system

Abstract

We provide the system. [Solution] A processor comprising an input device for entering user information, which generates a user profile based on the entered information and generates a customized learning plan based on the user profile, A means for monitoring the user's learning progress based on a generated customized learning plan and generating feedback using generative artificial intelligence, A means of providing generated feedback to the user and optimizing the learning plan, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] Since there is no system that efficiently provides individualized mentoring, there is a problem that individuals and companies have limited opportunities to receive high-quality guidance. For this reason, it is difficult for users to obtain a learning and skill improvement plan that suits their goals, and it is difficult for companies to promote the skill improvement of employees in a consistent manner. In addition, in the conventional method, since mental health support is insufficient, there is a risk that the continuous growth of users will be hindered.

Means for Solving the Problems

[0005] This invention employs an input device for inputting user information and a processor that generates a user profile based on the input information. Furthermore, it provides high-quality mentoring to individuals and companies by generating a customized learning plan based on the user profile, monitoring the user's learning progress according to this learning plan, and generating feedback using artificial intelligence. This enables the realization of an optimal learning experience tailored to the user's goals, and comprehensively supports the skill development process by incorporating the latest technological information into the learning plan and adding functions to support mental well-being.

[0006] An "input device" is hardware and an interface used by a user to input information into a system.

[0007] A "user profile" is data about an individual's goals, skill level, and interests, generated based on information the user provides to the system.

[0008] A "processor" is a computing device that processes data according to a program and generates user profiles and learning plans.

[0009] A "customized learning plan" is a design document that includes a learning schedule and content tailored to the user's profile.

[0010] "Generative artificial intelligence" is a technology that uses computer programs to analyze data and generate optimal feedback and advice for users.

[0011] "Monitoring" is the process of collecting and analyzing information to track users' learning activities and progress, and to provide appropriate feedback.

[0012] "Feedback" refers to information that evaluates and indicates areas for improvement in a user's learning activities, and is provided to support the user's growth. [Brief explanation of the drawing]

[0013] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Mode for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a 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.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention describes an embodiment for implementing a system for providing a personalized learning experience to a user. The system comprises an input device, a processor, a generative AI, and a user interface.

[0035] System Configuration Overview

[0036] When the system first imports user information, it uses a terminal to input the user's details. This information includes the user's goals, skill level, and areas of interest. The terminal then sends this information to the server to create the user profile.

[0037] The processor resides on the server, and a generative AI operates based on the user's profile. The generative AI creates a learning plan optimized for the user and incorporates the latest technical information into the plan. This process enables the continuous delivery of a personalized educational experience to the user.

[0038] User experience details

[0039] Users of the system first input their own information to create a user profile tailored to their individual needs. For example, if a user wants to acquire new technical skills, the server takes that learning motivation into consideration and generates a customized curriculum.

[0040] This curriculum includes recommended learning modules, online courses, or specific tasks. Users record their progress via their devices, and this information is fed back to the server. Generative AI analyzes this progress data and provides users with areas for improvement and new goals.

[0041] Specific example

[0042] For example, suppose an IT professional wants to learn a new programming language. This user inputs their goals and current skill level via a terminal. The server receives this data, and the processor generates a learning plan tailored to the user. This plan includes online course schedules and actual code exercises.

[0043] As users record their progress according to their plan, the generating AI provides feedback based on this data. For example, if a user is learning slowly, the AI ​​might suggest intensive exercises on weekends, providing feedback that helps the user further improve their learning effectiveness.

[0044] In this way, the present invention provides a high-quality learning experience tailored to the user and supports efficient skill acquisition.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The user creates their account using a device and enters the required information. This information includes their current skill level, learning goals, and areas of interest. The device then sends this entered information to the server.

[0048] Step 2:

[0049] The server generates a user profile based on the information received from the user. This profile contains detailed data about the user's learning needs and goals. The generated profile is stored in a database.

[0050] Step 3:

[0051] The server's processor uses generative AI to create a customized learning plan based on the user's profile. This determines specific learning modules, materials, and schedules that take into account the user's current skill level and goals.

[0052] Step 4:

[0053] Once the learning plan is complete, the server sends the plan to the user's terminal. The terminal displays the plan through a user interface so that the user can review and access it.

[0054] Step 5:

[0055] Users proceed with their learning according to the provided learning plan. Learning progress and activities are recorded via the device and sent to the server.

[0056] Step 6:

[0057] The server periodically analyzes progress data, and the generating AI produces real-time feedback. This feedback includes an assessment of the user's learning progress and advice on necessary adjustments.

[0058] Step 7:

[0059] Feedback is sent from the server to the user's device, where the user reviews it. Based on this feedback, the user can continue, revise, or change their plan to maximize their learning effectiveness.

[0060] Step 8:

[0061] The server uses user feedback to improve the overall service and continuously enhance the learning experience. It incorporates the latest technical information and updates learning plans as needed.

[0062] (Example 1)

[0063] 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."

[0064] Currently, providing personalized learning experiences that meet the diverse needs of learners is difficult, and effective feedback on learning progress and optimization of learning plans according to individual learning situations are not adequately implemented. Furthermore, it is not easy to incorporate the latest technological information into individual learning processes. As a result, there is a challenge in efficiently acquiring skills that are tailored to the unique needs of each individual learner.

[0065] 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.

[0066] This invention includes a server equipped with a device for inputting the user's goals, skill level, and areas of interest; a computing device that generates individual user profiles based on the input information and generates a personalized learning plan based on the user profile; means for recording the user's learning progress based on the generated personalized learning plan and generating feedback using a generating AI; and means for providing the generated feedback to the user and continuously optimizing the learning plan. This enables users to efficiently acquire skills and obtain an effective learning experience utilizing the latest technological information within an individually customized learning process.

[0067] "User information" refers to data that users enter to identify their individual learning needs, such as their goals, skill levels, and areas of interest.

[0068] An "input device" is a hardware or software means used by a user to input information.

[0069] A "user profile" is a dataset generated based on user information, reflecting individual characteristics and needs.

[0070] A "personalized learning plan" is a plan that incorporates learning content and procedures optimized for the individual, based on the user's profile.

[0071] A "processing unit" is a device centered around a processor that processes data and generates learning plans and feedback.

[0072] "Generative AI" refers to a system that uses artificial intelligence technology to analyze user data, generate learning plans, and provide feedback.

[0073] "Progress" refers to the status of activities and achievements that a user has accomplished based on their learning plan.

[0074] "Feedback" refers to information provided by the AI-generated based on the user's learning progress, including areas for improvement and new learning goals.

[0075] "Optimization means" refers to the process or method for continuously improving the user's learning experience and updating plans to suit individual learning needs.

[0076] This invention is a system for providing users with a personalized learning experience, and the system consists of the following elements.

[0077] 1. Enter user information

[0078] Terminal: Users input information using a terminal. This information includes goals, skill levels, and areas of interest, and is obtained through the input device.

[0079] 2. Generating a user profile

[0080] Server: Receives information sent from the terminal and creates a user profile using the computing unit. This profile is stored in a database and forms the basis for later learning plans.

[0081] 3. Generating a personalized learning plan

[0082] Server: Utilizes generative AI models to create individually optimized learning plans based on user profiles. This process is executed on the computing unit and reflects the latest technological information.

[0083] 4. Managing learning progress and providing feedback

[0084] User: Proceed according to the learning plan and record the process on the device.

[0085] Server: Analyzes progress information sent from the terminal and generates appropriate feedback using a generator AI. This feedback includes suggestions for improvement and new learning objectives.

[0086] Specific example

[0087] For example, if an IT professional wants to learn a new programming language, they input their goals and skills through a terminal. The server receives this information and generates a learning plan tailored to the user. The plan includes a list of online courses and practice problems. The server also provides personalized feedback as the user progresses through their learning. Based on this feedback, the user can further improve their learning efficiency.

[0088] Example of a prompt

[0089] "I want to learn a new programming language. My current skill level is intermediate, and I have experience with Java® and Python. Please generate a personalized learning plan."

[0090] Thus, the present invention is a useful system for providing a user-tailored learning process and supporting effective skill acquisition.

[0091] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0092] Step 1:

[0093] Enter user information

[0094] Terminal: Users use a terminal to input information such as goals, skill levels, and areas of interest. This data is initially acquired by the input device. The output data generated will contain detailed information about the user's individual needs.

[0095] Step 2:

[0096] Creating a user profile

[0097] Server: Receives user information sent from the terminal. This input data is processed by the processor to create a user profile. Specifically, it categorizes skill levels and parses goal strings, generating a user profile that is stored in the database as output.

[0098] Step 3:

[0099] Generating a learning plan

[0100] Server: Uses a generative AI model to create personalized learning plans based on user profiles. Specifically, relevant learning materials and curricula are selected considering the user's skill level and areas of interest. The input is the user profile, and the output is the generated learning plan.

[0101] Step 4:

[0102] Recording of learning activities and progress

[0103] User: Based on the obtained learning plan, the user begins learning activities. Tasks completed and progress made during the learning process are recorded via the device. Input is saved as data related to the learning process, and output is saved as progress information.

[0104] Step 5:

[0105] Analysis of progress data and generation of feedback

[0106] Server: The server analyzes the collected progress information using a processor and generates user-appropriate feedback using a generative AI. A specific example is providing additional materials to deepen understanding based on progress. The input is progress data, and the output is feedback including areas for improvement and goal setting.

[0107] (Application Example 1)

[0108] 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."

[0109] In modern education, providing personalized learning experiences for individual learners is crucial, but traditional systems struggle to appropriately deliver optimal content tailored to users' interests and skill levels. Furthermore, real-time feedback based on learning progress and suggestions for the next learning content are lacking. A system is needed to address these challenges.

[0110] 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.

[0111] In this invention, the server includes an input device for inputting user information, a processing unit that generates a user profile based on the input information and generates a customized learning plan based on the user profile, means for monitoring the user's learning progress based on the generated customized learning plan and generating feedback using generative artificial intelligence, means for providing the generated feedback to the user and optimizing the learning plan, means for selecting learning content based on the user's interests and skills and enabling viewing on a visual device, and means for using a generative artificial intelligence model to suggest content according to the learning progress. This makes it possible to provide individual learners with a more effective and efficient learning experience and support the improvement of their skills.

[0112] "User information" refers to data such as goals, skill levels, and areas of interest entered by learners.

[0113] An "input device" refers to a hardware or software configuration for collecting user information, processing it electronically, and transmitting that information to a server.

[0114] A "user profile" refers to digital information generated based on the user's input, representing the individual learner's characteristics and needs.

[0115] A "learning plan" refers to a set of guidelines, customized by a generating AI based on the user's profile, that outlines the goals and means by which the learner should pursue and achieve them.

[0116] A "processing device" refers to a computer device used for processing, analyzing, and generating learning plans for user information.

[0117] "Generative artificial intelligence" refers to artificial intelligence technology in which an algorithm learns from input data and generates optimized output and feedback.

[0118] "Feedback" refers to the evaluations and advice provided by the generated artificial intelligence in response to the learner's progress.

[0119] "Visual devices" refer to devices used by users to visually experience selected learning content, and include smartphones and head-mounted displays.

[0120] "Content suggestion" refers to the process of recommending learning materials and topics based on learners' progress data.

[0121] A "generative AI model" refers to an AI model designed to optimize learning plans based on information processing and provide appropriate content to users.

[0122] "Learning progress" refers to data that shows the current level of achievement and progress of learners in the educational process.

[0123] This invention is a system for providing users with a personalized learning experience. The system uses an input device to capture user information, including goals, skill levels, and areas of interest. This data is transmitted from the terminal to a server, where a user profile is generated. Based on this user profile, the server generates an optimized learning plan using a generative AI model. This learning plan selects and provides appropriate learning content based on the user's interests and skills.

[0124] The server delivers learning content to the user via visual devices, such as smartphones or head-mounted displays. During this process, it monitors the user's learning progress, generates feedback using generative artificial intelligence, and optimizes the learning plan. The generative AI makes the user's learning experience more effective by suggesting the next content to learn based on their progress.

[0125] For example, if a user wants to learn a new programming language, the system recommends learning materials tailored to the user's current skill level. The generated feedback includes specific advice, such as "the next coding exercises you should learn." This system supports efficient skill acquisition by continuously presenting the optimal learning path for the user.

[0126] An example of a prompt message is, "Based on the user's areas of interest and current learning progress, please suggest what content they should learn next." The AI ​​processes this prompt and generates the most suitable learning content.

[0127] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0128] Step 1:

[0129] The user uses their device to input their learning goals, current skill level, and areas of interest. This data forms the basis for generating the user profile. This input information is sent from the device to the server.

