, the server generates a customized learning plan. This plan includes learning materials, assignments, and test preparation strategies. For example, for a student struggling with mechanics in physics, a learning plan focused on fundamental mechanics concepts is created, providing interactive video materials and practice problems.

[0649] The generated educational plans are provided to users via a terminal. The terminal's interface is designed for intuitive operation, enabling users to learn effectively according to the learning plan. The server further uses a generating AI model to monitor students' progress and understanding, and updates the learning plan as needed. In addition, regular monitoring generates feedback reports on educational progress and outcomes, which are provided to parents and educators, thereby improving the quality of individualized instruction.

[0650] A concrete example of a prompt message could be, "Based on this student's physics learning history, generate a customized learning plan focused on mechanics," which could be input into the generating AI model.

[0651] In this way, the system of this invention can provide students with an optimal learning environment and significantly improve the efficiency and effectiveness of educational activities.

[0652] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0653] Step 1:

[0654] Users (students) input learning information using a terminal. This information includes homework progress, test scores, and study time. This data is entered through the terminal's interface, converted to a data format, and then sent to the server. Input data formats include numerical data, text data, and time data.

[0655] Step 2:

[0656] The terminal transmits the entered learning information to the server in real time. This process utilizes secure protocols such as SSL to ensure data security. During transmission, the data is divided into data packets, which are then reconstructed on the server side. The transmitted data is then ready to be registered in the database.

[0657] Step 3:

[0658] The server stores the received training data in a database. During storage, data integrity checks and formatting are performed. This stored data is then used as foundational information for AI analysis.

[0659] Step 4:

[0660] The server inputs the stored data into an AI model and performs data analysis. The AI ​​model uses supervised learning and performs data mining to recognize students' strengths and weaknesses. This analysis determines their level of understanding in specific subjects and skills. The output provides an evaluation of each student's level of understanding.

[0661] Step 5:

[0662] The server uses a generated AI model based on the analysis results of the AI ​​model to generate an optimized learning plan for the student. The generated plan includes specific teaching materials, assignments, and test preparation strategies. This plan generation process uses prompts to define a plan that suits the student. An example of a prompt might be, "Based on this student's physics learning history, generate a learning plan that focuses on mechanics."

[0663] Step 6:

[0664] The generated educational plans are provided to users via a terminal. The terminal screen displays the plan's contents in detail, helping students effectively progress through their studies according to its content. An interface is provided that allows users to access the information they need.

[0665] Step 7:

[0666] The server regularly monitors users' learning progress and re-analyzes it whenever new data is entered. This allows for timely updates of the educational plan through the generative AI model. Progress feedback reports are generated regularly and sent to parents and educators.

[0667] (Application Example 1)

[0668] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0669] Managing the health of users, including the elderly, presents a challenge in providing customized care plans tailored to individual needs. Conventional systems rely on a uniform approach, failing to adequately address individual health conditions and lifestyles, making efficient health maintenance difficult. This invention aims to improve individual health maintenance and quality of life by providing personalized care plans tailored to each user.

[0670] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0671] In this invention, the server includes means for collecting individual health data of users, means for analyzing the collected health data in real time, and means for generating individual care plans based on the analysis results. This makes it possible to generate and provide care plans tailored to the user's health condition.

[0672] "User" refers to the person receiving the service, and is the subject from whom health data is collected in this system.

[0673] "Health data" refers to information collected about the user's physical condition and lifestyle, which is used to create care plans.

[0674] "Methods for real-time analysis" refer to technologies that process collected health data immediately and quickly grasp the user's current condition.

[0675] An "individualized care plan" refers to a plan that includes customized care guidance based on the user's health condition and lifestyle.

[0676] "Monitoring progress" refers to continuously recording and evaluating the user's response to the care plan and changes in their health condition.

[0677] "Feedback" refers to evaluations and advice provided to users, their guardians, and instructors, and is information that can be used to adjust care plans.