[0130] Step 2:

[0131] The server uses the user information it receives to generate user profiles. A database system is used to organize the input data, creating profiles based on the individual user's characteristics and needs.

[0132] Step 3:

[0133] The server uses a generated AI model to create a customized learning plan based on the user's profile. It analyzes the profile information, performs data analysis to select the most suitable learning content for the user, and outputs the results as a learning plan.

[0134] Step 4:

[0135] Based on the learning plan generated by the server, selected learning content is delivered to the terminal. The learning content is displayed through a visual device and becomes accessible to the user.

[0136] Step 5:

[0137] As users consume the delivered learning content, progress data is generated. The device monitors this data and periodically sends it to the server.

[0138] Step 6:

[0139] The server analyzes the collected progress data and uses generative AI to create feedback for the user. Based on the progress data, the AI ​​model identifies weaknesses and areas that need improvement, and generates specific learning advice accordingly.

[0140] Step 7:

[0141] The server generates feedback and sends it to the device, providing it to the user immediately. This feedback includes suggestions for the next content to learn and how to learn it.

[0142] Step 8:

[0143] The user receives newly provided feedback and continues learning. Content to move on to the next step is then provided again through the device, and the learning cycle continues.

[0144] 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.

[0145] This invention enhances the quality of the learning experience through emotion recognition by combining an emotion engine with a system that provides personalized learning support to users. The system comprises an input device, a processor, a generative AI, a user interface, and an emotion engine.

[0146] System Configuration Overview

[0147] Users enter their basic information using a device and set their learning goals and areas of interest. This information is sent to a server, and a user profile is generated. Based on this profile, a processor uses AI to create a customized learning plan.

[0148] The emotion engine analyzes the user's emotional state through user feedback and interaction. As the user continues to learn, the device periodically sends the user's responses to the emotion engine, collecting emotional data.

[0149] The server uses generative AI to optimize feedback based on emotional information analyzed by the emotion engine. This feedback is adjusted according to the user's emotional state and reflected in the learning plan. For example, if the user is feeling frustrated, they may be offered easier tasks or guided to content that will boost their motivation.

[0150] User experience details

[0151] Users provide regular emotional feedback as part of the learning program. This allows the server to analyze emotional data in real time and provide appropriate support resources as needed. These resources include information and activities related to mental health. If a user is experiencing stress, relaxing content may be recommended.

[0152] Specific example

[0153] For example, while a user is learning a new skill, they record their emotions by answering in-session surveys and feedback forms. The server analyzes this data with an emotion engine to identify areas the user finds difficult. If the emotion engine detects user frustration, the generative AI provides tutorial videos and supplementary materials to reduce the user's stress and fine-tunes the learning plan.

[0154] In this way, the present invention provides a highly personalized learning experience that takes into account the user's emotional state, supporting efficient and sustainable skill improvement.

[0155] The following describes the processing flow.

[0156] Step 1:

[0157] The user logs into the platform using their device and enters information including their learning goals and current skill level. The device then sends this information to the server.

[0158] Step 2:

[0159] The server generates a user profile based on the information received from the user. The processor uses this profile to create a customized learning plan that is optimal for the user, which is generated by the AI.

[0160] Step 3:

[0161] The device displays a generated learning plan to the user. The user begins learning according to this plan and records their progress and feedback sequentially via the device.

[0162] Step 4:

[0163] During the learning process, the user uses a device to answer questions about their emotions, and the device collects the responses. The device then sends this emotion data to a server.

[0164] Step 5:

[0165] The server uses an emotion engine to analyze the user's emotional data. Based on this emotional information, it assesses the user's current psychological state and generates appropriate feedback based on that data.

[0166] Step 6:

[0167] The server utilizes generative AI to update the learning plan based on the user's emotional state. It also adjusts the content and tone of feedback and provides resources to enhance the user's interest and motivation.

[0168] Step 7:

[0169] The device displays feedback and an updated learning plan provided by the server to the user. The user then continues learning accordingly and adjusts their actions based on the feedback.

[0170] Step 8:

[0171] The server continuously analyzes user learning and emotional data, improving the system based on new data to continuously provide the optimal learning experience for the user.

[0172] (Example 2)

[0173] 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".

[0174] Modern learning systems are required to provide a learning experience optimized for each individual user, but they often lack sophisticated personalization that takes emotional states into account, which can lead to decreased learning efficiency. Furthermore, mechanisms to support mental health are insufficient and need improvement.

[0175] 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.

[0176] In this invention, the server includes an input device for inputting user information, an information processing device that generates user characteristic data based on the input information, and generates an individualized learning plan based on the user characteristic data; means for monitoring the user's learning process based on the individualized learning plan and generating evaluation information using the generated data processing technology; and means for detecting the user's emotional state and dynamically adjusting the evaluation information based on that information. This makes it possible to provide an optimal learning plan that takes the user's emotional state into account and to support their mental health.

[0177] "User information" refers to basic data provided by the user through input devices, including learning goals and areas of interest.

[0178] An "input device" is a hardware or software device used to input user information into a system.

[0179] "User characteristic data" refers to a dataset profiled based on user information, which serves as the foundation for generating personalized learning plans.

[0180] An "information processing device" is a part of a computer system that performs data analysis and generates learning plans based on user information.

[0181] A "personalized learning plan" is a customized learning procedure and content that is generated to suit the user's specific needs and goals.

[0182] "Data processing technology" refers to all algorithms and technologies used by information processing devices, specifically for analyzing user learning progress and emotional data.

[0183] "Evaluation information" refers to information generated to assess a user's learning progress and provide feedback for improvement.

[0184] "User emotional state" refers to the user's psychological or emotional state as identified through sensors and feedback.

[0185] This invention is a system for providing personalized learning support to users, and it improves the quality of the learning experience by combining an emotion engine. Specific embodiments are shown below.

[0186] Users provide the system with basic information, learning objectives, and areas of interest entered via an input device using a terminal. This data is sent to a server, which uses an information processing device to generate user characteristic data. The information processing device is part of a system that performs data analysis and generates learning plans based on user information.

[0187] Based on the generated user characteristic data, the server utilizes a generative AI model to create a personalized learning plan. The generative AI model is input with appropriate prompts to formulate the most suitable learning content and procedures for the user. For example, a prompt might be, "Provide resources that would be recommended when the user is facing a specific challenge and feeling frustrated, such as beginner-friendly tutorial videos or content for relaxation."

[0188] During learning, the device continuously collects the user's emotional state through sensors and feedback forms, supplying the data to the emotion engine. The server analyzes this emotional data to generate evaluation information tailored to the user's learning progress. This evaluation information is provided to the user as feedback to efficiently support their learning progress.

[0189] The server dynamically adjusts evaluation information based on collected emotional data, enabling a flexible learning experience tailored to the user's needs. This allows users to continue learning efficiently while receiving support that takes their mental health into consideration.

[0190] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0191] Step 1:

[0192] The user uses a device to input basic information such as learning goals and areas of interest. This information is sent from the device to the server. The server integrates the received data and generates a user profile. In this process, the input data is analyzed and stored in a cache to output a profile that reflects the user's characteristics.

[0193] Step 2:

[0194] The server utilizes a generative AI model to create personalized learning plans based on the user's profile. It uses profile data as input and provides the generative AI with appropriate prompts. As a result, learning content and tasks suitable for the user are automatically generated, and a customized learning plan is output.

[0195] Step 3:

[0196] As the user progresses through the learning process, the device periodically collects data on their emotional state through sensors and questionnaires. This input data, which reflects the user's current emotional state, is sent to the server. The server analyzes this data using an emotion engine to understand the user's psychological aspects.

[0197] Step 4:

[0198] The server generates evaluation information based on analyzed emotional data and optimizes feedback. Using the analysis results of the emotional engine as input, it dynamically adjusts the evaluation information through a generating AI model to generate and output support tailored to the user's state.

[0199] Step 5:

[0200] Based on user needs, the server dynamically adjusts evaluation information and sends it to the terminal. This allows users to receive feedback and adjust the difficulty of tasks and the content of learning resources, enabling more efficient learning. This final output is provided to the user through the terminal as support.

[0201] (Application Example 2)

[0202] 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 device 14 will be referred to as the "terminal."

[0203] In today's educational environment, there is a need for learning support that takes into account the emotional state of individual users. Conventional systems mainly provide uniform learning support that does not take into account the mental health or emotions of users, and are particularly insufficiently optimized in terms of learning motivation and stress reduction. This invention solves this problem by analyzing the user's emotional state in real time and providing learning plans and support based on that analysis.

[0204] 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.

[0205] In this invention, the server includes input means for inputting user information, processing means for generating a user profile based on the input information and generating a customized learning plan based on the user profile, a function for monitoring the user's learning progress based on the generated customized learning plan and generating feedback using artificial intelligence, a function for providing the generated feedback to the user and optimizing the learning plan, and a function for analyzing the user's emotional state, processing the data with artificial intelligence, and providing appropriate educational resources. This enables the optimization of the learning experience according to the user's emotional state and effective individualized learning support.

[0206] "User information" refers to personal data and information about learning goals that the system needs to generate a user's profile.

[0207] "Input means" refers to the devices or interfaces that users use to provide information to a system.

[0208] A "user profile" represents individual characteristic data that reflects a user's learning style and interests.

[0209] A "learning plan" is a program that is optimized for each individual user and outlines a step-by-step method and tasks to help them reach specific learning goals.

[0210] "Processing means" refers to computer systems and algorithms that perform various calculations and analyses based on input data and generate results.

[0211] "Learning progress" is an indicator that shows the extent to which a user has achieved their set learning goals.

[0212] "Generative artificial intelligence" refers to artificial intelligence that uses machine learning techniques to generate meaningful information and feedback from data.

[0213] "Feedback" refers to information generated to support learning by providing evaluations and advice on a user's behavior and performance.

[0214] "Emotional state" refers to the emotional state a user experiences while learning, and it is a crucial factor that influences the system.

[0215] "Educational resources" refer to teaching materials, content, and other learning support resources provided to assist learning.

[0216] "Monitoring" refers to the act of continuously observing user activity and progress, and the data collected is used to optimize the system.

[0217] "Optimization" is the process of adjusting and improving each element to maximize the performance and effectiveness of a system.

[0218] To implement this invention, the user first inputs the necessary user information through a terminal, and the server generates a user profile based on this information. Based on the generated profile, the server generates a customized learning plan. This process uses an input device, a processor, a generative AI, and an independent terminal or server equipped with an emotion engine.

[0219] The server monitors the user's learning progress and collects and analyzes data on user feedback and emotional state. Specific software examples include Google® Speech-to-Text for speech recognition, Emotion API for emotion recognition, and OpenAI®'s GPT-3® for generative AI. This data is processed by an emotion engine to analyze the user's emotional state, and then optimized feedback is generated and provided to the user using generative AI.

[0220] For example, when a user is working on a math problem, the server observes the user's facial expressions and voice through the camera and microphone. If the user shows emotional signs such as a confused expression or a sigh, the generating AI will create a message such as, "Let's try an easier problem next! We can solve it together," and deliver it to the user through the device. Parents will also receive a notification saying, "Your child is finding this a little difficult, but is trying an easier activity."

[0221] Examples of prompts for the generative AI model include: "The child is looking confused while solving a math problem. Please generate words of encouragement for this situation." This allows for the optimization of learning plans and feedback based on the user's emotional state.

[0222] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0223] Step 1:

[0224] Users input learning objectives and basic information using an input device via their terminal.

[0225] This input data is sent to the server, which then generates a user profile based on it. The output at this point is the specific user profile data.

[0226] Step 2:

[0227] Based on the generated user profile, the server uses a processor to create a customized learning plan using a generative AI.

[0228] The input is the user profile, and the output is a personalized learning plan tailored to the user.

[0229] Step 3:

[0230] As the user progresses through the learning process, the device collects facial and voice data in real time via its camera and microphone.

[0231] This information is sent to the emotion engine, which then analyzes the user's emotional state.

[0232] The analyzed output data contains information that indicates the current emotional state.

[0233] Step 4:

[0234] The server uses a generative AI to generate feedback based on the analyzed emotional state data.

[0235] The AI ​​is inputted with emotional state data, and its output consists of user-friendly feedback and content.

[0236] Step 5:

[0237] The generated feedback and educational resources are provided to the user via the device.

[0238] The input here is the feedback generated in step 4, and the output is the specific message or learning content displayed to the user.

[0239] Step 6:

[0240] The server periodically monitors the learning progress and, when new data becomes available, optimizes the learning plan and feedback again based on the profile and emotional state.

[0241] The input includes the latest learning progress data, and the output generates an updated learning plan and feedback.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] [Second Embodiment]

[0246] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0247] 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.