[0678] The system that implements this application example implements a program to individually manage users' health data and provide optimal care plans. The server plays a central role, processing the collected data and performing real-time analysis. Specifically, the hardware consists of terminals used by users (smartphones or care robots), which transmit health data to the server. The server receives this data and analyzes the user's health status using an AI model implemented in Python.

[0679] Based on the analysis results, the server automatically generates individualized care plans and provides them to users via their terminals. These care plans include personalized dietary suggestions and exercise advice tailored to each user's health condition, contributing to improvements in their daily lives. The server also periodically monitors the user's progress and generates feedback. This feedback monitors changes in the user's condition and the effectiveness of the plan in real time, suggesting the next steps to take.

[0680] As a concrete example, to improve the cardiac health of elderly individuals, a suitable walking plan may be created based on daily heart rate data. The server analyzes changes in heart rate and advises on appropriate exercise intensity and frequency, helping users maintain their cardiac health. An example of a prompt in this case would be, "Generate an exercise plan effective for cardiac health based on current heart rate data." In this way, the system provides optimal care for each user, supporting the maintenance and improvement of their health.

[0681] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0682] Step 1:

[0683] The device collects the user's daily activity and health data. This data includes heart rate, steps taken, and meal records. The device periodically sends this data to the server. Input is the data recorded by the user, and output is the data sent to the server.

[0684] Step 2:

[0685] The server analyzes data received from the terminal in real time. Here, an AI model implemented in Python processes the data and extracts parameters for evaluating the user's health status. The input is raw data received from the terminal, and the output is analyzed health assessment information. Specifically, it calculates the average and variability of the obtained heart rate and generates health status indicators.

[0686] Step 3:

[0687] The server uses a generative AI model to create an individualized care plan based on health assessment information. The input is the assessment information obtained in step 2, and the output is a care plan that includes actions recommended for the user. Specifically, it selects and plans exercise and dietary content appropriate for the user. Prompt messages are also generated in this step, with instructions such as "Create the optimal exercise plan from the current data" being generated by the generative AI model.

[0688] Step 4:

[0689] The terminal presents the care plan received from the server to the user. The input is the care plan sent from the server, and the output is the display to the user. The user can adjust their daily life based on this. Specifically, the terminal displays the plan contents using alerts and notifications to make it easy for the user to understand.

[0690] Step 5:

[0691] The server monitors data updates from the terminal and continuously monitors the user's progress. The input is newly acquired health data, and the output is feedback based on the progress. Specifically, it evaluates whether progress is proceeding according to plan and prepares to adjust the plan as needed. This feedback is ultimately returned to the user.

[0692] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0693] This invention is a system that not only collects and analyzes individual student learning data, but also incorporates an emotion engine that recognizes students' emotions in real time, thereby further optimizing learning plans. The system consists of three main functions: data collection, emotion analysis, learning plan generation, progress monitoring, and feedback provision.

[0694] First, the user (student) uses a device to input their learning progress and activities. In this process, the device may be equipped with sensors to recognize the student's nonverbal responses (e.g., changes in facial expressions or voice), thereby collecting emotional data.

[0695] The device sends collected learning data and sentiment data to the server. This data is designed to be stored on the server in real time.

[0696] The server uses an AI model to analyze the student's learning data and emotional data that it receives. By combining the learning data and emotional data, it is possible to understand not only the student's current level of comprehension but also their emotional state, such as learning stress or decreased motivation.

[0697] Based on these analysis results, the server generates an individually optimized learning plan. This plan may include assignments tailored to the student's learning progress, learning materials to enhance optimal learning, and possibly encouraging and relaxing content that matches the student's emotional state.

[0698] The generated learning plan is provided to the user via the device. Students proceed with their studies based on this plan, while the device continuously monitors changes in the student's emotions and dynamically adjusts the plan as needed.

[0699] Furthermore, the server generates a feedback report that combines learning and emotional progress, and provides this to parents or teachers. This feedback includes not only the student's learning progress but also guidance that takes into account emotional support.

[0700] As a concrete example, when an elementary school student is working on a math problem and encounters a difficult one, the emotion engine recognizes the student's frustration. Based on this data, the server adjusts the difficulty of the problem and generates a new assignment with appropriate explanations. In this way, the system maximizes the effectiveness of student learning while maintaining their motivation to learn.

[0701] The following describes the processing flow.

[0702] Step 1:

[0703] The user (student) launches the learning app and logs into their device to begin their daily learning activities. At this point, they enter basic information about their learning content and goals.

[0704] Step 2:

[0705] The device monitors students' facial expressions, tone of voice, and input speed in real time, and uses an emotion engine to analyze students' emotional states (joy, excitement, frustration, etc.).

[0706] Step 3:

[0707] The device sends the collected learning data and emotion data to the server. The data includes emotional states along with learning progress information.

[0708] Step 4:

[0709] The server analyzes learning data and emotional data using an AI model to understand the students' current situation. This analysis evaluates not only the students' strengths and weaknesses in subjects, but also their current motivation and stress levels.

[0710] Step 5:

[0711] Based on the analysis results, the server generates a personalized learning plan. This plan is adjusted according to the user's learning progress and understanding, incorporating stress-reducing exercises and positive reinforcement elements as needed.

[0712] Step 6:

[0713] The device presents the user (student) with a learning plan received from the server. The student can proceed with their studies according to the presented plan, and the device continuously monitors their emotional state throughout this time.

[0714] Step 7:

[0715] The server periodically reviews progress in both learning and emotional aspects and adjusts the learning plan as needed. For example, if a student's concentration is waning, it provides instructions such as recommending a short break.

[0716] Step 8:

[0717] The server generates a feedback report based on learning and emotional progress and sends it to parents or instructors. This report includes specific advice to improve the student's learning efficiency and information on emotional support.

[0718] Step 9:

[0719] Users (parents or teachers) can review the feedback reports they receive and adjust their teaching methods and support approaches for students. They can also use this information to improve communication with students, thereby enhancing educational effectiveness.

[0720] (Example 2)

[0721] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0722] Conventional learning support systems provide feedback based on learners' understanding and progress, but they lack optimization that takes into account changes in learners' emotions. As a result, they are unable to respond appropriately when learners experience stress or a decline in motivation, making it difficult to maximize learning effectiveness and maintain motivation.

[0723] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0724] In this invention, the server includes means for collecting individual learning information of learners, means for recognizing nonverbal responses as emotions in real time and extracting emotional information, and means for analyzing the collected learning information and emotional information. This makes it possible to provide an optimized learning plan that takes into account both the learner's learning progress and emotional state.

[0725] A "learner" is an individual who seeks to acquire knowledge and skills through specific educational activities.

[0726] "Individual learning information" refers to data that represents the progress and level of understanding a learner has achieved in a specific subject or topic.

[0727] "Nonverbal responses" refer to reactions that do not involve language, such as facial expressions, tone of voice, and body movements, which are used to understand emotions.

[0728] "Emotional information" refers to data that indicates the learner's emotional state, and is extracted based on real-time emotion recognition.

[0729] "Analyzing" refers to the process of evaluating collected data using statistical methods and machine learning algorithms to understand learners' learning tendencies and emotional states.

[0730] A "learning plan" refers to the content of learning activities and materials provided that are designed based on each learner's individual progress, level of understanding, and emotional state.

[0731] "Feedback" refers to guidance guidelines presented to parents and instructors based on an analysis of information regarding learners' progress and understanding.

[0732] The system of this invention collects and analyzes individual learning information and emotional information of learners, and provides an optimized learning plan based on this information. Specific embodiments of this system will be described below.

[0733] First, the user (learner) uses a device to input their learning progress and activities. The device has built-in sensors such as a camera and microphone, which capture nonverbal responses in real time. During this process, emotional information is collected by capturing changes in the learner's facial expressions and voice.

[0734] Next, the device sends the collected learning and sentiment information to the server. Data transmission is performed using the HTTPS protocol to maintain data confidentiality and integrity.