[0248] 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).

[0249] 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.

[0250] 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.

[0251] 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).

[0252] 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.

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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.

[0257] 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".

[0258] This invention describes an embodiment for implementing a system for providing a personalized learning experience to a user. The system comprises an input device, a processor, a generative AI, and a user interface.

[0259] System Configuration Overview

[0260] When the system first imports user information, it uses a terminal to input the user's details. This information includes the user's goals, skill level, and areas of interest. The terminal then sends this information to the server to create the user profile.

[0261] The processor resides on the server, and a generative AI operates based on the user's profile. The generative AI creates a learning plan optimized for the user and incorporates the latest technical information into the plan. This process enables the continuous delivery of a personalized educational experience to the user.

[0262] User experience details

[0263] Users of the system first input their own information to create a user profile tailored to their individual needs. For example, if a user wants to acquire new technical skills, the server takes that learning motivation into consideration and generates a customized curriculum.

[0264] This curriculum includes recommended learning modules, online courses, or specific tasks. Users record their progress via their devices, and this information is fed back to the server. Generative AI analyzes this progress data and provides users with areas for improvement and new goals.

[0265] Specific example

[0266] For example, suppose an IT professional wants to learn a new programming language. This user inputs their goals and current skill level via a terminal. The server receives this data, and the processor generates a learning plan tailored to the user. This plan includes online course schedules and actual code exercises.

[0267] As users record their progress according to their plan, the generating AI provides feedback based on this data. For example, if a user is learning slowly, the AI ​​might suggest intensive exercises on weekends, providing feedback that helps the user further improve their learning effectiveness.

[0268] In this way, the present invention provides a high-quality learning experience tailored to the user and supports efficient skill acquisition.

[0269] The following describes the processing flow.

[0270] Step 1:

[0271] The user creates their account using a device and enters the required information. This information includes their current skill level, learning goals, and areas of interest. The device then sends this entered information to the server.

[0272] Step 2:

[0273] The server generates a user profile based on the information received from the user. This profile contains detailed data about the user's learning needs and goals. The generated profile is stored in a database.

[0274] Step 3:

[0275] The server's processor uses generative AI to create a customized learning plan based on the user's profile. This determines specific learning modules, materials, and schedules that take into account the user's current skill level and goals.

[0276] Step 4:

[0277] Once the learning plan is complete, the server sends the plan to the user's terminal. The terminal displays the plan through a user interface so that the user can review and access it.

[0278] Step 5:

[0279] Users proceed with their learning according to the provided learning plan. Learning progress and activities are recorded via the device and sent to the server.

[0280] Step 6:

[0281] The server periodically analyzes the progress data, and the generative AI generates real-time feedback. This feedback includes an evaluation of the user's learning status and advice on necessary adjustments.

[0282] Step 7:

[0283] The feedback is sent from the server to the user's terminal, and the user checks it. Receiving this feedback, the user makes a decision to continue, modify, or change the plan to maximize their learning effect.

[0284] Step 8:

[0285] Based on the user's feedback, the server improves the overall service and continuously enhances the learning experience. If necessary, it incorporates the latest technical information and updates the learning plan.

[0286] (Example 1)

[0287] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] Currently, it is not only difficult to provide a personalized learning experience that meets the needs of various learners, but also the effective feedback on learning progress and the optimization of the learning plan according to individual learning situations are not fully carried out. In addition, it is not easy to reflect the latest technical information in individual learning processes. As a result, there is a problem that it is difficult for individual learners to acquire skills efficiently according to their specific needs.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0290] This invention includes a server equipped with a device for inputting the user's goals, skill level, and areas of interest; a computing device that generates individual user profiles based on the input information and generates a personalized learning plan based on the user profile; means for recording the user's learning progress based on the generated personalized learning plan and generating feedback using a generating AI; and means for providing the generated feedback to the user and continuously optimizing the learning plan. This enables users to efficiently acquire skills and obtain an effective learning experience utilizing the latest technological information within an individually customized learning process.

[0291] "User information" refers to data that users enter to identify their individual learning needs, such as their goals, skill levels, and areas of interest.

[0292] An "input device" is a hardware or software means used by a user to input information.

[0293] A "user profile" is a dataset generated based on user information, reflecting individual characteristics and needs.

[0294] A "personalized learning plan" is a plan that incorporates learning content and procedures optimized for the individual, based on the user's profile.

[0295] A "processing unit" is a device centered around a processor that processes data and generates learning plans and feedback.

[0296] "Generative AI" refers to a system that uses artificial intelligence technology to analyze user data, generate learning plans, and provide feedback.

[0297] "Progress" refers to the status of activities and achievements that a user has accomplished based on their learning plan.

[0298] "Feedback" refers to information on improvement points and new learning goals provided by the generative AI based on the user's learning progress.

[0299] "Means for optimization" refers to a process or method for continuously improving the user's learning experience and updating a plan suitable for individual learning needs.

[0300] The present invention is a system for providing a personalized learning experience to a user, and the system is composed of the following elements.

[0301] 1. Input of user information

[0302] Terminal: The user uses the terminal to input information. The information includes goals, skill levels, and fields of interest, and is obtained through an input device.

[0303] 2. Generation of user profile

[0304] Server: Receives the information sent from the terminal and creates a user profile by an arithmetic unit. This profile is stored in a database and serves as the basis for subsequent learning plans.

[0305] 3. Generation of personalized learning plan

[0306] Server: Utilizes a generative AI model to create an individually optimized learning plan based on the user profile. This process is executed by an arithmetic unit and reflects the latest technical information.

[0307] 4. Management of learning progress and provision of feedback

[0308] User: Advances the progress according to the learning plan and records the process on the terminal.

[0309] Server: Analyzes progress information sent from the terminal and generates appropriate feedback using a generator AI. This feedback includes suggestions for improvement and new learning objectives.

[0310] Specific example

[0311] For example, if an IT professional wants to learn a new programming language, they input their goals and skills through a terminal. The server receives this information and generates a learning plan tailored to the user. The plan includes a list of online courses and practice problems. The server also provides personalized feedback as the user progresses through their learning. Based on this feedback, the user can further improve their learning efficiency.

[0312] Example of a prompt

[0313] "I want to learn a new programming language. My current skill level is intermediate, and I have experience with Java and Python. Please generate a personalized learning plan."

[0314] Thus, the present invention is a useful system for providing a user-tailored learning process and supporting effective skill acquisition.

[0315] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0316] Step 1:

[0317] Enter user information

[0318] Terminal: Users use a terminal to input information such as goals, skill levels, and areas of interest. This data is initially acquired by the input device. The output data generated will contain detailed information about the user's individual needs.

[0319] Step 2:

[0320] Creating a user profile

[0321] Server: Receives user information sent from the terminal. This input data is processed by the processor to create a user profile. Specifically, it categorizes skill levels and parses goal strings, generating a user profile that is stored in the database as output.

[0322] Step 3:

[0323] Generating a learning plan

[0324] Server: Uses a generative AI model to create personalized learning plans based on user profiles. Specifically, relevant learning materials and curricula are selected considering the user's skill level and areas of interest. The input is the user profile, and the output is the generated learning plan.

[0325] Step 4:

[0326] Recording of learning activities and progress

[0327] User: Based on the obtained learning plan, the user begins learning activities. Tasks completed and progress made during the learning process are recorded via the device. Input is saved as data related to the learning process, and output is saved as progress information.

[0328] Step 5:

[0329] Analysis of progress data and generation of feedback

[0330] Server: The server analyzes the collected progress information using a processor and generates user-appropriate feedback using a generative AI. A specific example is providing additional materials to deepen understanding based on progress. The input is progress data, and the output is feedback including areas for improvement and goal setting.

[0331] (Application Example 1)

[0332] 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."

[0333] In modern education, providing personalized learning experiences for individual learners is crucial, but traditional systems struggle to appropriately deliver optimal content tailored to users' interests and skill levels. Furthermore, real-time feedback based on learning progress and suggestions for the next learning content are lacking. A system is needed to address these challenges.

[0334] 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.

[0335] In this invention, the server includes an input device for inputting user information, a processing unit that generates a user profile based on the input information and generates a customized learning plan based on the user profile, means for monitoring the user's learning progress based on the generated customized learning plan and generating feedback using generative artificial intelligence, means for providing the generated feedback to the user and optimizing the learning plan, means for selecting learning content based on the user's interests and skills and enabling viewing on a visual device, and means for using a generative artificial intelligence model to suggest content according to the learning progress. This makes it possible to provide individual learners with a more effective and efficient learning experience and support the improvement of their skills.

[0336] "User information" refers to data such as goals, skill levels, and areas of interest entered by learners.

[0337] An "input device" refers to a hardware or software configuration for collecting user information, processing it electronically, and transmitting that information to a server.

[0338] A "user profile" refers to digital information generated based on the user's input, representing the individual learner's characteristics and needs.

[0339] A "learning plan" refers to a set of guidelines, customized by a generating AI based on the user's profile, that outlines the goals and means by which the learner should pursue and achieve them.

[0340] A "processing device" refers to a computer device used for processing, analyzing, and generating learning plans for user information.

[0341] "Generative artificial intelligence" refers to artificial intelligence technology in which an algorithm learns from input data and generates optimized output and feedback.

[0342] "Feedback" refers to the evaluations and advice provided by the generated artificial intelligence in response to the learner's progress.

[0343] "Visual devices" refer to devices used by users to visually experience selected learning content, and include smartphones and head-mounted displays.

[0344] "Content suggestion" refers to the process of recommending learning materials and topics based on learners' progress data.

[0345] A "generative AI model" refers to an AI model designed to optimize learning plans based on information processing and provide appropriate content to users.

[0346] "Learning progress" refers to data that shows the current level of achievement and progress of learners in the educational process.

[0347] This invention is a system for providing users with a personalized learning experience. The system uses an input device to capture user information, including goals, skill levels, and areas of interest. This data is transmitted from the terminal to a server, where a user profile is generated. Based on this user profile, the server generates an optimized learning plan using a generative AI model. This learning plan selects and provides appropriate learning content based on the user's interests and skills.

[0348] The server delivers learning content to the user via visual devices, such as smartphones or head-mounted displays. During this process, it monitors the user's learning progress, generates feedback using generative artificial intelligence, and optimizes the learning plan. The generative AI makes the user's learning experience more effective by suggesting the next content to learn based on their progress.

[0349] For example, if a user wants to learn a new programming language, the system recommends learning materials tailored to the user's current skill level. The generated feedback includes specific advice, such as "the next coding exercises you should learn." This system supports efficient skill acquisition by continuously presenting the optimal learning path for the user.

[0350] An example of a prompt message is, "Based on the user's areas of interest and current learning progress, please suggest what content they should learn next." The AI ​​processes this prompt and generates the most suitable learning content.

[0351] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0352] Step 1:

[0353] The user uses their device to input their learning goals, current skill level, and areas of interest. This data forms the basis for generating the user profile. This input information is sent from the device to the server.

[0354] Step 2:

[0355] The server uses the user information it receives to generate user profiles. A database system is used to organize the input data, creating profiles based on the individual user's characteristics and needs.

[0356] Step 3:

[0357] The server uses a generated AI model to create a customized learning plan based on the user's profile. It analyzes the profile information, performs data analysis to select the most suitable learning content for the user, and outputs the results as a learning plan.

[0358] Step 4:

[0359] Based on the learning plan generated by the server, selected learning content is delivered to the terminal. The learning content is displayed through a visual device and becomes accessible to the user.

[0360] Step 5:

[0361] As users consume the delivered learning content, progress data is generated. The device monitors this data and periodically sends it to the server.

[0362] Step 6:

[0363] The server analyzes the collected progress data and uses generative AI to create feedback for the user. Based on the progress data, the AI ​​model identifies weaknesses and areas that need improvement, and generates specific learning advice accordingly.

[0364] Step 7:

[0365] The server generates feedback and sends it to the device, providing it to the user immediately. This feedback includes suggestions for the next content to learn and how to learn it.

[0366] Step 8:

[0367] The user receives newly provided feedback and continues learning. Content to move on to the next step is then provided again through the device, and the learning cycle continues.

[0368] 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.

[0369] This invention enhances the quality of the learning experience through emotion recognition by combining an emotion engine with a system that provides personalized learning support to users. The system comprises an input device, a processor, a generative AI, a user interface, and an emotion engine.

[0370] System Configuration Overview

[0371] Users enter their basic information using a device and set their learning goals and areas of interest. This information is sent to a server, and a user profile is generated. Based on this profile, a processor uses AI to create a customized learning plan.