[0735] The server analyzes the received information using a generative AI model. This AI model combines statistical analysis algorithms and machine learning algorithms to evaluate the learner's learning tendencies and emotional state. This reveals the learner's current level of understanding and emotional state, and generates an individually optimized learning plan.

[0736] The device then provides the user with a generated learning plan. This plan includes personalized content that takes into account the learner's current progress and emotional state, thereby enhancing learning effectiveness.

[0737] For example, if a learner is unable to concentrate on a task, a device sensor can detect this and notify the server. The server can then provide a new plan suggesting an appropriate break.

[0738] An example of a prompt message would be: "Analyze the learner's learning and emotional data and generate a learning plan for the next week. If the learner lacks focus, include measures to address this." This allows the system to continuously and dynamically provide support tailored to the learner's state.

[0739] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0740] Step 1:

[0741] Users (learners) input their learning progress and activities through the device. In addition, the device's sensors capture the learner's facial expressions and voice, collecting nonverbal responses as data. This input data includes information related to the learner's level of understanding and emotional state.

[0742] Step 2:

[0743] The device transmits collected learning and emotional information to the server. Secure communication protocols such as HTTPS are used to ensure data confidentiality during transmission. Input data includes specific learning progress and emotional changes of students, and this data is stored on the server in real time as output.

[0744] Step 3:

[0745] The server analyzes the received data using a generation AI model. The input training data and emotion data are processed by the AI ​​model to obtain evaluation results of the learner's learning tendencies and emotional state. This process allows for accurate real-time understanding of the learner's comprehension and emotional changes.

[0746] Step 4:

[0747] The server generates individually optimized learning plans based on the analysis results. The input is evaluation data output by the AI ​​model, and based on this, a learning plan is generated that includes tasks, materials, and emotionally sensitive content tailored to the learner's progress. For example, for a learner experiencing a specific emotional state, relaxation content to reduce stress will be selected.

[0748] Step 5:

[0749] The device delivers the generated learning plan to the user (learner). The learner can proceed with their learning according to the presented learning plan, while the device continuously monitors changes in their emotions using sensors. The input is the learning plan, and the output is the specific learning activities in which the learner puts that plan into practice.

[0750] Step 6:

[0751] The server generates and provides feedback reports to instructors and parents, combining learning and emotional progress. Inputs include the learner's learning history and emotional changes, and the server analyzes this data to output progress and guidance guidelines. Specifically, it visualizes areas requiring attention regarding understanding and emotional aspects, and suggests the next instructional steps.

[0752] (Application Example 2)

[0753] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0754] In student learning, it is crucial to provide individually optimized content and flexibly adjust plans to take into account students' emotional states. However, conventional technologies have not adequately provided dynamic adjustments based on individual learning progress and emotions, making it difficult to provide efficient learning while maintaining students' motivation. This invention aims to solve these problems, thereby maximizing learning effectiveness and providing a flexible learning environment that meets the needs of individual learners.

[0755] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0756] In this invention, the server includes means for collecting individual student learning data, means for analyzing the collected data and nonverbal responses in real time, means for generating individual student learning plans based on the analysis results, and means for providing the generated learning plans to users and continuously monitoring and dynamically adjusting the students' emotional changes. This makes it possible to maximize the learning effectiveness of students while providing appropriate feedback and content that responds to their emotions.

[0757] "Individual student learning data" refers to data based on each student's learning progress, level of understanding, and the content of the assignments they are working on.

[0758] "Nonverbal responses" refer to reactions that are conveyed through means other than language, such as a student's facial expressions, tone of voice, and posture.

[0759] "Methods for real-time analysis" refer to technical means that enable immediate analysis of data the moment it is collected, allowing for the immediate acquisition of results.

[0760] "Methods for generating learning plans" refer to methods for creating optimized learning content and schedules based on each student's individual learning data and emotional state.

[0761] "Means for continuously monitoring and dynamically adjusting emotional changes" refers to technical means for constantly monitoring students' emotional states and adjusting learning plans and materials on the spot.

[0762] "Maximizing learning effectiveness" means enabling students to learn efficiently and maximize their understanding and knowledge.