[0372] The emotion engine analyzes the user's emotional state through user feedback and interaction. As the user continues to learn, the device periodically sends the user's responses to the emotion engine, collecting emotional data.

[0373] The server uses generative AI to optimize feedback based on emotional information analyzed by the emotion engine. This feedback is adjusted according to the user's emotional state and reflected in the learning plan. For example, if the user is feeling frustrated, they may be offered easier tasks or guided to content that will boost their motivation.

[0374] User experience details

[0375] Users provide regular emotional feedback as part of the learning program. This allows the server to analyze emotional data in real time and provide appropriate support resources as needed. These resources include information and activities related to mental health. If a user is experiencing stress, relaxing content may be recommended.

[0376] Specific example

[0377] For example, while a user is learning a new skill, they record their emotions by answering in-session surveys and feedback forms. The server analyzes this data with an emotion engine to identify areas the user finds difficult. If the emotion engine detects user frustration, the generative AI provides tutorial videos and supplementary materials to reduce the user's stress and fine-tunes the learning plan.

[0378] In this way, the present invention provides a highly personalized learning experience that takes into account the user's emotional state, supporting efficient and sustainable skill improvement.

[0379] The following describes the processing flow.

[0380] Step 1:

[0381] The user logs into the platform using their device and enters information including their learning goals and current skill level. The device then sends this information to the server.

[0382] Step 2:

[0383] The server generates a user profile based on the information received from the user. The processor uses this profile to create a customized learning plan that is optimal for the user, which is generated by the AI.

[0384] Step 3:

[0385] The device displays a generated learning plan to the user. The user begins learning according to this plan and records their progress and feedback sequentially via the device.

[0386] Step 4:

[0387] During the learning process, the user uses a device to answer questions about their emotions, and the device collects the responses. The device then sends this emotion data to a server.

[0388] Step 5:

[0389] The server uses an emotion engine to analyze the user's emotional data. Based on this emotional information, it assesses the user's current psychological state and generates appropriate feedback based on that data.

[0390] Step 6:

[0391] The server utilizes generative AI to update the learning plan based on the user's emotional state. It also adjusts the content and tone of feedback and provides resources to enhance the user's interest and motivation.

[0392] Step 7:

[0393] The device displays feedback and an updated learning plan provided by the server to the user. The user then continues learning accordingly and adjusts their actions based on the feedback.

[0394] Step 8:

[0395] The server continuously analyzes user learning and emotional data, improving the system based on new data to continuously provide the optimal learning experience for the user.

[0396] (Example 2)

[0397] 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".

[0398] Modern learning systems are required to provide a learning experience optimized for each individual user, but they often lack sophisticated personalization that takes emotional states into account, which can lead to decreased learning efficiency. Furthermore, mechanisms to support mental health are insufficient and need improvement.

[0399] 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.

[0400] In this invention, the server includes an input device for inputting user information, an information processing device that generates user characteristic data based on the input information, and generates an individualized learning plan based on the user characteristic data; means for monitoring the user's learning process based on the individualized learning plan and generating evaluation information using the generated data processing technology; and means for detecting the user's emotional state and dynamically adjusting the evaluation information based on that information. This makes it possible to provide an optimal learning plan that takes the user's emotional state into account and to support their mental health.

[0401] "User information" refers to basic data provided by the user through input devices, including learning goals and areas of interest.

[0402] An "input device" is a hardware or software device used to input user information into a system.

[0403] "User characteristic data" refers to a dataset profiled based on user information, which serves as the foundation for generating personalized learning plans.

[0404] An "information processing device" is a part of a computer system that performs data analysis and generates learning plans based on user information.

[0405] A "personalized learning plan" is a customized learning procedure and content that is generated to suit the user's specific needs and goals.

[0406] "Data processing technology" refers to all algorithms and technologies used by information processing devices, specifically for analyzing user learning progress and emotional data.

[0407] "Evaluation information" refers to information generated to assess a user's learning progress and provide feedback for improvement.

[0408] "User emotional state" refers to the user's psychological or emotional state as identified through sensors and feedback.

[0409] This invention is a system for providing personalized learning support to users, and it improves the quality of the learning experience by combining an emotion engine. Specific embodiments are shown below.

[0410] Users provide the system with basic information, learning objectives, and areas of interest entered via an input device using a terminal. This data is sent to a server, which uses an information processing device to generate user characteristic data. The information processing device is part of a system that performs data analysis and generates learning plans based on user information.

[0411] Based on the generated user characteristic data, the server utilizes a generative AI model to create a personalized learning plan. The generative AI model is input with appropriate prompts to formulate the most suitable learning content and procedures for the user. For example, a prompt might be, "Provide resources that would be recommended when the user is facing a specific challenge and feeling frustrated, such as beginner-friendly tutorial videos or content for relaxation."

[0412] During learning, the device continuously collects the user's emotional state through sensors and feedback forms, supplying the data to the emotion engine. The server analyzes this emotional data to generate evaluation information tailored to the user's learning progress. This evaluation information is provided to the user as feedback to efficiently support their learning progress.

[0413] The server dynamically adjusts evaluation information based on collected emotional data, enabling a flexible learning experience tailored to the user's needs. This allows users to continue learning efficiently while receiving support that takes their mental health into consideration.

[0414] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0415] Step 1:

[0416] The user uses a device to input basic information such as learning goals and areas of interest. This information is sent from the device to the server. The server integrates the received data and generates a user profile. In this process, the input data is analyzed and stored in a cache to output a profile that reflects the user's characteristics.

[0417] Step 2:

[0418] The server utilizes a generative AI model to create personalized learning plans based on the user's profile. It uses profile data as input and provides the generative AI with appropriate prompts. As a result, learning content and tasks suitable for the user are automatically generated, and a customized learning plan is output.

[0419] Step 3:

[0420] As the user progresses through the learning process, the device periodically collects data on their emotional state through sensors and questionnaires. This input data, which reflects the user's current emotional state, is sent to the server. The server analyzes this data using an emotion engine to understand the user's psychological aspects.

[0421] Step 4:

[0422] The server generates evaluation information based on analyzed emotional data and optimizes feedback. Using the analysis results of the emotional engine as input, it dynamically adjusts the evaluation information through a generating AI model to generate and output support tailored to the user's state.

[0423] Step 5:

[0424] Based on user needs, the server dynamically adjusts evaluation information and sends it to the terminal. This allows users to receive feedback and adjust the difficulty of tasks and the content of learning resources, enabling more efficient learning. This final output is provided to the user through the terminal as support.

[0425] (Application Example 2)

[0426] 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 will be referred to as the "terminal."

[0427] In today's educational environment, there is a need for learning support that takes into account the emotional state of individual users. Conventional systems mainly provide uniform learning support that does not take into account the mental health or emotions of users, and are particularly insufficiently optimized in terms of learning motivation and stress reduction. This invention solves this problem by analyzing the user's emotional state in real time and providing learning plans and support based on that analysis.

[0428] 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.

[0429] In this invention, the server includes input means for inputting user information, processing means for generating a user profile based on the input information and generating a customized learning plan based on the user profile, a function for monitoring the user's learning progress based on the generated customized learning plan and generating feedback using artificial intelligence, a function for providing the generated feedback to the user and optimizing the learning plan, and a function for analyzing the user's emotional state, processing the data with artificial intelligence, and providing appropriate educational resources. This enables the optimization of the learning experience according to the user's emotional state and effective individualized learning support.

[0430] "User information" refers to personal data and information about learning goals that the system needs to generate a user's profile.

[0431] "Input means" refers to the devices or interfaces that users use to provide information to a system.

[0432] A "user profile" represents individual characteristic data that reflects a user's learning style and interests.

[0433] A "learning plan" is a program that is optimized for each individual user and outlines a step-by-step method and tasks to help them reach specific learning goals.

[0434] "Processing means" refers to computer systems and algorithms that perform various calculations and analyses based on input data and generate results.

[0435] "Learning progress" is an indicator that shows the extent to which a user has achieved their set learning goals.

[0436] "Generative artificial intelligence" refers to artificial intelligence that uses machine learning techniques to generate meaningful information and feedback from data.

[0437] "Feedback" refers to information generated to support learning by providing evaluations and advice on a user's behavior and performance.

[0438] "Emotional state" refers to the emotional state a user experiences while learning, and it is a crucial factor that influences the system.

[0439] "Educational resources" refer to teaching materials, content, and other learning support resources provided to assist learning.

[0440] "Monitoring" refers to the act of continuously observing user activity and progress, and the data collected is used to optimize the system.

[0441] "Optimization" is the process of adjusting and improving each element to maximize the performance and effectiveness of a system.

[0442] To implement this invention, the user first inputs the necessary user information through a terminal, and the server generates a user profile based on this information. Based on the generated profile, the server generates a customized learning plan. This process uses an input device, a processor, a generative AI, and an independent terminal or server equipped with an emotion engine.

[0443] The server monitors the user's learning progress and collects and analyzes data on user feedback and emotional state. Specific software examples include Google Speech-to-Text for speech recognition, the Emotion API for emotion recognition, and OpenAI's GPT-3 for generative AI. This data is processed by an emotion engine to analyze the user's emotional state, and then optimized feedback is generated and provided to the user using generative AI.

[0444] For example, when a user is working on a math problem, the server observes the user's facial expressions and voice through the camera and microphone. If the user shows emotional signs such as a confused expression or a sigh, the generating AI will create a message such as, "Let's try an easier problem next! We can solve it together," and deliver it to the user through the device. Parents will also receive a notification saying, "Your child is finding this a little difficult, but is trying an easier activity."

[0445] Examples of prompts for the generative AI model include: "The child is looking confused while solving a math problem. Please generate words of encouragement for this situation." This allows for the optimization of learning plans and feedback based on the user's emotional state.

[0446] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0447] Step 1:

[0448] Users input learning objectives and basic information using an input device via their terminal.

[0449] This input data is sent to the server, which then generates a user profile based on it. The output at this point is the specific user profile data.

[0450] Step 2:

[0451] Based on the generated user profile, the server uses a processor to create a customized learning plan using a generative AI.

[0452] The input is the user profile, and the output is a personalized learning plan tailored to the user.

[0453] Step 3:

[0454] As the user progresses through the learning process, the device collects facial and voice data in real time via its camera and microphone.

[0455] This information is sent to the emotion engine, which then analyzes the user's emotional state.

[0456] The analyzed output data contains information that indicates the current emotional state.

[0457] Step 4:

[0458] The server uses a generative AI to generate feedback based on the analyzed emotional state data.

[0459] The AI ​​is inputted with emotional state data, and its output consists of user-friendly feedback and content.

[0460] Step 5:

[0461] The generated feedback and educational resources are provided to the user via the device.

[0462] The input here is the feedback generated in step 4, and the output is the specific message or learning content displayed to the user.

[0463] Step 6:

[0464] The server periodically monitors the learning progress and, when new data becomes available, optimizes the learning plan and feedback again based on the profile and emotional state.

[0465] The input includes the latest learning progress data, and the output generates an updated learning plan and feedback.

[0466] 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.

[0467] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0468] 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.

[0469] [Third Embodiment]

[0470] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0471] 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.

[0472] 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).

[0473] 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.

[0474] 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.

[0475] 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).

[0476] 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.

[0477] 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.

[0478] 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.

[0479] 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.

[0480] 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.

[0481] 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".

[0482] This invention describes an embodiment for implementing a system for providing a personalized learning experience to a user. The system comprises an input device, a processor, a generative AI, and a user interface.

[0483] System Configuration Overview

[0484] When the system first imports user information, it uses a terminal to input the user's details. This information includes the user's goals, skill level, and areas of interest. The terminal then sends this information to the server to create the user profile.

[0485] The processor resides on the server, and a generative AI operates based on the user's profile. The generative AI creates a learning plan optimized for the user and incorporates the latest technical information into the plan. This process enables the continuous delivery of a personalized educational experience to the user.

[0486] User experience details

[0487] Users of the system first input their own information to create a user profile tailored to their individual needs. For example, if a user wants to acquire new technical skills, the server takes that learning motivation into consideration and generates a customized curriculum.

[0488] This curriculum includes recommended learning modules, online courses, or specific tasks. Users record their progress via their devices, and this information is fed back to the server. Generative AI analyzes this progress data and provides users with areas for improvement and new goals.

[0489] Specific example

[0490] For example, suppose an IT professional wants to learn a new programming language. This user inputs their goals and current skill level via a terminal. The server receives this data, and the processor generates a learning plan tailored to the user. This plan includes online course schedules and actual code exercises.

[0491] As users record their progress according to their plan, the generating AI provides feedback based on this data. For example, if a user is learning slowly, the AI ​​might suggest intensive exercises on weekends, providing feedback that helps the user further improve their learning effectiveness.

[0492] In this way, the present invention provides a high-quality learning experience tailored to the user and supports efficient skill acquisition.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] The user creates their account using a device and enters the required information. This information includes their current skill level, learning goals, and areas of interest. The device then sends this entered information to the server.

[0496] Step 2:

[0497] The server generates a user profile based on the information received from the user. This profile contains detailed data about the user's learning needs and goals. The generated profile is stored in a database.

[0498] Step 3:

[0499] The server's processor uses generative AI to create a customized learning plan based on the user's profile. This determines specific learning modules, materials, and schedules that take into account the user's current skill level and goals.

[0500] Step 4:

[0501] Once the learning plan is complete, the server sends the plan to the user's terminal. The terminal displays the plan through a user interface so that the user can review and access it.

[0502] Step 5:

[0503] Users proceed with their learning according to the provided learning plan. Learning progress and activities are recorded via the device and sent to the server.

[0504] Step 6:

[0505] The server periodically analyzes progress data, and the generating AI produces real-time feedback. This feedback includes an assessment of the user's learning progress and advice on necessary adjustments.

[0506] Step 7:

[0507] Feedback is sent from the server to the user's device, where the user reviews it. Based on this feedback, the user can continue, revise, or change their plan to maximize their learning effectiveness.

[0508] Step 8:

[0509] The server uses user feedback to improve the overall service and continuously enhance the learning experience. It incorporates the latest technical information and updates learning plans as needed.

[0510] (Example 1)

[0511] 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."

[0512] Currently, providing personalized learning experiences that meet the diverse needs of learners is difficult, and effective feedback on learning progress and optimization of learning plans according to individual learning situations are not adequately implemented. Furthermore, it is not easy to incorporate the latest technological information into individual learning processes. As a result, there is a challenge in efficiently acquiring skills that are tailored to the unique needs of each individual learner.

[0513] 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.

[0514] This invention includes a server equipped with a device for inputting the user's goals, skill level, and areas of interest; a computing device that generates individual user profiles based on the input information and generates a personalized learning plan based on the user profile; means for recording the user's learning progress based on the generated personalized learning plan and generating feedback using a generating AI; and means for providing the generated feedback to the user and continuously optimizing the learning plan. This enables users to efficiently acquire skills and obtain an effective learning experience utilizing the latest technological information within an individually customized learning process.

[0515] "User information" refers to data that users enter to identify their individual learning needs, such as their goals, skill levels, and areas of interest.

[0516] An "input device" is a hardware or software means used by a user to input information.

[0517] A "user profile" is a dataset generated based on user information, reflecting individual characteristics and needs.

[0518] A "personalized learning plan" is a plan that incorporates learning content and procedures optimized for the individual, based on the user's profile.

[0519] A "processing unit" is a device centered around a processor that processes data and generates learning plans and feedback.

[0520] "Generative AI" refers to a system that uses artificial intelligence technology to analyze user data, generate learning plans, and provide feedback.

[0521] "Progress" refers to the status of activities and achievements that a user has accomplished based on their learning plan.

[0522] "Feedback" refers to information provided by the AI-generated based on the user's learning progress, including areas for improvement and new learning goals.

[0523] "Optimization means" refers to the process or method for continuously improving the user's learning experience and updating plans to suit individual learning needs.

[0524] This invention is a system for providing users with a personalized learning experience, and the system consists of the following elements.

[0525] 1. Enter user information

[0526] Terminal: Users input information using a terminal. This information includes goals, skill levels, and areas of interest, and is obtained through the input device.

[0527] 2. Generating a user profile

[0528] Server: Receives information sent from the terminal and creates a user profile using the computing unit. This profile is stored in a database and forms the basis for later learning plans.

[0529] 3. Generating a personalized learning plan

[0530] Server: Utilizes generative AI models to create individually optimized learning plans based on user profiles. This process is executed on the computing unit and reflects the latest technological information.

[0531] 4. Managing learning progress and providing feedback

[0532] User: Proceed according to the learning plan and record the process on the device.

[0533] Server: Analyzes progress information sent from the terminal and generates appropriate feedback using a generator AI. This feedback includes suggestions for improvement and new learning objectives.

[0534] Specific example

[0535] For example, if an IT professional wants to learn a new programming language, they input their goals and skills through a terminal. The server receives this information and generates a learning plan tailored to the user. The plan includes a list of online courses and practice problems. The server also provides personalized feedback as the user progresses through their learning. Based on this feedback, the user can further improve their learning efficiency.

[0536] Example of a prompt

[0537] "I want to learn a new programming language. My current skill level is intermediate, and I have experience with Java and Python. Please generate a personalized learning plan."

[0538] Thus, the present invention is a useful system for providing a user-tailored learning process and supporting effective skill acquisition.

[0539] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0540] Step 1:

[0541] Enter user information

[0542] Terminal: Users use a terminal to input information such as goals, skill levels, and areas of interest. This data is initially acquired by the input device. The output data generated will contain detailed information about the user's individual needs.

[0543] Step 2:

[0544] Creating a user profile

[0545] Server: Receives user information sent from the terminal. This input data is processed by the processor to create a user profile. Specifically, it categorizes skill levels and parses goal strings, generating a user profile that is stored in the database as output.

[0546] Step 3:

[0547] Generating a learning plan

[0548] Server: Uses a generative AI model to create personalized learning plans based on user profiles. Specifically, relevant learning materials and curricula are selected considering the user's skill level and areas of interest. The input is the user profile, and the output is the generated learning plan.

[0549] Step 4:

[0550] Recording of learning activities and progress

[0551] User: Based on the obtained learning plan, the user begins learning activities. Tasks completed and progress made during the learning process are recorded via the device. Input is saved as data related to the learning process, and output is saved as progress information.

[0552] Step 5:

[0553] Analysis of progress data and generation of feedback

[0554] Server: The server analyzes the collected progress information using a processor and generates user-appropriate feedback using a generative AI. A specific example is providing additional materials to deepen understanding based on progress. The input is progress data, and the output is feedback including areas for improvement and goal setting.

[0555] (Application Example 1)

[0556] 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."

[0557] In modern education, providing personalized learning experiences for individual learners is crucial, but traditional systems struggle to appropriately deliver optimal content tailored to users' interests and skill levels. Furthermore, real-time feedback based on learning progress and suggestions for the next learning content are lacking. A system is needed to address these challenges.

[0558] 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.

[0559] In this invention, the server includes an input device for inputting user information, a processing unit that generates a user profile based on the input information and generates a customized learning plan based on the user profile, means for monitoring the user's learning progress based on the generated customized learning plan and generating feedback using generative artificial intelligence, means for providing the generated feedback to the user and optimizing the learning plan, means for selecting learning content based on the user's interests and skills and enabling viewing on a visual device, and means for using a generative artificial intelligence model to suggest content according to the learning progress. This makes it possible to provide individual learners with a more effective and efficient learning experience and support the improvement of their skills.

[0560] "User information" refers to data such as goals, skill levels, and areas of interest entered by learners.

[0561] An "input device" refers to a hardware or software configuration for collecting user information, processing it electronically, and transmitting that information to a server.

[0562] A "user profile" refers to digital information generated based on the user's input, representing the individual learner's characteristics and needs.

[0563] A "learning plan" refers to a set of guidelines, customized by a generating AI based on the user's profile, that outlines the goals and means by which the learner should pursue and achieve them.

[0564] A "processing device" refers to a computer device used for processing, analyzing, and generating learning plans for user information.

[0565] "Generative artificial intelligence" refers to artificial intelligence technology in which an algorithm learns from input data and generates optimized output and feedback.

[0566] "Feedback" refers to the evaluations and advice provided by the generated artificial intelligence in response to the learner's progress.

[0567] "Visual devices" refer to devices used by users to visually experience selected learning content, and include smartphones and head-mounted displays.

[0568] "Content suggestion" refers to the process of recommending learning materials and topics based on learners' progress data.

[0569] A "generative AI model" refers to an AI model designed to optimize learning plans based on information processing and provide appropriate content to users.

[0570] "Learning progress" refers to data that shows the current level of achievement and progress of learners in the educational process.

[0571] This invention is a system for providing users with a personalized learning experience. The system uses an input device to capture user information, including goals, skill levels, and areas of interest. This data is transmitted from the terminal to a server, where a user profile is generated. Based on this user profile, the server generates an optimized learning plan using a generative AI model. This learning plan selects and provides appropriate learning content based on the user's interests and skills.

[0572] The server delivers learning content to the user via visual devices, such as smartphones or head-mounted displays. During this process, it monitors the user's learning progress, generates feedback using generative artificial intelligence, and optimizes the learning plan. The generative AI makes the user's learning experience more effective by suggesting the next content to learn based on their progress.

[0573] For example, if a user wants to learn a new programming language, the system recommends learning materials tailored to the user's current skill level. The generated feedback includes specific advice, such as "the next coding exercises you should learn." This system supports efficient skill acquisition by continuously presenting the optimal learning path for the user.

[0574] An example of a prompt message is, "Based on the user's areas of interest and current learning progress, please suggest what content they should learn next." The AI ​​processes this prompt and generates the most suitable learning content.

[0575] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0576] Step 1:

[0577] The user uses their device to input their learning goals, current skill level, and areas of interest. This data forms the basis for generating the user profile. This input information is sent from the device to the server.

[0578] Step 2:

[0579] The server uses the user information it receives to generate user profiles. A database system is used to organize the input data, creating profiles based on the individual user's characteristics and needs.

[0580] Step 3:

[0581] The server uses a generated AI model to create a customized learning plan based on the user's profile. It analyzes the profile information, performs data analysis to select the most suitable learning content for the user, and outputs the results as a learning plan.

[0582] Step 4:

[0583] Based on the learning plan generated by the server, selected learning content is delivered to the terminal. The learning content is displayed through a visual device and becomes accessible to the user.

[0584] Step 5:

[0585] As users consume the delivered learning content, progress data is generated. The device monitors this data and periodically sends it to the server.

[0586] Step 6:

[0587] The server analyzes the collected progress data and uses generative AI to create feedback for the user. Based on the progress data, the AI ​​model identifies weaknesses and areas that need improvement, and generates specific learning advice accordingly.

[0588] Step 7:

[0589] The server generates feedback and sends it to the device, providing it to the user immediately. This feedback includes suggestions for the next content to learn and how to learn it.

[0590] Step 8:

[0591] The user receives newly provided feedback and continues learning. Content to move on to the next step is then provided again through the device, and the learning cycle continues.

[0592] 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.

[0593] This invention enhances the quality of the learning experience through emotion recognition by combining an emotion engine with a system that provides personalized learning support to users. The system comprises an input device, a processor, a generative AI, a user interface, and an emotion engine.

[0594] System Configuration Overview

[0595] Users enter their basic information using a device and set their learning goals and areas of interest. This information is sent to a server, and a user profile is generated. Based on this profile, a processor uses AI to create a customized learning plan.

[0596] The emotion engine analyzes the user's emotional state through user feedback and interaction. As the user continues to learn, the device periodically sends the user's responses to the emotion engine, collecting emotional data.

[0597] The server uses generative AI to optimize feedback based on emotional information analyzed by the emotion engine. This feedback is adjusted according to the user's emotional state and reflected in the learning plan. For example, if the user is feeling frustrated, they may be offered easier tasks or guided to content that will boost their motivation.

[0598] User experience details

[0599] Users provide regular emotional feedback as part of the learning program. This allows the server to analyze emotional data in real time and provide appropriate support resources as needed. These resources include information and activities related to mental health. If a user is experiencing stress, relaxing content may be recommended.

[0600] Specific example

[0601] For example, while a user is learning a new skill, they record their emotions by answering in-session surveys and feedback forms. The server analyzes this data with an emotion engine to identify areas the user finds difficult. If the emotion engine detects user frustration, the generative AI provides tutorial videos and supplementary materials to reduce the user's stress and fine-tunes the learning plan.

[0602] In this way, the present invention provides a highly personalized learning experience that takes into account the user's emotional state, supporting efficient and sustainable skill improvement.

[0603] The following describes the processing flow.

[0604] Step 1:

[0605] The user logs into the platform using their device and enters information including their learning goals and current skill level. The device then sends this information to the server.

[0606] Step 2:

[0607] The server generates a user profile based on the information received from the user. The processor uses this profile to create a customized learning plan that is optimal for the user, which is generated by the AI.

[0608] Step 3:

[0609] The device displays a generated learning plan to the user. The user begins learning according to this plan and records their progress and feedback sequentially via the device.

[0610] Step 4:

[0611] During the learning process, the user uses a device to answer questions about their emotions, and the device collects the responses. The device then sends this emotion data to a server.