[0763] "Appropriate feedback" refers to providing information and advice that is deemed beneficial in order to enhance students' learning effectiveness.

[0764] "Providing content" means supplying students with materials that facilitate their learning, such as information and teaching materials necessary for studying.

[0765] In this invention, the user first begins learning using a device. The device is equipped with a camera and microphone, which collect the student's nonverbal responses in real time. The device sends this data to a server. The server uses an emotion recognition engine and an AI model to analyze the received learning data and emotion data. Specifically, the emotion recognition engine analyzes the student's facial expressions and voice to determine their emotional state.

[0766] The server uses an AI model to generate personalized learning plans based on collected data. These plans include optimal learning materials and methods, but are also instantly adjusted based on the student's emotional state. If a change in emotion is detected, new learning tasks or refreshing content are presented.

[0767] For example, if a user is working on a math problem and the camera detects signs of stress, the server will present a new practice problem and deliver an AI-generated message of encouragement to the student via their device. This allows the student to continue learning with peace of mind.

[0768] Furthermore, the server generates feedback reports that combine progress data and emotional data, and provides them to parents and educators. This report enables instruction based on students' learning progress and emotional fluctuations.

[0769] An example of a prompt message might be, "What kind of encouraging message would be effective if a student is having trouble concentrating on their studies?" This allows the system to provide encouragement that is optimized for the student.

[0770] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0771] Step 1:

[0772] The user begins learning using a device. The device is equipped with a camera and microphone, and has the function of collecting the student's facial expressions and voice. At this stage, the input consists of the student's engagement with the learning content and audio and video data. The output is non-verbal response data.

[0773] Step 2:

[0774] The terminal sends the collected nonverbal response data from students to the server. This transmission takes place over the network, and the terminal sends facial expression data and voice data in real time as multiple data packets. The input requires the students' nonverbal response data, and the output is the completion of the data transfer to the server.

[0775] Step 3:

[0776] The server analyzes the received nonverbal response data in real time. Using an emotion recognition engine, it determines the student's emotional state from the input facial expressions and voice. This step yields the emotional state (e.g., joy, stress) as output.

[0777] Step 4:

[0778] The server uses emotion recognition results and training data as input to generate an individually optimized learning plan using an AI model. The AI ​​model performs data calculations to determine the optimal learning materials and pace based on the student's current level of understanding and emotional state. The output is an individualized learning plan.

[0779] Step 5:

[0780] The generated learning plan is provided to the device and presented to the user. The device displays this plan on its screen and provides audio notifications as needed. The input is the learning plan, and the output is the presentation of the plan.

[0781] Step 6:

[0782] As the user continues learning, the device continuously monitors changes in emotions and resends data to the server as needed. Dynamic adjustments are also made on the server based on these changes in emotions. The input for this step is continuously collected emotion data, and the output is an updated learning plan.

[0783] Step 7:

[0784] The server generates feedback reports based on learning and emotional progress data and provides them to parents or educators. Inputs include accumulated progress and emotional data, and the output is a comprehensive feedback report. This feedback is sent via email or other means.

[0785] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0786] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0787] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0788] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0789] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0790] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0791] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0792] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0793] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0794] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0795] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0796] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0797] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0798] 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.

[0799] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0800] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0801] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0802] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0803] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0804] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0805] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0806] The following is further disclosed regarding the embodiments described above.

[0807] (Claim 1)

[0808] A means of collecting individual student learning data,

[0809] A means of analyzing the collected data in real time,

[0810] A means of generating individualized learning plans for students based on the analysis results,

[0811] A means of providing the generated learning plan to the user,

[0812] A means of regularly monitoring and reassessing students' progress,

[0813] A system that includes means of providing feedback to parents or instructors based on students' understanding and progress.

[0814] (Claim 2)

[0815] The system according to claim 1, which includes recommended learning materials, homework assignments, and practice problems for exam preparation in generating a learning plan.

[0816] (Claim 3)

[0817] The system according to claim 1, further comprising means for verifying the integrity of the collected data and checking for any defects or errors.