[0612] Step 5:

[0613] The server uses an emotion engine to analyze the user's emotional data. Based on this emotional information, it assesses the user's current psychological state and generates appropriate feedback based on that data.

[0614] Step 6:

[0615] The server utilizes generative AI to update the learning plan based on the user's emotional state. It also adjusts the content and tone of feedback and provides resources to enhance the user's interest and motivation.

[0616] Step 7:

[0617] The device displays feedback and an updated learning plan provided by the server to the user. The user then continues learning accordingly and adjusts their actions based on the feedback.

[0618] Step 8:

[0619] The server continuously analyzes user learning and emotional data, improving the system based on new data to continuously provide the optimal learning experience for the user.

[0620] (Example 2)

[0621] 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."

[0622] Modern learning systems are required to provide a learning experience optimized for each individual user, but they often lack sophisticated personalization that takes emotional states into account, which can lead to decreased learning efficiency. Furthermore, mechanisms to support mental health are insufficient and need improvement.

[0623] 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.

[0624] In this invention, the server includes an input device for inputting user information, an information processing device that generates user characteristic data based on the input information, and generates an individualized learning plan based on the user characteristic data; means for monitoring the user's learning process based on the individualized learning plan and generating evaluation information using the generated data processing technology; and means for detecting the user's emotional state and dynamically adjusting the evaluation information based on that information. This makes it possible to provide an optimal learning plan that takes the user's emotional state into account and to support their mental health.

[0625] "User information" refers to basic data provided by the user through input devices, including learning goals and areas of interest.

[0626] An "input device" is a hardware or software device used to input user information into a system.

[0627] "User characteristic data" refers to a dataset profiled based on user information, which serves as the foundation for generating personalized learning plans.

[0628] An "information processing device" is a part of a computer system that performs data analysis and generates learning plans based on user information.

[0629] A "personalized learning plan" is a customized learning procedure and content that is generated to suit the user's specific needs and goals.

[0630] "Data processing technology" refers to all algorithms and technologies used by information processing devices, specifically for analyzing user learning progress and emotional data.

[0631] "Evaluation information" refers to information generated to assess a user's learning progress and provide feedback for improvement.

[0632] "User emotional state" refers to the user's psychological or emotional state as identified through sensors and feedback.

[0633] This invention is a system for providing personalized learning support to users, and it improves the quality of the learning experience by combining an emotion engine. Specific embodiments are shown below.

[0634] Users provide the system with basic information, learning objectives, and areas of interest entered via an input device using a terminal. This data is sent to a server, which uses an information processing device to generate user characteristic data. The information processing device is part of a system that performs data analysis and generates learning plans based on user information.

[0635] Based on the generated user characteristic data, the server utilizes a generative AI model to create a personalized learning plan. The generative AI model is input with appropriate prompts to formulate the most suitable learning content and procedures for the user. For example, a prompt might be, "Provide resources that would be recommended when the user is facing a specific challenge and feeling frustrated, such as beginner-friendly tutorial videos or content for relaxation."

[0636] During learning, the device continuously collects the user's emotional state through sensors and feedback forms, supplying the data to the emotion engine. The server analyzes this emotional data to generate evaluation information tailored to the user's learning progress. This evaluation information is provided to the user as feedback to efficiently support their learning progress.

[0637] The server dynamically adjusts evaluation information based on collected emotional data, enabling a flexible learning experience tailored to the user's needs. This allows users to continue learning efficiently while receiving support that takes their mental health into consideration.

[0638] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0639] Step 1:

[0640] The user uses a device to input basic information such as learning goals and areas of interest. This information is sent from the device to the server. The server integrates the received data and generates a user profile. In this process, the input data is analyzed and stored in a cache to output a profile that reflects the user's characteristics.

[0641] Step 2:

[0642] The server utilizes a generative AI model to create personalized learning plans based on the user's profile. It uses profile data as input and provides the generative AI with appropriate prompts. As a result, learning content and tasks suitable for the user are automatically generated, and a customized learning plan is output.

[0643] Step 3:

[0644] As the user progresses through the learning process, the device periodically collects data on their emotional state through sensors and questionnaires. This input data, which reflects the user's current emotional state, is sent to the server. The server analyzes this data using an emotion engine to understand the user's psychological aspects.

[0645] Step 4:

[0646] The server generates evaluation information based on analyzed emotional data and optimizes feedback. Using the analysis results of the emotional engine as input, it dynamically adjusts the evaluation information through a generating AI model to generate and output support tailored to the user's state.

[0647] Step 5:

[0648] Based on user needs, the server dynamically adjusts evaluation information and sends it to the terminal. This allows users to receive feedback and adjust the difficulty of tasks and the content of learning resources, enabling more efficient learning. This final output is provided to the user through the terminal as support.

[0649] (Application Example 2)

[0650] 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."

[0651] In today's educational environment, there is a need for learning support that takes into account the emotional state of individual users. Conventional systems mainly provide uniform learning support that does not take into account the mental health or emotions of users, and are particularly insufficiently optimized in terms of learning motivation and stress reduction. This invention solves this problem by analyzing the user's emotional state in real time and providing learning plans and support based on that analysis.

[0652] 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.

[0653] In this invention, the server includes input means for inputting user information, processing means for generating a user profile based on the input information and generating a customized learning plan based on the user profile, a function for monitoring the user's learning progress based on the generated customized learning plan and generating feedback using artificial intelligence, a function for providing the generated feedback to the user and optimizing the learning plan, and a function for analyzing the user's emotional state, processing the data with artificial intelligence, and providing appropriate educational resources. This enables the optimization of the learning experience according to the user's emotional state and effective individualized learning support.

[0654] "User information" refers to personal data and information about learning goals that the system needs to generate a user's profile.

[0655] "Input means" refers to the devices or interfaces that users use to provide information to a system.

[0656] A "user profile" represents individual characteristic data that reflects a user's learning style and interests.

[0657] A "learning plan" is a program that is optimized for each individual user and outlines a step-by-step method and tasks to help them reach specific learning goals.

[0658] "Processing means" refers to computer systems and algorithms that perform various calculations and analyses based on input data and generate results.

[0659] "Learning progress" is an indicator that shows the extent to which a user has achieved their set learning goals.

[0660] "Generative artificial intelligence" refers to artificial intelligence that uses machine learning techniques to generate meaningful information and feedback from data.

[0661] "Feedback" refers to information generated to support learning by providing evaluations and advice on a user's behavior and performance.

[0662] "Emotional state" refers to the emotional state a user experiences while learning, and it is a crucial factor that influences the system.

[0663] "Educational resources" refer to teaching materials, content, and other learning support resources provided to assist learning.

[0664] "Monitoring" refers to the act of continuously observing user activity and progress, and the data collected is used to optimize the system.

[0665] "Optimization" is the process of adjusting and improving each element to maximize the performance and effectiveness of a system.

[0666] To implement this invention, the user first inputs the necessary user information through a terminal, and the server generates a user profile based on this information. Based on the generated profile, the server generates a customized learning plan. This process uses an input device, a processor, a generative AI, and an independent terminal or server equipped with an emotion engine.

[0667] The server monitors the user's learning progress and collects and analyzes data on user feedback and emotional state. Specific software examples include Google Speech-to-Text for speech recognition, the Emotion API for emotion recognition, and OpenAI's GPT-3 for generative AI. This data is processed by an emotion engine to analyze the user's emotional state, and then optimized feedback is generated and provided to the user using generative AI.

[0668] For example, when a user is working on a math problem, the server observes the user's facial expressions and voice through the camera and microphone. If the user shows emotional signs such as a confused expression or a sigh, the generating AI will create a message such as, "Let's try an easier problem next! We can solve it together," and deliver it to the user through the device. Parents will also receive a notification saying, "Your child is finding this a little difficult, but is trying an easier activity."

[0669] Examples of prompts for the generative AI model include: "The child is looking confused while solving a math problem. Please generate words of encouragement for this situation." This allows for the optimization of learning plans and feedback based on the user's emotional state.

[0670] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0671] Step 1:

[0672] Users input learning objectives and basic information using an input device via their terminal.

[0673] This input data is sent to the server, which then generates a user profile based on it. The output at this point is the specific user profile data.

[0674] Step 2:

[0675] Based on the generated user profile, the server uses a processor to create a customized learning plan using a generative AI.

[0676] The input is the user profile, and the output is a personalized learning plan tailored to the user.

[0677] Step 3:

[0678] As the user progresses through the learning process, the device collects facial and voice data in real time via its camera and microphone.

[0679] This information is sent to the emotion engine, which then analyzes the user's emotional state.

[0680] The analyzed output data contains information that indicates the current emotional state.

[0681] Step 4:

[0682] The server uses a generative AI to generate feedback based on the analyzed emotional state data.

[0683] The AI ​​is inputted with emotional state data, and its output consists of user-friendly feedback and content.

[0684] Step 5:

[0685] The generated feedback and educational resources are provided to the user via the device.

[0686] The input here is the feedback generated in step 4, and the output is the specific message or learning content displayed to the user.

[0687] Step 6:

[0688] The server periodically monitors the learning progress and, when new data becomes available, optimizes the learning plan and feedback again based on the profile and emotional state.

[0689] The input includes the latest learning progress data, and the output generates an updated learning plan and feedback.

[0690] 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.

[0691] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0692] 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.

[0693] [Fourth Embodiment]

[0694] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0695] 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.

[0696] 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).

[0697] 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.

[0698] 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.

[0699] 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).

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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".

[0707] This invention describes an embodiment for implementing a system for providing a personalized learning experience to a user. The system comprises an input device, a processor, a generative AI, and a user interface.

[0708] System Configuration Overview

[0709] When the system first imports user information, it uses a terminal to input the user's details. This information includes the user's goals, skill level, and areas of interest. The terminal then sends this information to the server to create the user profile.

[0710] The processor resides on the server, and a generative AI operates based on the user's profile. The generative AI creates a learning plan optimized for the user and incorporates the latest technical information into the plan. This process enables the continuous delivery of a personalized educational experience to the user.

[0711] User experience details

[0712] Users of the system first input their own information to create a user profile tailored to their individual needs. For example, if a user wants to acquire new technical skills, the server takes that learning motivation into consideration and generates a customized curriculum.

[0713] This curriculum includes recommended learning modules, online courses, or specific tasks. Users record their progress via their devices, and this information is fed back to the server. Generative AI analyzes this progress data and provides users with areas for improvement and new goals.

[0714] Specific example

[0715] For example, suppose an IT professional wants to learn a new programming language. This user inputs their goals and current skill level via a terminal. The server receives this data, and the processor generates a learning plan tailored to the user. This plan includes online course schedules and actual code exercises.

[0716] As users record their progress according to their plan, the generating AI provides feedback based on this data. For example, if a user is learning slowly, the AI ​​might suggest intensive exercises on weekends, providing feedback that helps the user further improve their learning effectiveness.

[0717] In this way, the present invention provides a high-quality learning experience tailored to the user and supports efficient skill acquisition.

[0718] The following describes the processing flow.

[0719] Step 1:

[0720] The user creates their account using a device and enters the required information. This information includes their current skill level, learning goals, and areas of interest. The device then sends this entered information to the server.

[0721] Step 2:

[0722] The server generates a user profile based on the information received from the user. This profile contains detailed data about the user's learning needs and goals. The generated profile is stored in a database.

[0723] Step 3:

[0724] The server's processor uses generative AI to create a customized learning plan based on the user's profile. This determines specific learning modules, materials, and schedules that take into account the user's current skill level and goals.

[0725] Step 4:

[0726] Once the learning plan is complete, the server sends the plan to the user's terminal. The terminal displays the plan through a user interface so that the user can review and access it.

[0727] Step 5:

[0728] Users proceed with their learning according to the provided learning plan. Learning progress and activities are recorded via the device and sent to the server.

[0729] Step 6:

[0730] The server periodically analyzes progress data, and the generating AI produces real-time feedback. This feedback includes an assessment of the user's learning progress and advice on necessary adjustments.

[0731] Step 7:

[0732] Feedback is sent from the server to the user's device, where the user reviews it. Based on this feedback, the user can continue, revise, or change their plan to maximize their learning effectiveness.

[0733] Step 8:

[0734] The server uses user feedback to improve the overall service and continuously enhance the learning experience. It incorporates the latest technical information and updates learning plans as needed.

[0735] (Example 1)

[0736] 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".

[0737] Currently, providing personalized learning experiences that meet the diverse needs of learners is difficult, and effective feedback on learning progress and optimization of learning plans according to individual learning situations are not adequately implemented. Furthermore, it is not easy to incorporate the latest technological information into individual learning processes. As a result, there is a challenge in efficiently acquiring skills that are tailored to the unique needs of each individual learner.

[0738] 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.

[0739] This invention includes a server equipped with a device for inputting the user's goals, skill level, and areas of interest; a computing device that generates individual user profiles based on the input information and generates a personalized learning plan based on the user profile; means for recording the user's learning progress based on the generated personalized learning plan and generating feedback using a generating AI; and means for providing the generated feedback to the user and continuously optimizing the learning plan. This enables users to efficiently acquire skills and obtain an effective learning experience utilizing the latest technological information within an individually customized learning process.

[0740] "User information" refers to data that users enter to identify their individual learning needs, such as their goals, skill levels, and areas of interest.

[0741] An "input device" is a hardware or software means used by a user to input information.

[0742] A "user profile" is a dataset generated based on user information, reflecting individual characteristics and needs.

[0743] A "personalized learning plan" is a plan that incorporates learning content and procedures optimized for the individual, based on the user's profile.

[0744] A "processing unit" is a device centered around a processor that processes data and generates learning plans and feedback.

[0745] "Generative AI" refers to a system that uses artificial intelligence technology to analyze user data, generate learning plans, and provide feedback.

[0746] "Progress" refers to the status of activities and achievements that a user has accomplished based on their learning plan.

[0747] "Feedback" refers to information provided by the AI-generated based on the user's learning progress, including areas for improvement and new learning goals.

[0748] "Optimization means" refers to the process or method for continuously improving the user's learning experience and updating plans to suit individual learning needs.

[0749] This invention is a system for providing users with a personalized learning experience, and the system consists of the following elements.

[0750] 1. Enter user information

[0751] Terminal: Users input information using a terminal. This information includes goals, skill levels, and areas of interest, and is obtained through the input device.

[0752] 2. Generating a user profile

[0753] Server: Receives information sent from the terminal and creates a user profile using the computing unit. This profile is stored in a database and forms the basis for later learning plans.

[0754] 3. Generating a personalized learning plan

[0755] Server: Utilizes generative AI models to create individually optimized learning plans based on user profiles. This process is executed on the computing unit and reflects the latest technological information.

[0756] 4. Managing learning progress and providing feedback

[0757] User: Proceed according to the learning plan and record the process on the device.

[0758] Server: Analyzes progress information sent from the terminal and generates appropriate feedback using a generator AI. This feedback includes suggestions for improvement and new learning objectives.

[0759] Specific example

[0760] For example, if an IT professional wants to learn a new programming language, they input their goals and skills through a terminal. The server receives this information and generates a learning plan tailored to the user. The plan includes a list of online courses and practice problems. The server also provides personalized feedback as the user progresses through their learning. Based on this feedback, the user can further improve their learning efficiency.

[0761] Example of a prompt

[0762] "I want to learn a new programming language. My current skill level is intermediate, and I have experience with Java and Python. Please generate a personalized learning plan."

[0763] Thus, the present invention is a useful system for providing a user-tailored learning process and supporting effective skill acquisition.

[0764] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0765] Step 1:

[0766] Enter user information

[0767] Terminal: Users use a terminal to input information such as goals, skill levels, and areas of interest. This data is initially acquired by the input device. The output data generated will contain detailed information about the user's individual needs.

[0768] Step 2:

[0769] Creating a user profile

[0770] Server: Receives user information sent from the terminal. This input data is processed by the processor to create a user profile. Specifically, it categorizes skill levels and parses goal strings, generating a user profile that is stored in the database as output.

[0771] Step 3:

[0772] Generating a learning plan

[0773] Server: Uses a generative AI model to create personalized learning plans based on user profiles. Specifically, relevant learning materials and curricula are selected considering the user's skill level and areas of interest. The input is the user profile, and the output is the generated learning plan.

[0774] Step 4:

[0775] Recording of learning activities and progress

[0776] User: Based on the obtained learning plan, the user begins learning activities. Tasks completed and progress made during the learning process are recorded via the device. Input is saved as data related to the learning process, and output is saved as progress information.

[0777] Step 5:

[0778] Analysis of progress data and generation of feedback

[0779] Server: The server analyzes the collected progress information using a processor and generates user-appropriate feedback using a generative AI. A specific example is providing additional materials to deepen understanding based on progress. The input is progress data, and the output is feedback including areas for improvement and goal setting.

[0780] (Application Example 1)

[0781] 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".

[0782] In modern education, providing personalized learning experiences for individual learners is crucial, but traditional systems struggle to appropriately deliver optimal content tailored to users' interests and skill levels. Furthermore, real-time feedback based on learning progress and suggestions for the next learning content are lacking. A system is needed to address these challenges.

[0783] 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.

[0784] In this invention, the server includes an input device for inputting user information, a processing unit that generates a user profile based on the input information and generates a customized learning plan based on the user profile, means for monitoring the user's learning progress based on the generated customized learning plan and generating feedback using generative artificial intelligence, means for providing the generated feedback to the user and optimizing the learning plan, means for selecting learning content based on the user's interests and skills and enabling viewing on a visual device, and means for using a generative artificial intelligence model to suggest content according to the learning progress. This makes it possible to provide individual learners with a more effective and efficient learning experience and support the improvement of their skills.

[0785] "User information" refers to data such as goals, skill levels, and areas of interest entered by learners.

[0786] An "input device" refers to a hardware or software configuration for collecting user information, processing it electronically, and transmitting that information to a server.

[0787] A "user profile" refers to digital information generated based on the user's input, representing the individual learner's characteristics and needs.

[0788] A "learning plan" refers to a set of guidelines, customized by a generating AI based on the user's profile, that outlines the goals and means by which the learner should pursue and achieve them.

[0789] A "processing device" refers to a computer device used for processing, analyzing, and generating learning plans for user information.

[0790] "Generative artificial intelligence" refers to artificial intelligence technology in which an algorithm learns from input data and generates optimized output and feedback.

[0791] "Feedback" refers to the evaluations and advice provided by the generated artificial intelligence in response to the learner's progress.

[0792] "Visual devices" refer to devices used by users to visually experience selected learning content, and include smartphones and head-mounted displays.

[0793] "Content suggestion" refers to the process of recommending learning materials and topics based on learners' progress data.

[0794] A "generative AI model" refers to an AI model designed to optimize learning plans based on information processing and provide appropriate content to users.

[0795] "Learning progress" refers to data that shows the current level of achievement and progress of learners in the educational process.

[0796] This invention is a system for providing users with a personalized learning experience. The system uses an input device to capture user information, including goals, skill levels, and areas of interest. This data is transmitted from the terminal to a server, where a user profile is generated. Based on this user profile, the server generates an optimized learning plan using a generative AI model. This learning plan selects and provides appropriate learning content based on the user's interests and skills.

[0797] The server delivers learning content to the user via visual devices, such as smartphones or head-mounted displays. During this process, it monitors the user's learning progress, generates feedback using generative artificial intelligence, and optimizes the learning plan. The generative AI makes the user's learning experience more effective by suggesting the next content to learn based on their progress.

[0798] For example, if a user wants to learn a new programming language, the system recommends learning materials tailored to the user's current skill level. The generated feedback includes specific advice, such as "the next coding exercises you should learn." This system supports efficient skill acquisition by continuously presenting the optimal learning path for the user.

[0799] An example of a prompt message is, "Based on the user's areas of interest and current learning progress, please suggest what content they should learn next." The AI ​​processes this prompt and generates the most suitable learning content.

[0800] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0801] Step 1:

[0802] The user uses their device to input their learning goals, current skill level, and areas of interest. This data forms the basis for generating the user profile. This input information is sent from the device to the server.

[0803] Step 2:

[0804] The server uses the user information it receives to generate user profiles. A database system is used to organize the input data, creating profiles based on the individual user's characteristics and needs.

[0805] Step 3:

[0806] The server uses a generated AI model to create a customized learning plan based on the user's profile. It analyzes the profile information, performs data analysis to select the most suitable learning content for the user, and outputs the results as a learning plan.

[0807] Step 4:

[0808] Based on the learning plan generated by the server, selected learning content is delivered to the terminal. The learning content is displayed through a visual device and becomes accessible to the user.

[0809] Step 5:

[0810] As users consume the delivered learning content, progress data is generated. The device monitors this data and periodically sends it to the server.

[0811] Step 6:

[0812] The server analyzes the collected progress data and uses generative AI to create feedback for the user. Based on the progress data, the AI ​​model identifies weaknesses and areas that need improvement, and generates specific learning advice accordingly.

[0813] Step 7:

[0814] The server generates feedback and sends it to the device, providing it to the user immediately. This feedback includes suggestions for the next content to learn and how to learn it.

[0815] Step 8:

[0816] The user receives newly provided feedback and continues learning. Content to move on to the next step is then provided again through the device, and the learning cycle continues.

[0817] 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.

[0818] This invention enhances the quality of the learning experience through emotion recognition by combining an emotion engine with a system that provides personalized learning support to users. The system comprises an input device, a processor, a generative AI, a user interface, and an emotion engine.

[0819] System Configuration Overview

[0820] Users enter their basic information using a device and set their learning goals and areas of interest. This information is sent to a server, and a user profile is generated. Based on this profile, a processor uses AI to create a customized learning plan.

[0821] The emotion engine analyzes the user's emotional state through user feedback and interaction. As the user continues to learn, the device periodically sends the user's responses to the emotion engine, collecting emotional data.

[0822] The server uses generative AI to optimize feedback based on emotional information analyzed by the emotion engine. This feedback is adjusted according to the user's emotional state and reflected in the learning plan. For example, if the user is feeling frustrated, they may be offered easier tasks or guided to content that will boost their motivation.

[0823] User experience details

[0824] Users provide regular emotional feedback as part of the learning program. This allows the server to analyze emotional data in real time and provide appropriate support resources as needed. These resources include information and activities related to mental health. If a user is experiencing stress, relaxing content may be recommended.

[0825] Specific example

[0826] For example, while a user is learning a new skill, they record their emotions by answering in-session surveys and feedback forms. The server analyzes this data with an emotion engine to identify areas the user finds difficult. If the emotion engine detects user frustration, the generative AI provides tutorial videos and supplementary materials to reduce the user's stress and fine-tunes the learning plan.

[0827] In this way, the present invention provides a highly personalized learning experience that takes into account the user's emotional state, supporting efficient and sustainable skill improvement.

[0828] The following describes the processing flow.

[0829] Step 1:

[0830] The user logs into the platform using their device and enters information including their learning goals and current skill level. The device then sends this information to the server.

[0831] Step 2:

[0832] The server generates a user profile based on the information received from the user. The processor uses this profile to create a customized learning plan that is optimal for the user, which is generated by the AI.

[0833] Step 3:

[0834] The device displays a generated learning plan to the user. The user begins learning according to this plan and records their progress and feedback sequentially via the device.

[0835] Step 4:

[0836] During the learning process, the user uses a device to answer questions about their emotions, and the device collects the responses. The device then sends this emotion data to a server.

[0837] Step 5:

[0838] The server uses an emotion engine to analyze the user's emotional data. Based on this emotional information, it assesses the user's current psychological state and generates appropriate feedback based on that data.

[0839] Step 6:

[0840] The server utilizes generative AI to update the learning plan based on the user's emotional state. It also adjusts the content and tone of feedback and provides resources to enhance the user's interest and motivation.

[0841] Step 7:

[0842] The device displays feedback and an updated learning plan provided by the server to the user. The user then continues learning accordingly and adjusts their actions based on the feedback.

[0843] Step 8:

[0844] The server continuously analyzes user learning and emotional data, improving the system based on new data to continuously provide the optimal learning experience for the user.

[0845] (Example 2)

[0846] 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".

[0847] Modern learning systems are required to provide a learning experience optimized for each individual user, but they often lack sophisticated personalization that takes emotional states into account, which can lead to decreased learning efficiency. Furthermore, mechanisms to support mental health are insufficient and need improvement.

[0848] 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.

[0849] In this invention, the server includes an input device for inputting user information, an information processing device that generates user characteristic data based on the input information, and generates an individualized learning plan based on the user characteristic data; means for monitoring the user's learning process based on the individualized learning plan and generating evaluation information using the generated data processing technology; and means for detecting the user's emotional state and dynamically adjusting the evaluation information based on that information. This makes it possible to provide an optimal learning plan that takes the user's emotional state into account and to support their mental health.

[0850] "User information" refers to basic data provided by the user through input devices, including learning goals and areas of interest.

[0851] An "input device" is a hardware or software device used to input user information into a system.

[0852] "User characteristic data" refers to a dataset profiled based on user information, which serves as the foundation for generating personalized learning plans.