[0818] "Example 1"

[0819] (Claim 1)

[0820] A means of collecting individual learning information from students,

[0821] A means of processing the collected information in real time,

[0822] A means of creating individualized educational plans for students based on the processing results,

[0823] Means of providing the created educational plan to the user,

[0824] A means of regularly monitoring and reassessing students' progress,

[0825] A means of providing supervisors or educators with advice based on students' understanding and progress,

[0826] A method for automatically updating customized learning plans using a generative AI model,

[0827] A means of storing the collected information and recording it in a database,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, which includes recommended educational materials, assignment content, and practice problems for exam preparation in the creation of an educational plan.

[0831] (Claim 3)

[0832] The system according to claim 1, further comprising means for verifying the integrity of the collected information and confirming that there are no deficiencies or errors, and means for ensuring data security in order to improve the accuracy of the analysis.

[0833] "Application Example 1"

[0834] (Claim 1)

[0835] A means of collecting individual health data of users,

[0836] A means of analyzing collected health data in real time,

[0837] A means of generating individual care plans based on analysis results,

[0838] Means of providing the generated care plan to the user,

[0839] A means of regularly monitoring and re-evaluating the progress of users,

[0840] A system that includes means of providing feedback to parents or instructors based on the user's health status and progress.

[0841] (Claim 2)

[0842] The system according to claim 1, which includes recommendations for dietary content, exercise activities, and health management in generating a care plan.

[0843] (Claim 3)

[0844] The system according to claim 1, further comprising means for verifying the integrity of collected health data and checking for deficiencies or errors.

[0845] "Example 2 of combining an emotion engine"

[0846] (Claim 1)

[0847] A means of collecting individual learning information of learners,

[0848] A means of recognizing nonverbal responses as emotions in real time and extracting emotional information,

[0849] A means for analyzing collected learning information and emotional information,

[0850] A means for generating individually optimized learning plans based on analysis results,

[0851] A means of providing the generated learning plan to the learner and dynamically adjusting it in response to changes in their emotions,

[0852] A system that includes means of providing instructors and parents with feedback on learning and emotional progress.

[0853] (Claim 2)

[0854] The system according to claim 1, comprising a learning plan that includes recommended materials, practice exercises, and encouraging content adapted to the student's emotional state.

[0855] (Claim 3)

[0856] The system according to claim 1, further comprising means for verifying the consistency of collected learning information and emotional information and checking for any deficiencies or errors.

[0857] "Application example 2 when combining with an emotional engine"

[0858] (Claim 1)

[0859] A means of collecting individual student learning data,

[0860] A means of analyzing collected data and nonverbal responses in real time,

[0861] A means of generating individualized learning plans for students based on the analysis results,

[0862] A means to provide users with a generated learning plan, and to continuously monitor and dynamically adjust changes in students' emotions,

[0863] A means of regularly monitoring and reassessing students' progress,

[0864] A means of providing feedback to parents or instructors based on students' understanding and progress,

[0865] A system that includes means to incorporate encouraging and relaxing content into learning plans based on emotional state.

[0866] (Claim 2)

[0867] The system according to claim 1, which generates a learning plan that includes recommended learning materials, assignment content, and practice problems for exam preparation, and dynamically adjusts the content based on the user's emotional state.

[0868] (Claim 3)

[0869] The system according to claim 1, further comprising means for verifying the integrity of collected data and checking for deficiencies or errors, and comprising an optimization algorithm based on the results of an analysis of students' emotional states. [Explanation of Symbols]

[0870] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting individual health data of users, A means of analyzing collected health data in real time, A means of generating individual care plans based on analysis results, Means of providing the generated care plan to the user, A means of regularly monitoring and re-evaluating the progress of users, A system that includes means of providing feedback to parents or instructors based on the user's health status and progress.

2. The system according to claim 1, which includes recommendations for dietary content, exercise activities, and health management in generating a care plan.

3. The system according to claim 1, further comprising means for verifying the integrity of collected health data and checking for any deficiencies or errors.

Images