[0853] An "information processing device" is a part of a computer system that performs data analysis and generates learning plans based on user information.

[0854] A "personalized learning plan" is a customized learning procedure and content that is generated to suit the user's specific needs and goals.

[0855] "Data processing technology" refers to all algorithms and technologies used by information processing devices, specifically for analyzing user learning progress and emotional data.

[0856] "Evaluation information" refers to information generated to assess a user's learning progress and provide feedback for improvement.

[0857] "User emotional state" refers to the user's psychological or emotional state as identified through sensors and feedback.

[0858] This invention is a system for providing personalized learning support to users, and it improves the quality of the learning experience by combining an emotion engine. Specific embodiments are shown below.

[0859] Users provide the system with basic information, learning objectives, and areas of interest entered via an input device using a terminal. This data is sent to a server, which uses an information processing device to generate user characteristic data. The information processing device is part of a system that performs data analysis and generates learning plans based on user information.

[0860] Based on the generated user characteristic data, the server utilizes a generative AI model to create a personalized learning plan. The generative AI model is input with appropriate prompts to formulate the most suitable learning content and procedures for the user. For example, a prompt might be, "Provide resources that would be recommended when the user is facing a specific challenge and feeling frustrated, such as beginner-friendly tutorial videos or content for relaxation."

[0861] During learning, the device continuously collects the user's emotional state through sensors and feedback forms, supplying the data to the emotion engine. The server analyzes this emotional data to generate evaluation information tailored to the user's learning progress. This evaluation information is provided to the user as feedback to efficiently support their learning progress.

[0862] The server dynamically adjusts evaluation information based on collected emotional data, enabling a flexible learning experience tailored to the user's needs. This allows users to continue learning efficiently while receiving support that takes their mental health into consideration.

[0863] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0864] Step 1:

[0865] The user uses a device to input basic information such as learning goals and areas of interest. This information is sent from the device to the server. The server integrates the received data and generates a user profile. In this process, the input data is analyzed and stored in a cache to output a profile that reflects the user's characteristics.

[0866] Step 2:

[0867] The server utilizes a generative AI model to create personalized learning plans based on the user's profile. It uses profile data as input and provides the generative AI with appropriate prompts. As a result, learning content and tasks suitable for the user are automatically generated, and a customized learning plan is output.

[0868] Step 3:

[0869] As the user progresses through the learning process, the device periodically collects data on their emotional state through sensors and questionnaires. This input data, which reflects the user's current emotional state, is sent to the server. The server analyzes this data using an emotion engine to understand the user's psychological aspects.

[0870] Step 4:

[0871] The server generates evaluation information based on analyzed emotional data and optimizes feedback. Using the analysis results of the emotional engine as input, it dynamically adjusts the evaluation information through a generating AI model to generate and output support tailored to the user's state.

[0872] Step 5:

[0873] Based on user needs, the server dynamically adjusts evaluation information and sends it to the terminal. This allows users to receive feedback and adjust the difficulty of tasks and the content of learning resources, enabling more efficient learning. This final output is provided to the user through the terminal as support.

[0874] (Application Example 2)

[0875] 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".

[0876] In today's educational environment, there is a need for learning support that takes into account the emotional state of individual users. Conventional systems mainly provide uniform learning support that does not take into account the mental health or emotions of users, and are particularly insufficiently optimized in terms of learning motivation and stress reduction. This invention solves this problem by analyzing the user's emotional state in real time and providing learning plans and support based on that analysis.

[0877] 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.

[0878] In this invention, the server includes input means for inputting user information, processing means for generating a user profile based on the input information and generating a customized learning plan based on the user profile, a function for monitoring the user's learning progress based on the generated customized learning plan and generating feedback using artificial intelligence, a function for providing the generated feedback to the user and optimizing the learning plan, and a function for analyzing the user's emotional state, processing the data with artificial intelligence, and providing appropriate educational resources. This enables the optimization of the learning experience according to the user's emotional state and effective individualized learning support.

[0879] "User information" refers to personal data and information about learning goals that the system needs to generate a user's profile.

[0880] "Input means" refers to the devices or interfaces that users use to provide information to a system.

[0881] A "user profile" represents individual characteristic data that reflects a user's learning style and interests.

[0882] A "learning plan" is a program that is optimized for each individual user and outlines a step-by-step method and tasks to help them reach specific learning goals.

[0883] "Processing means" refers to computer systems and algorithms that perform various calculations and analyses based on input data and generate results.

[0884] "Learning progress" is an indicator that shows the extent to which a user has achieved their set learning goals.

[0885] "Generative artificial intelligence" refers to artificial intelligence that uses machine learning techniques to generate meaningful information and feedback from data.

[0886] "Feedback" refers to information generated to support learning by providing evaluations and advice on a user's behavior and performance.

[0887] "Emotional state" refers to the emotional state a user experiences while learning, and it is a crucial factor that influences the system.

[0888] "Educational resources" refer to teaching materials, content, and other learning support resources provided to assist learning.

[0889] "Monitoring" refers to the act of continuously observing user activity and progress, and the data collected is used to optimize the system.

[0890] "Optimization" is the process of adjusting and improving each element to maximize the performance and effectiveness of a system.

[0891] To implement this invention, the user first inputs the necessary user information through a terminal, and the server generates a user profile based on this information. Based on the generated profile, the server generates a customized learning plan. This process uses an input device, a processor, a generative AI, and an independent terminal or server equipped with an emotion engine.

[0892] The server monitors the user's learning progress and collects and analyzes data on user feedback and emotional state. Specific software examples include Google Speech-to-Text for speech recognition, the Emotion API for emotion recognition, and OpenAI's GPT-3 for generative AI. This data is processed by an emotion engine to analyze the user's emotional state, and then optimized feedback is generated and provided to the user using generative AI.

[0893] For example, when a user is working on a math problem, the server observes the user's facial expressions and voice through the camera and microphone. If the user shows emotional signs such as a confused expression or a sigh, the generating AI will create a message such as, "Let's try an easier problem next! We can solve it together," and deliver it to the user through the device. Parents will also receive a notification saying, "Your child is finding this a little difficult, but is trying an easier activity."

[0894] Examples of prompts for the generative AI model include: "The child is looking confused while solving a math problem. Please generate words of encouragement for this situation." This allows for the optimization of learning plans and feedback based on the user's emotional state.

[0895] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0896] Step 1:

[0897] Users input learning objectives and basic information using an input device via their terminal.

[0898] This input data is sent to the server, which then generates a user profile based on it. The output at this point is the specific user profile data.

[0899] Step 2:

[0900] Based on the generated user profile, the server uses a processor to create a customized learning plan using a generative AI.

[0901] The input is the user profile, and the output is a personalized learning plan tailored to the user.

[0902] Step 3:

[0903] As the user progresses through the learning process, the device collects facial and voice data in real time via its camera and microphone.

[0904] This information is sent to the emotion engine, which then analyzes the user's emotional state.

[0905] The analyzed output data contains information that indicates the current emotional state.

[0906] Step 4:

[0907] The server uses a generative AI to generate feedback based on the analyzed emotional state data.

[0908] The AI ​​is inputted with emotional state data, and its output consists of user-friendly feedback and content.

[0909] Step 5:

[0910] The generated feedback and educational resources are provided to the user via the device.

[0911] The input here is the feedback generated in step 4, and the output is the specific message or learning content displayed to the user.

[0912] Step 6:

[0913] The server periodically monitors the learning progress and, when new data becomes available, optimizes the learning plan and feedback again based on the profile and emotional state.

[0914] The input includes the latest learning progress data, and the output generates an updated learning plan and feedback.

[0915] 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.

[0916] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0917] 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.

[0918] 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.

[0919] 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. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, 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.

[0920] 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.

[0921] 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.

[0922] 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.

[0923] 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."

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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.

[0932] 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.

[0933] 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.

[0934] 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.

[0935] 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.

[0936] The following is further disclosed regarding the embodiments described above.

[0937] (Claim 1)

[0938] A processor comprising an input device for entering user information, which generates a user profile based on the entered information and generates a customized learning plan based on the user profile,

[0939] A means for monitoring the user's learning progress based on a generated customized learning plan and generating feedback using generative artificial intelligence,

[0940] A means of providing generated feedback to the user and optimizing the learning plan,

[0941] A system that includes this.

[0942] (Claim 2)

[0943] The system according to claim 1, characterized by comprising a processor that evaluates the user's mental health based on user information and provides support resources as needed.

[0944] (Claim 3)

[0945] The system according to claim 1, characterized in that it includes means for incorporating the latest technological information and curating learning content based on the user's profile when generating a learning plan.

[0946] "Example 1"

[0947] (Claim 1)

[0948] A device for inputting the user's goals, skill level, and areas of interest; a computing device that generates individual user profiles based on the input information; and a computing device that generates personalized learning plans based on said user profiles.

[0949] A means of recording the user's learning progress based on a generated personalized learning plan and generating feedback using a generator AI,

[0950] A means of providing users with generated feedback and continuously optimizing their learning plan,

[0951] A system that includes this.

[0952] (Claim 2)

[0953] The system according to claim 1, characterized by having a processor that analyzes user progress data, evaluates learning speed and effectiveness using generating AI, and improves the learning method as needed.

[0954] (Claim 3)

[0955] The system according to claim 1, characterized in that it includes means for dynamically reflecting the latest technological information when generating a learning plan and for selecting learning materials based on the user's profile.

[0956] "Application Example 1"

[0957] (Claim 1)

[0958] A processing device equipped with an input device for inputting user information, which generates a user profile based on the input information and generates a customized learning plan based on the user profile,

[0959] A means for monitoring the user's learning progress based on a generated customized learning plan and generating feedback using generative artificial intelligence,

[0960] A means of providing generated feedback to the user and optimizing the learning plan,

[0961] A means of selecting learning content based on the user's interests and skills, and enabling viewing on a visual device,

[0962] A method using a generative artificial intelligence model to suggest content according to learning progress,

[0963] A system that includes this.

[0964] (Claim 2)

[0965] The system according to claim 1, characterized by comprising a processing device that evaluates the user's psychological state based on user information and provides support resources as needed.

[0966] (Claim 3)

[0967] The system according to claim 1, characterized in that it incorporates the latest technical information when generating a learning plan and includes means for selecting learning materials based on the user's profile.

[0968] "Example 2 of combining an emotion engine"

[0969] (Claim 1)

[0970] An information processing device equipped with an input device for inputting user information, which generates user characteristic data based on the input information and generates an individualized learning plan based on the user characteristic data,

[0971] A means for monitoring the user's learning process based on an individualized learning plan and generating evaluation information using the generated data processing technology,

[0972] A means for detecting the user's emotional state and dynamically adjusting evaluation information based on that information,

[0973] A means of providing users with generated evaluation information and refining their learning plans,

[0974] A system that includes this.

[0975] (Claim 2)

[0976] The system according to claim 1, characterized in that it includes an information processing device that analyzes the user's mental health status based on user information and provides support resources as needed.

[0977] (Claim 3)

[0978] The system according to claim 1, characterized in that it includes means for adopting the latest knowledge information and selecting learning content based on user characteristic data when generating a learning plan.

[0979] "Application example 2 when combining with an emotional engine"

[0980] (Claim 1)

[0981] A processing means comprising an input means for inputting user information, a user profile generated based on the input information, and a customized learning plan generated based on the user profile,

[0982] It has the functionality to monitor the user's learning progress based on the generated customized learning plan and to generate feedback using generative artificial intelligence,

[0983] It provides users with generated feedback and features that optimize their learning plans.

[0984] It has a function that analyzes the user's emotional state, processes that data using artificial intelligence, and provides appropriate educational resources.

[0985] A system that includes this.

[0986] (Claim 2)

[0987] The system according to claim 1, comprising processing means for evaluating the user's mental health based on user information and providing support resources as needed, and characterized in that learning resources are adjusted based on emotional analysis data.

[0988] (Claim 3)

[0989] The system according to claim 1, characterized in that, when generating a learning plan, it incorporates new technological information, has a function to select educational content based on the user profile, and optimizes the content according to the user's emotional state. [Explanation of symbols]

[0990] 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 processor comprising an input device for entering user information, which generates a user profile based on the entered information and generates a customized learning plan based on the user profile, A means for monitoring the user's learning progress based on a generated customized learning plan and generating feedback using generative artificial intelligence, A means of providing generated feedback to the user and optimizing the learning plan, A system that includes this.

2. The system according to claim 1, characterized in that it includes a processor that evaluates the user's mental health based on user information and provides support resources as needed.

3. The system according to claim 1, characterized in that it includes means for incorporating the latest technological information and curating learning content based on the user's profile when generating a learning plan.

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